Goto

Collaborating Authors

Zürich


An AI-Controlled Drone Racer Has Beaten Human Pilots For The First Time

#artificialintelligence

Drone racing is an increasingly popular sport with big money prizes for skilled professionals. New control algorithms developed at the University of Zurich (UZH) have beaten experienced human pilots for the first time – but they still have significant limitations. In the past, attempts to develop automated algorithms to beat humans have run into problems with accurately simulating the limitations of the quadcopter and the flight path it takes. Traditional flight paths around a complex drone racing course are calculated using polynomial methods which produce a series of smooth curves, and these are not necessarily as fast as the sharper and more jagged paths flown by human pilots. A team from the Robotics and Perception Group at UZH has developed a trajectory planning algorithm to calculates the optimal route at every point in the flight, rather than doing it section by section.


New algorithm flies drones faster than human racing pilots

Robohub

To be useful, drones need to be quick. Because of their limited battery life they must complete whatever task they have – searching for survivors on a disaster site, inspecting a building, delivering cargo – in the shortest possible time. And they may have to do it by going through a series of waypoints like windows, rooms, or specific locations to inspect, adopting the best trajectory and the right acceleration or deceleration at each segment. The best human drone pilots are very good at doing this and have so far always outperformed autonomous systems in drone racing. Now, a research group at the University of Zurich (UZH) has created an algorithm that can find the quickest trajectory to guide a quadrotor – a drone with four propellers – through a series of waypoints on a circuit.


New Algorithm Flies Drones Faster than Human Racing Pilots - ELE Times

#artificialintelligence

To be useful, drones need to be quick. Because of their limited battery life, they must complete whatever task they have--searching for survivors on a disaster site, inspecting a building, delivering cargo--in the shortest possible time. And they may have to do it by going through a series of waypoints like windows, rooms, or specific locations to inspect, adopting the best trajectory and the right acceleration or deceleration at each segment. The best human drone pilots are very good at doing this and have so far always outperformed autonomous systems in drone racing. Now, a research group at the University of Zurich (UZH) has created an algorithm that can find the quickest trajectory to guide a quadrotor--a drone with four propellers--through a series of waypoints on a circuit.


Senior, Computer Vision R&D Engineer, SLAM/VIO

#artificialintelligence

Magic Leap's mission is to deliver enterprise a powerful tool for transformation-- an augmented reality platform of great utility and simplicity. Our ultimate vision is to amplify human potential. Our office in Zurich, Switzerland is a center of excellence for Computer Vision and Deep Learning. We are looking for exceptional engineers, passionate about shaping the future of computing. As a Computer Vision R&D Engineer, you'll be responsible for delivering high-performance production software with state-of-the-art computer vision capabilities in the field of SLAM and sensor fusion.


Harnessing AI to Discover New Drugs: Rewriting the Rulebook for Pharmaceutical Research

#artificialintelligence

Artificial intelligence (AI) is able to recognize the biological activity of natural products in a targeted manner, as researchers at ETH Zurich have demonstrated. Moreover, AI helps to find molecules that have the same effect as a natural substance but are easier to manufacture. This opens up huge possibilities for drug discovery, which also has potential to rewrite the rulebook for pharmaceutical research. Nature has a vast store of medicinal substances. "Over 50 percent of all drugs today are inspired by nature," says Gisbert Schneider, Professor of Computer- Assisted Drug Design at ETH Zurich.


