GILUPI (Germany)

<p>Currently, the global food system is the single largest threat to people and planet. Food is the leading cause behind transgressing five of the nine planetary boundaries. It is a major source of carbon emissions, as well as the single largest contributor to global deforestation, overuse of fresh water and eutrophication of our aquatic ecosystems. And while agriculture has been a major engine of poverty reduction, agricultural activities are unable to deliver a decent livelihood for an estimated 80 percent of those living in extreme poverty. The projected increase in frequency and severity of climate extreme events is posing additional threats to the global food system.</p><p>A transformation towards a more inclusive, sustainable and health-promoting food system is urgently needed. This presentation will introduce the newly established Food Systems Economics Commission (FSEC) that provides detailed and robust evidence assessing the implications of the policy and investment decisions needed to foster a food system transformation. It integrates global modelling tools such as integrated assessment modelling and innovative applications of agent-based modelling with political economy considerations.  It investigates the hidden costs of our current food system, explores transitions pathways towards a new food and land use economy and suggests key policy instruments to foster the transformation towards a sustainable, inclusive, healthy and resilient food system.</p>

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of "extremeness". Intuitively, we think of this as calculating an anomalous p-value. This makes COPOD both parameter-free, highly interpretable, and computationally efficient. In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.

The global energy system is undergoing a major transition, and in energy planning and decision-making across governments, industry and academia, models play a crucial role. Because of their policy relevance and contested nature, the transparency and open availability of energy models and data are of particular importance. Here we provide a practical how-to guide based on the collective experience of members of the Open Energy Modelling Initiative (Openmod). We discuss key steps to consider when opening code and data, including determining intellectual property ownership, choosing a licence and appropriate modelling languages, distributing code and data, and providing support and building communities. After illustrating these decisions with examples and lessons learned from the community, we conclude that even though individual researchers' choices are important, institutional changes are still also necessary for more openness and transparency in energy research.

The detection of circulating tumor cells (CTCs) in the blood of cancer patients is a challenging task. CTCs are, especially at the early stages of cancer development, extremely rare cells hidden in a vast background of regular blood cells. We describe a new strategy for the isolation of CTCs from whole blood. The key component is a medical wire coated with a multilayer assembly that allows highly specific capture of EpCAM (epithelial cell adhesion molecule) positive CTCs from blood. The assembly is generated in a layer-by-layer fashion through photochemically induced C,H insertion reactions and consists of a protective layer, which shields the contacting solution from the metal, a protein resistant layer, which prevents nonspecific interactions with proteins and a layer containing the EpCAM antibodies. In vitro experiments show that these surfaces can capture tumor cells from whole blood with enrichment factors (specifically vs nonspecifically bound cells) of up to about 3000 compared to the number of leucocytes in the blood. The purity of the isolated cells is greater than 90%. After "fishing" them from the blood, the cells, still bound to the wire, can be genetically analyzed. This demonstrates that this strategy might prove useful for next generation sequencing.

Die Anwendung zirkulierender Tumorzellen in der Diagnostik und Therapie des Lungen-karzinoms ist derzeit nicht etabliert. Bisher werden CTC in vitro aus Blutproben bestimmt. Anhand dieser Methoden (CellSearch) konnten nur bei ca. 30% der Patienten mit primärem Lungenkarzinom CTC detektiert werden; bei 12% der Patienten mit nichtmalignen Lungenerkrankungen wurden CTC gefunden (Tanaka 2009). Es besteht eine ungenügende Sensitivität. Nur in 19% der Patienten mit klinischen Stadium I (Frühkarzinom) wurden CTC gefunden (Tanaka 2009). Somit ist der diagnostische Wert der bisherigen Systeme zur Bestimmung von CTC fraglich. Ziel dieser Studie ist es, CTC bei Patienten mit einem primären Lungenkarzinom über einen FSMW-Kollektor (functionalized and structured medical wire) in vivo aus dem Patientenblut zu isolieren und damit deren Wertigkeit als klinischer Marker therapiebegleitend und für die Diagnostik zu verbessern.