Month: January 2023


FBK-CHuB is seeking a Researcher in the field of the classification, analysis and modelling of online disinformation spreading behaviour.
In particular, the candidate will be involved in a large European research project focused on the development of a platform tackling misinformation and disinformation across the EU by empowering scientific researchers and media practitioners with advanced AI-based technologies that: 1) allow multichannel (distinct online social media and news feeds), multilingual and multimodal (textual, visual and audio content) monitoring, detection and recording of misinformation and disinformation on online social media and traditional media; 2) estimate the risk of unreliable information consumption; 3) create a trustworthy online environment involving researchers, media practitioners and policy makers to facilitate the creation and distribution of reliable information and counter-narratives, while labelling and countering mis/disinformation.

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The circular economy

A sustainable future requires preservation of the world’s finite resources, which often means the waste from one process loops back and becomes the input for another. Advanced technologies and techniques are helping an array of industries to make reuse and recycling more central to their operations. 

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Scaling up our understanding of tipping points

Sonia Kéfi, Camille Saade, Eric L. Berlow, Juliano S. Cabral and Emanuel A. Fronhofer

Philosophical Transactions of the Royal Society B-Biological Sciences; Vol.: 377; Issue: 1857; Article No.: 20210386

Anthropogenic activities are increasingly affecting ecosystems across the globe. Meanwhile, empirical and theoretical evidence suggest that natural systems can exhibit abrupt collapses in response to incremental increases in the stressors, sometimes with dramatic ecological and economic consequences. These catastrophic shifts are faster and larger than expected from the changes in the stressors and happen once a tipping point is crossed. The primary mechanisms that drive ecosystem responses to perturbations lie in their architecture of relationships, i.e. how species interact with each other and with the physical environment and the spatial structure of the environment. Nonetheless, existing theoretical work on catastrophic shifts has so far largely focused on relatively simple systems that have either few species and/or no spatial structure. This work has laid a critical foundation for understanding how abrupt responses to incremental stressors are possible, but it remains difficult to predict (let alone manage) where or when they are most likely to occur in more complex real-world settings. Here, we discuss how scaling up our investigations of catastrophic shifts from simple to more complex—species rich and spatially structured—systems could contribute to expanding our understanding of how nature works and improve our ability to anticipate the effects of global change on ecological systems.

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The Clinical Trials Puzzle: How Network Effects Limit Drug Discovery

Kishore Vasan, Deisy Gysi, Albert-Laszlo Barabasi
The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model of drug discovery. We demonstrate that the model is able to accurately recreate the exploration patterns observed in clinical trials. Most importantly, we show that a network-based search strategy can widen the scope of drug discovery by guiding exploration to novel proteins that are part of under explored regions in the human interactome.

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Exact and rapid linear clustering of networks with dynamic programming

Alice Patania, Antoine Allard, Jean-Gabriel Young
We study the problem of clustering networks whose nodes have imputed or physical positions in a single dimension, such as prestige hierarchies or the similarity dimension of hyperbolic embeddings. Existing algorithms, such as the critical gap method and other greedy strategies, only offer approximate solutions. Here, we introduce a dynamic programming approach that returns provably optimal solutions in polynomial time — O(n^2) steps — for a broad class of clustering objectives. We demonstrate the algorithm through applications to synthetic and empirical networks, and show that it outperforms existing heuristics by a significant margin, with a similar execution time.

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