Quantifying host-microbiota interactions

Science

The human microbiota is a complex microbial community living on and in our bodies. Its impact on a host's health is immense, affecting digestion ([ 1 ][1]), the immune system ([ 2 ][2]), behavior ([ 3 ][3]), metabolic diseases ([ 4 ][4]), and responses to drugs ([ 5 ][5]–[ 7 ][6]). Rapid advances in experimental and computational methods have moved the human microbiome field from identifying associations between microbiota composition and host health to unraveling the underlying molecular mechanisms ([ 8 ][7]–[ 10 ][8]). However, exactly how much the microbiota contributes to host health is a very difficult question to answer. By focusing on mechanistic and quantitative questions about the microbiome's contributions to host metabolism, I leverage my background in applied mathematics and systems biology to develop computational models describing host-microbiota interactions. Good models require good data from controlled experiments—a challenging proposition in complex host-microbiota systems. As a postdoc, I joined Andy Goodman's lab at Yale University and found myself in a perfect position to collect such data. By combining bacterial genetics with gnotobiotic mouse models, I learned how to modify the microbiome of germ-free, sterile mice. In the Goodman lab, we used these mice to study the contribution of microbiota to host metabolism of a number of pharmaceutical drugs. We found that this was also a good system to quantify host-microbiome interactions in vivo, because the compounds we used can be introduced into the system in a controlled way. We first focused on brivudine, an antiviral compound that can be converted into a potentially toxic metabolite, bromovinyluracil (BVU), by either a host or its microbiome ([ 11 ][9]). To identify bacteria capable of converting brivudine to BVU, we incubated individual bacterial species with the drug in vitro. One of the most potent brivudine metabolizers was Bacteroides thetaiotaomicron , a common gut bacterium with a genetic deletion library readily available. By incubating this library with the drug, we identified one bacterial mutant that had lost the capacity to convert brivudine to BVU. We then colonized germ-free mice with either the wild-type or mutant B. thetaiotaomicron , which provided us with a controllable host-microbiome system and two mouse groups that were identical, save for a single bacterial gene. When we administered brivudine to these two groups, the observed outcome was somewhat puzzling. Although drug levels in the intestine were much higher in mice colonized with the mutant bacterium, serum levels were comparable between the two mouse groups. The metabolite levels showed the opposite pattern: no difference (and very low levels) in the intestine but much higher metabolite levels in the sera of mice colonized with the wild-type bacterium (see the figure). These data could potentially be explained by bacterial conversion of the drug in the intestine and the rapid metabolite absorption into the serum. To test this explanation, we started with a simple kinetic model with two equations describing host drug metabolism in the liver and bacterial drug metabolism in the intestine. Once solved, this equation system showed that the difference between the amounts of metabolite absorbed into the sera of each of the two mouse groups was determined by the amount of BVU produced by microbes in the gut. This controlled experimental setup allowed us to quantify that the bacterial contribution to the toxic drug metabolite in vivo was about 70% ([ 12 ][10]) (see the figure). We expanded the model to describe drug metabolism processes in eight different tissues and in enterohepatic circulation (when the drug metabolized in the liver is secreted back into the small intestine via bile). We then demonstrated that our approach can be generalized to estimate the bacterial contribution to drug metabolism even if the metabolizing species remain unknown by using data from germ-free mice and mice harboring a complex microbial community. We also showed that microbial contribution to the drug metabolite far exceeds the host for sorivudine, an antiviral drug with different host and microbiome metabolism rates, and for clonazepam, an anxiolytic and anticonvulsant drug converted to multiple metabolites ([ 12 ][10]). ![Figure][11] Experimental and computational approaches that quantify host and microbial contributions to drug metabolism Oral drugs are administered to gnotobiotic mice that differ in a single microbial drug-metabolizing enzyme (GNMUT, mutant; GNWT, wild type); drug and drug metabolite kinetics are then quantified across tissues. A microbiome-host pharmacokinetic model developed from these measurements accurately predicts serum metabolite exposure and untangles host and microbiome contributions to drug metabolism. GRAPHIC: ADAPTED FROM M. ZIMMERMANN-KOGADEEVA BY N. CARY/ SCIENCE Quantifying the metabolic host-microbiome interactions is not the only purpose of our model. Having a robust model of host-microbiome interaction allows us to study, explain, and predict the system's behavior in different conditions. By analyzing how drug and metabolite profiles change when model parameters are varied, we found that the similarity of drug serum profiles between germ-free and colonized mice can be explained by the fast and microbiota-independent drug absorption from the small intestine. Our model further suggests that even for rapidly absorbed drugs, microbiome contributions to a host's metabolism can be substantial under certain conditions (e.g., a high microbiome to host ratio of drug metabolism or extensive enterohepatic circulation of the drug and its metabolites) ([ 13 ][12]). Such computational models enable us to investigate host-microbiota interactions in silico, guide experimental design, and help reduce the number of experiments needed to confirm model predictions. To systematically investigate microbial capacity to metabolize drugs, we next conducted a high-throughput in vitro screen. We found that microbiota contribution to drug metabolism might even be more widespread than we anticipated—two-thirds (176 out of 271) of the human-targeted drugs we examined were metabolized by at least one of the 76 tested bacteria ([ 14 ][13]). Although follow-up studies are required to test these microbiota-drug interactions in vivo, our findings emphasize that the microbiota should be considered when developing new drugs, stratifying patients, and choosing the most efficient treatment strategies. In the future, I believe that computational models combined with quantitative experimental data will allow us to measure host-microbiome interactions beyond drug metabolism and to better understand, predict, and control the effect of the microbiome on our health in everyday life. FINALIST Maria Zimmermann-Kogadeeva Maria Zimmermann-Kogadeeva received undergraduate degrees from Lomonosov Moscow State University in Russia and a PhD from ETH Zürich, Switzerland. After completing her postdoctoral fellowships at Yale University in the Goodman group and at European Molecular Biology Laboratory (EMBL) Heidelberg in the Bork group, Maria will start her laboratory in the Genome Biology Unit at EMBL Heidelberg in 2021. Her research combines computational modeling and multiomics data integration to investigate how microbes adapt to their surroundings and how metabolic adaptations of individual bacteria shape the functional outcome of microbial communities and their interactions with the host and the environment. [ www.sciencemag.org/content/373/6551/173.2 ][14] 1. [↵][15]1. H. J. Flint , Nutr. Rev. 70, S10 (2012). [OpenUrl][16][CrossRef][17][PubMed][18] 2. [↵][19]1. A. L. Kau, 2. P. P. Ahern, 3. N. W. Griffin, 4. A. L. Goodman, 5. J. I. Gordon , Nature 474, 327 (2011). [OpenUrl][20][CrossRef][21][PubMed][22][Web of Science][23] 3. [↵][24]1. T. R. Sampson, 2. S. K. Mazmanian , Cell Host Microbe 17, 565 (2015). [OpenUrl][25][CrossRef][26][PubMed][27] 4. [↵][28]1. J. Durack, 2. S. V. Lynch , J. Exp. Med. 216, 20 (2019). [OpenUrl][29][Abstract/FREE Full Text][30] 5. [↵][31]1. P. Spanogiannopoulos, 2. E. N. Bess, 3. R. N. Carmody, 4. P. J. Turnbaugh , Nat. Rev. Microbiol. 14, 273 (2016). [OpenUrl][32][CrossRef][33][PubMed][34] 6. 1. N. Koppel, 2. V. Maini Rekdal, 3. E. P. Balskus , Science 356, eaag2770 (2017). [OpenUrl][35][Abstract/FREE Full Text][36] 7. [↵][37]1. I. D. Wilson, 2. J. K. Nicholson , Transl. Res. 179, 204 (2017). [OpenUrl][38][CrossRef][39][PubMed][40] 8. [↵][41]1. T. S. B. Schmidt, 2. J. Raes, 3. P. Bork , Cell 172, 1198 (2018). [OpenUrl][42][PubMed][43] 9. 1. M. Alexander, 2. P. J. Turnbaugh , Immunity 53, 264 (2020). [OpenUrl][44] 10. [↵][45]1. C. Tropini, 2. K. A. Earle, 3. K. C. Huang, 4. J. L. Sonnenburg , Cell Host Microbe 21, 433 (2017). [OpenUrl][46][CrossRef][47][PubMed][48] 11. [↵][49]1. H. Machida et al. , Biochem. Pharmacol. 49, 763 (1995). [OpenUrl][50][CrossRef][51][PubMed][52] 12. [↵][53]1. M. Zimmermann, 2. M. Zimmermann-Kogadeeva, 3. R. Wegmann, 4. A. L. Goodman , Science 363, eaat9931 (2019). [OpenUrl][54][Abstract/FREE Full Text][55] 13. [↵][56]1. M. Zimmermann-Kogadeeva, 2. M. Zimmermann, 3. A. L. Goodman , Gut Microbes 11, 587 (2020). [OpenUrl][57] 14. [↵][58]1. M. Zimmermann, 2. M. Zimmermann-Kogadeeva, 3. R. Wegmann, 4. A. L. Goodman , Nature 570, 462 (2019). [OpenUrl][59][PubMed][43] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-7 [7]: #ref-8 [8]: #ref-10 [9]: #ref-11 [10]: #ref-12 [11]: pending:yes [12]: #ref-13 [13]: #ref-14 [14]: http://www.sciencemag.org/content/373/6551/173.2 [15]: #xref-ref-1-1 "View reference 1 in text" [16]: {openurl}?query=rft.jtitle%253DNutr.%2BRev.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1753-4887.2012.00499.x%26rft_id%253Dinfo%253Apmid%252F22861801%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [17]: /lookup/external-ref?access_num=10.1111/j.1753-4887.2012.00499.x&link_type=DOI [18]: /lookup/external-ref?access_num=22861801&link_type=MED&atom=%2Fsci%2F373%2F6551%2F173.2.atom [19]: #xref-ref-2-1 "View reference 2 in text" [20]: {openurl}?query=rft.jtitle%253DNature%26rft.stitle%253DNature%26rft.aulast%253DKau%26rft.auinit1%253DA.%2BL.%26rft.volume%253D474%26rft.issue%253D7351%26rft.spage%253D327%26rft.epage%253D336%26rft.atitle%253DHuman%2Bnutrition%252C%2Bthe%2Bgut%2Bmicrobiome%2Band%2Bthe%2Bimmune%2Bsystem.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature10213%26rft_id%253Dinfo%253Apmid%252F21677749%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [21]: /lookup/external-ref?access_num=10.1038/nature10213&link_type=DOI [22]: /lookup/external-ref?access_num=21677749&link_type=MED&atom=%2Fsci%2F373%2F6551%2F173.2.atom [23]: /lookup/external-ref?access_num=000291647100036&link_type=ISI [24]: #xref-ref-3-1 "View reference 3 in text" [25]: {openurl}?query=rft.jtitle%253DCell%2BHost%2BMicrobe%26rft.volume%253D17%26rft.spage%253D565%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.chom.2015.04.011%26rft_id%253Dinfo%253Apmid%252F25974299%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: /lookup/external-ref?access_num=10.1016/j.chom.2015.04.011&link_type=DOI [27]: /lookup/external-ref?access_num=25974299&link_type=MED&atom=%2Fsci%2F373%2F6551%2F173.2.atom [28]: #xref-ref-4-1 "View reference 4 in text" [29]: {openurl}?query=rft.jtitle%253DJ.%2BExp.%2BMed.%26rft_id%253Dinfo%253Adoi%252F10.1084%252Fjem.20180448%26rft_id%253Dinfo%253Apmid%252F30322864%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiamVtIjtzOjU6InJlc2lkIjtzOjg6IjIxNi8xLzIwIjtzOjQ6ImF0b20iO3M6MjQ6Ii9zY2kvMzczLzY1NTEvMTczLjIuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [31]: #xref-ref-5-1 "View reference 5 in text" [32]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BMicrobiol.%26rft.volume%253D14%26rft.spage%253D273%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnrmicro.2016.17%26rft_id%253Dinfo%253Apmid%252F26972811%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [33]: /lookup/external-ref?access_num=10.1038/nrmicro.2016.17&link_type=DOI [34]: /lookup/external-ref?access_num=26972811&link_type=MED&atom=%2Fsci%2F373%2F6551%2F173.2.atom [35]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DKoppel%26rft.auinit1%253DN.%26rft.volume%253D356%26rft.issue%253D6344%26rft.spage%253Deaag2770%26rft.epage%253Deaag2770%26rft.atitle%253DChemical%2Btransformation%2Bof%2Bxenobiotics%2Bby%2Bthe%2Bhuman%2Bgut%2Bmicrobiota%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aag2770%26rft_id%253Dinfo%253Apmid%252F28642381%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [36]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjE3OiIzNTYvNjM0NC9lYWFnMjc3MCI7czo0OiJhdG9tIjtzOjI0OiIvc2NpLzM3My82NTUxLzE3My4yLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [37]: #xref-ref-7-1 "View reference 7 in text" [38]: {openurl}?query=rft.jtitle%253DTransl.%2BRes.%26rft.volume%253D179%26rft.spage%253D204%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.trsl.2016.08.002%26rft_id%253Dinfo%253Apmid%252F27591027%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: /lookup/external-ref?access_num=10.1016/j.trsl.2016.08.002&link_type=DOI [40]: /lookup/external-ref?access_num=27591027&link_type=MED&atom=%2Fsci%2F373%2F6551%2F173.2.atom [41]: #xref-ref-8-1 "View reference 8 in text" [42]: {openurl}?query=rft.jtitle%253DCell%26rft.volume%253D172%26rft.spage%253D1198%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [43]: /lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fsci%2F373%2F6551%2F173.2.atom [44]: {openurl}?query=rft.jtitle%253DImmunity%26rft.volume%253D53%26rft.spage%253D264%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [45]: #xref-ref-10-1 "View reference 10 in text" [46]: {openurl}?query=rft.jtitle%253DCell%2BHost%2BMicrobe%26rft.volume%253D21%26rft.spage%253D433%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.chom.2017.03.010%26rft_id%253Dinfo%253Apmid%252F28407481%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [47]: /lookup/external-ref?access_num=10.1016/j.chom.2017.03.010&link_type=DOI [48]: /lookup/external-ref?access_num=28407481&link_type=MED&atom=%2Fsci%2F373%2F6551%2F173.2.atom [49]: #xref-ref-11-1 "View reference 11 in text" [50]: {openurl}?query=rft.jtitle%253DBiochemical%2Bpharmacology%26rft.stitle%253DBiochem%2BPharmacol%26rft.aulast%253DMachida%26rft.auinit1%253DH.%26rft.volume%253D49%26rft.issue%253D6%26rft.spage%253D763%26rft.epage%253D766%26rft.atitle%253DDeglycosylation%2Bof%2Bantiherpesviral%2B5-substituted%2Barabinosyluracil%2Bderivatives%2Bby%2Brat%2Bliver%2Bextract%2Band%2Benterobacteria%2Bcells.%26rft_id%253Dinfo%253Adoi%252F10.1016%252F0006-2952%252894%252900543-U%26rft_id%253Dinfo%253Apmid%252F7702634%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [51]: /lookup/external-ref?access_num=10.1016/0006-2952(94)00543-U&link_type=DOI [52]: /lookup/external-ref?access_num=7702634&link_type=MED&atom=%2Fsci%2F373%2F6551%2F173.2.atom [53]: #xref-ref-12-1 "View reference 12 in text" [54]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DZimmermann%26rft.auinit1%253DM.%26rft.volume%253D363%26rft.issue%253D6427%26rft.spage%253Deaat9931%26rft.epage%253Deaat9931%26rft.atitle%253DSeparating%2Bhost%2Band%2Bmicrobiome%2Bcontributions%2Bto%2Bdrug%2Bpharmacokinetics%2Band%2Btoxicity%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aat9931%26rft_id%253Dinfo%253Apmid%252F30733391%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [55]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjE3OiIzNjMvNjQyNy9lYWF0OTkzMSI7czo0OiJhdG9tIjtzOjI0OiIvc2NpLzM3My82NTUxLzE3My4yLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [56]: #xref-ref-13-1 "View reference 13 in text" [57]: {openurl}?query=rft.jtitle%253DGut%2BMicrobes%26rft.volume%253D11%26rft.spage%253D587%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [58]: #xref-ref-14-1 "View reference 14 in text" [59]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D570%26rft.spage%253D462%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx


Microscopy deep learning predicts viral infections

#artificialintelligence

IMAGE: Deep Learning detects virus infected cells and predicts acute, severe infections. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to completely suppress and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly. This can lead to serious acute diseases of the lungs or nervous system. The research group of Urs Greber, Professor at the Department of Molecular Life Sciences at the University of Zurich (UZH), has now shown for the first time that a machine-learning algorithm can recognize the cells infected with herpes or adenoviruses based solely on the fluorescence of the cell nucleus.


CoinGeek Zurich: How blockchain can assist AI, machine learning field - CoinGeek

#artificialintelligence

A panel on "AI, Machine Learning & Blockchain" at the CoinGeek Zurich Conference shared valuable insights about artificial intelligence (AI), artificial general intelligence (AGI), and how researchers can use the blockchain to further this field. Panelist Konstantinos Sgantzos, a scientific advisor at Bolooba, kicked it off by describing artificial intelligence as an over-hyped term. "There is no intelligence in AI," he said. "We should redefine it as an analysis of information. When we talk about a narrow AI, we are talking about a specialized agent. We have big data, which is why the blockchain is very important."


Microscopy deep learning predicts viral infections

#artificialintelligence

When viruses infect a cell, changes in the cell nucleus occur, and these can be observed through fluorescence microscopy. Using fluoresence images made in live cells, researchers at the University of Zurich have trained an artificial neural network to reliably recognize cells that are infected by adenoviruses or herpes viruses. The procedure also identifies severe acute infections at an early stage. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to keep completely in check and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly.


Addressing the human cost in a changing climate

Science

Climate change is leading to systemic and existential impacts, and evidence is mounting that these can result in the displacement of human populations. There is a rapidly growing demand for comprehensive risk assessments that include displacement and its associated costs to inform humanitarian response and national planning and coordination. However, owing to complex causation, missing and incomplete data, and the political nature of the issue, the longer-term economic impacts of disaster- and climate-related displacement remain largely hidden. Current approaches are rarely ex ante and prospective and do not consider systemic risk management. Not surprisingly, response-based approaches have shown mixed results, repeatedly demanding substantial resources while not addressing the root causes of displacement. Climate change is not only affecting the intensity, frequency, and duration of hazards that trigger displacement but is also eroding already fragile livelihoods and ecosystems, acting as an aggravator of existing vulnerability and contributing to chronic poverty and conflict in affected countries ([ 1 ][1]). Although disaster risk reduction as a cross-sectoral issue has gained considerable attention over the past two decades, disaster displacement risk is still not fully integrated in national policies and planning. Out of 46 countries included in the 2020 Internal Displacement Index, most acknowledge disaster displacement in principle and have climate policies or national adaptation plans in place. However, only 27 recognize the link between the gradual impacts of climate change and displacement ([ 2 ][2]). With an evidence-based, longer-term vision and investments, climate-related displacement— the forced movement of people in response to a hazard—can be averted and replaced by a range of measures such as planned relocation that is voluntary (at least to a large degree) and financially supported, or by building the resilience of at-risk populations, reducing vulnerability to such an extent that moving is not required. What is missing is a risk-informed framework for country-led, forward-looking approaches to make the case for substantial investment in effective risk reduction, durable solutions for those displaced, and the prevention of new displacement. Applied risk science, using probabilistic models and large empirical datasets compiled over the years, combined with insights from local empirical research and community assessments, now offers the opportunity for a step change in informed decision-making. For example, the shift from deterministic disaster risk assessments, based on historical data, to state-of-the-art probabilistic modeling used by the insurance industry, calibrated with historical data but including randomness to encompass all possible scenarios, presents a notable advance in risk science that is yet to be fully applied to displacement risk. New tools and risk modeling platforms, such as CLIMADA run by ETH Zürich or CAPRA of the World Bank, can now be adapted for displacement risk assessments. Further, assessing the social and economic cost of displacement can provide incentives for transformational action and change, from mere response to disaster displacement to proactively addressing vulnerability and exposure, thereby reducing displacement risk. Disaster displacement is a global reality and everyday occurrence. Millions of disaster displacements have been systematically recorded since 2008—on average, 24.5 million new movements every year ([ 3 ][3]). Weather-related hazards account for almost 90% of all these displacements ([ 2 ][2]), with climate change and the increasing concentration of populations in areas exposed to storms and floods, coupled with socioeconomic drivers of vulnerability, meaning that more people are at risk of being displaced. Demographic, historical, political, and socioeconomic factors determine whether people can withstand the impacts of a physical hazard or environmental stressor or have to leave their homes. Climate change interacts with all of these factors, particularly where resources and the capacities of humans and systems are already stretched ([ 4 ][4]). For example, sea level rise results in loss of land in coastal areas and low-lying atolls of island states, forcing communities to retreat or leave the land altogether. Salinization can reduce crop yields, undermine arable land and freshwater availability, and force people to move. Increasing temperatures affect soil moisture and degradation, which make the soil susceptible to nutrient loss and erosion, thereby destroying the livelihood basis for rural communities. Glacial retreat and melt, loss of biodiversity, and land and forest degradation mean decreased ecosystem services and provisioning services, pushing people to move. Because climate change can also alter the intensity, frequency, and duration of hazard events, climate anomalies and more devastating sudden-onset disasters may follow. Most of the impacts of climate change only result in displacement for those vulnerable to them. This essential point is repeatedly forgotten, with important policy implications ([ 5 ][5]). A prosperous farmer with access to drip irrigation and fertilizers, reliable buyers, loans, and insurance will not be as affected by changes in rainfall patterns as a smallholder subsistence farmer relying on the regularity of seasonal rains or a pastoralist in search of pasture for his herd. An urban dweller with an office job and regular income will not need to leave his home because of the loss of mangroves, which are providing sustenance to millions in coastal communities. Nonetheless, although individual vulnerability leads to a risk of adverse displacement outcomes, disaster and climate risks are increasingly becoming systemic because high-level and widespread impacts may ripple through social and economic networks, incurring further adverse micro and macro impacts and disruptions ([ 6 ][6]). Climate change is thus a displacement trigger in its own right (e.g., loss of coastlines through sea level rise and coastal erosion), a visible aggravator (e.g., when livelihoods are eroded because of soil degradation and loss of ecosystem services), and a hidden aggravator (e.g., increasing the intensity of cyclonic winds and shifting rainfall patterns that result in floods). But the impacts of climate change interact with broader changes in the physical and social environment, resulting in potentially rising costs associated with future displacement. ![Figure][7] Global disaster displacement risk relative to population size Average Annual Displacement (AAD) risk is a compact metric that represents the estimated effect, accumulated over a long time frame, of future small to medium and extreme events and estimates the likely displacement associated with them on a yearly basis for sudden-onset hazards such as tsunamis, cyclonic winds, storm surges, and riverine floods. See ([ 10 ][8]) for details. Each country's AAD risk relative to its population size is shown (expected annual displacements / 10,000 people). Country income group classification from the World Bank. GRAPHIC: N. DESAI/ SCIENCE BASED ON B. DESAI ET AL. Disaster displacement often undermines the welfare and well-being of affected individuals and communities and can also incur a substantial social and economic burden on countries. Although many countries have begun to plan for t he risk of extreme events in one way or another, governments typically do not formally account for displacement risk and their associated costs in national development plans and annual budgets of line ministries. Even without taking into account the aggravating forces of climate change, there is growing evidence that displacement not only severely disrupts the lives of those forced to flee their homes but also has an economic impact on local communities and national economies ([ 7 ][9]). The direct cost of providing every internally displaced person (totaling more than 55 million in 2020) with support for housing, education, health, and security has been estimated at US$370 per person per year, accumulating to more than US$20.5 billion for 2020 ([ 2 ][2]). These figures are mostly based on information available from protracted conflict-related displacement situations because the economic impacts of displacements linked with disasters and climate change usually go unrecorded. A key knowledge gap exists here because only limited event-based or nationally aggregated data is available on how long people remain displaced after a disaster, despite ample evidence that this type of displacement is often long-term and can become protracted ([ 2 ][2]). These impacts can add up to billions of dollars worldwide. Each time one person is displaced, even for a few days, costs arise for transportation, shelter, food and nonfood items, and the loss of income if the person cannot continue their usual work. Adding in long-term consequences, such as lack of schooling, training, and on-the-job experience, increases this economic impact. These costs should be on national balance sheets but are instead most often borne by communities themselves, by local governments that have to divert already limited development funds to response, and by humanitarian actors. In the face of increasingly severe disaster- and climate-related displacement, these costs are only set to rise. The highest economic impacts usually stem from the loss of income and the need to provide displaced people with shelter and health care. Disaster-resilient housing and livelihoods, as well as strong primary health care systems, are also where investments are needed most ahead of disaster events to reduce displacement and enable lasting solutions. By nature of its mandate, humanitarian response is not set up to invest in resilient livelihoods or infrastructure and service development. It is not only low-income nations that are at risk of economic impacts due to displacement. During the 2019–2020 bushfires in Australia, the loss of economic production as a result of people missing just one day of work during displacement was estimated to be about US$510 per person ([ 8 ][10]). These costs add up, particularly if a disaster causes considerable housing destruction, which may delay people from returning to their homes for months. The cost of covering housing needs resulting from Australia's Black Summer bushfires was estimated to be between US$44 million and US$52 million for a year, posing a substantial financial burden, given that previous recovery efforts indicate that it can take people between 1 and 4 years to rebuild their homes ([ 8 ][10]). These numbers and examples from across the globe highlight that we need to get better at understanding and assessing the nature and scale of disaster displacement risk. The coverage and detail of relevant datasets have improved, and various models and approaches exist at regional and global scales, although their time frames, methods, and resulting estimates vary enormously. For example, the World Bank, using a gravity model and new data on climate change, water availability, and crop production, has estimated that slow-onset climate hazards such as water scarcity and declining crop yields could lead to more than 100 million additional internal migrants in Latin America, South Asia, and sub-Saharan Africa by 2050 should neither accelerating climate impacts nor unequal development be adequately addressed ([ 9 ][11]). In many such assessments, there is a strong focus on environmental stressors and hazards, and on climate change's impacts on their intensity and frequency. This may have potentially resulted in inflated numbers and certainly in an inflated perception regarding the role of climate change in the dynamics of human mobility and forced movements today and in the coming decades. Estimates from a probabilistic model that takes housing rendered uninhabitable as a proxy for displacement in sudden-onset disasters, such as floods and cyclones, suggest that an average of around 14 million displacements can be expected each year (a conservative approach that is highly likely to be an underestimate) ([ 10 ][8]). This displacement risk is heavily concentrated in the Asia-Pacific region, where both exposure and vulnerability are high. Even in relative terms—that is, numbers of potential displacements in relation to population size—displacement risk is high not only for South and East Asia but also for Pacific and other small island states (see the first figure). Climate change as well as changes in population size and composition and of key social and economic indicators all affect how this displacement risk may change in the future. According to probabilistic, spatially explicit risk modeling that uses ensembles of climate models and hydrodynamic modeling to quantify flood hazard, is calibrated on past events, and incorporates commonly used climate change and development scenarios, rapidly increasing exposure due to population growth may be the largest driver of displacement risk in the future ([ 11 ][12]). Nevertheless, this strong role of population size should not overshadow the fact that the substantial increase related to climate change is not trivial (see the second figure). New assessments show that we can expect a 50% increase in displacement risk related to floods for each degree of temperature increase ([ 11 ][12]). Although, currently, various epistemic uncertainties need to be reckoned with, such projections serve to illustrate the future burden to consider in a rapidly warming and changing climate. Beyond probabilistic and deterministic disaster displacement risk models, there are other modeling approaches that can increasingly be put to the task. Agent-based network models can assess individual-level impacts and costs through a bottom-up methodology that can reflect how shocks to one part of a system (community, economy, country, or region) can cascade through the whole system and also spill over into other systems ([ 12 ][13]). Further, a system dynamics approach can describe in a relatively comprehensive manner the relationships between a wide range of dimensions and indicators, although it requires granular datasets that are often unavailable and is highly cost- and labor-intensive to develop. ![Figure][7] Change in flood displacement risk Shaded areas show different scenarios of flood displacement risk based on a range of climate and hydrological models, relative to historical baseline. The width of the shading represents an estimate of the uncertainty induced by natural climate variability and limitations in current understanding of the climate system and hydrological systems. Dashed lines show the average values across models. Historical baseline is defined by the average flood hazard frequency and intensity from 1976 to 2005, combined with population data for 2000. RCPs reflect different trajectories of variation in atmospheric GHG concentrations. SSPs reflect different scenarios of global socioeconomic development. Modified from ([ 11 ][12]). GRAPHIC: KELLIE HOLOSKI/ SCIENCE BASED ON KAM ET AL. ([ 11 ][12]) Finally, integrating risk estimates with analysis of public finance allows quantification of the relevance and “additionality” of internal displacement impacts on governments' (and often donors') budgets. First attempts at undertaking this analysis, adapting the International Institute for Applied Systems Analysis (IIASA) catastrophe simulation model (CatSim) in support of public financing strategies in pre- and postdisaster contexts, have shown that the cost of internal displacement can substantially increase national and global budget gaps (fiscal gaps) and the chance of budget crises ([ 13 ][14]). F or example, in Bangladesh, a disaster with a return period of 50 years can be expected to incur costs related to internal displacement of nearly US$4.1 billion per year of subsequent displacement; a smaller magnitude but more frequent disaster with a return period of 10 years would incur more than US$1 billion. The estimated possible amount of funding that the country may be able to divert from existing development budgets and credit buffers adds up to just over US$1 billion of fiscal resilience, which means that Bangladesh is likely to be unable to cover the costs associated with internal displacement for events that occur every 10 years on average. Further estimates of such costs can provide the basis for making the case for preventive action and for developing appropriate financial instruments such as national reserve funds, enhanced social protection schemes, and catastrophe bonds, as well as regional or global sovereign insurance pools ([ 14 ][15]). Beyond these first steps in developing basic estimates of the costs, further work is required to better understand who bears these and how benefits from improved policies would be distributed across different segments of society. Comprehensive risk assessments that account for displacement risk and estimate its economic costs signal a need to improve coordination on budget allocations and cooperation in program execution across ministries and public and private sectors. This would enable the explicit inclusion of these contingent risks into budget stress-testing procedures and other risk-management planning processes. It would also provide incentives for managing risk with an ex ante approach, because it anticipates the ex post consequences and trade-offs involved in responding to shocks ([ 13 ][14]). Risk assessments should help communities and local and national governments grappling with immediate displacement risk or the prospect of intensifying natural hazards or loss of territory or habitats. More financing must be made available for localized, granular displacement risk assessments, which municipalities can use to inform urban development plans, zoning regulations, and local building codes and for forward-looking, long-term planning for relocation where necessary. Recent attempts at providing a measure for displacement risk and its impacts are only the first step. In the coming years, further investment should build on the promises of longer-term risk modeling and couple its results with impact assessments so that countries can build displacement estimates into their multiyear development plans ([ 15 ][16]). Understanding needs and priorities in the decision-making processes of affected populations, institutional capacities, and socioeconomic dynamics, even if less systematically assessed, will be at least as important at indicating what the future holds. Given the scope and complexity of the problem, a pluralistic methodological setup is required to contribute to a better understanding of displacement risk and to inform effective policy and response under a broad range of circumstances. 1. [↵][17]Intergovernmental Panel on Climate Change (IPCC), Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, Eds. (IPCC, 2014). 2. [↵][18]Internal Displacement Monitoring Centre (IDMC), Global Report on Internal Displacement 2021 (2021); [www.internal-displacement.org/global-report/grid2021][19]. 3. [↵][20]IDMC, Global Internal Displacement Database; [www.internal-displacement.org/database][21]. 4. [↵][22]1. M. Brzoska, 2. C. Fröhlich , Migr. Dev. 5, 190 (2016). [OpenUrl][23][CrossRef][24] 5. [↵][25]Economics of Climate Adaptation (ECA), “Shaping climate-resilient development: A framework for decision-making. A report of the Economics of Climate Adaptation Working Group” (ECA, 2009); [www.ethz.ch/content/dam/ethz/special-interest/usys/ied/wcr-dam/documents/Economics\_of\_Climate\_Adaptation\_ECA.pdf][26]. 6. [↵][27]1. C. Raymond et al ., Nat. Clim. Chang. 10, 611 (2020). [OpenUrl][28] 7. [↵][29]1. S. Ambrus 1. C. Cazabat, 2. L. Yasukawa , “Unveiling the cost of internal displacement. 2020 report,” S. Ambrus, Ed. (IDMC, 2020); [www.internal-displacement.org/sites/default/files/publications/documents/IDMC\_CostEstimate\_final.pdf][30]. 8. [↵][31]1. J. Lennard 1. E. du Parc, 2. L. Yasukawa , “The 2019–2020 Australian bushfires: From temporary evacuation to longer-term displacement,” J. Lennard, Ed. (IDMC, 2020); [www.internal-displacement.org/sites/default/files/publications/documents/Australian%20bushfires_Final.pdf][32]. 9. [↵][33]1. K. K. Rigaud et al ., “Groundswell: Preparing for internal climate migration” (World Bank, 2018); . 10. [↵][34]IDMC, “Global disaster displacement risk – A baseline for future work” (2017); [www.internal-displacement.org/publications/global-disaster-displacement-risk-a-baseline-for-future-work][35]. 11. [↵][36]1. P. M. Kam et al ., Environ. Res. Lett. 16, 044026 (2020). [OpenUrl][37] 12. [↵][38]1. A. Naqvi, 2. F. Gaupp, 3. S. Hochrainer-Stigler , OR Spectrum 42, 727 (2020). [OpenUrl][39] 13. [↵][40]IDMC, IIASA, “Points of no return: Estimating governments' fiscal resilience to internal displacement” (IDMC, 2020); [www.internal-displacement.org/sites/default/files/publications/documents/201903-fiscal-risk-paper.pdf][41]. 14. [↵][42]1. J. Linnerooth-Bayer, 2. S. Hochrainer-Stigler , Clim. Change 133, 85 (2015). [OpenUrl][43] 15. [↵][44]1. S. Hochrainer-Stigler et al ., Int. J. Disaster Risk Reduct. 24, 482 (2017). [OpenUrl][45] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: pending:yes [8]: #ref-10 [9]: #ref-7 [10]: #ref-8 [11]: #ref-9 [12]: #ref-11 [13]: #ref-12 [14]: #ref-13 [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: #xref-ref-2-1 "View reference 2 in text" [19]: http://www.internal-displacement.org/global-report/grid2021 [20]: #xref-ref-3-1 "View reference 3 in text" [21]: http://www.internal-displacement.org/database [22]: #xref-ref-4-1 "View reference 4 in text" [23]: {openurl}?query=rft.jtitle%253DMigr.%2BDev.%26rft.volume%253D5%26rft.spage%253D190%26rft_id%253Dinfo%253Adoi%252F10.1080%252F21632324.2015.1022973%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [24]: /lookup/external-ref?access_num=10.1080/21632324.2015.1022973&link_type=DOI [25]: #xref-ref-5-1 "View reference 5 in text" [26]: http://www.ethz.ch/content/dam/ethz/special-interest/usys/ied/wcr-dam/documents/Economics_of_Climate_Adaptation_ECA.pdf [27]: #xref-ref-6-1 "View reference 6 in text" [28]: {openurl}?query=rft.jtitle%253DNat.%2BClim.%2BChang.%26rft.volume%253D10%26rft.spage%253D611%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [29]: #xref-ref-7-1 "View reference 7 in text" [30]: http://www.internal-displacement.org/sites/default/files/publications/documents/IDMC_CostEstimate_final.pdf [31]: #xref-ref-8-1 "View reference 8 in text" [32]: http://www.internal-displacement.org/sites/default/files/publications/documents/Australian%20bushfires_Final.pdf [33]: #xref-ref-9-1 "View reference 9 in text" [34]: #xref-ref-10-1 "View reference 10 in text" [35]: http://www.internal-displacement.org/publications/global-disaster-displacement-risk-a-baseline-for-future-work [36]: #xref-ref-11-1 "View reference 11 in text" [37]: {openurl}?query=rft.jtitle%253DEnviron.%2BRes.%2BLett.%26rft.volume%253D16%26rft.spage%253D044026%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [38]: #xref-ref-12-1 "View reference 12 in text" [39]: {openurl}?query=rft.jtitle%253DOR%2BSpectrum%26rft.volume%253D42%26rft.spage%253D727%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [40]: #xref-ref-13-1 "View reference 13 in text" [41]: http://www.internal-displacement.org/sites/default/files/publications/documents/201903-fiscal-risk-paper.pdf [42]: #xref-ref-14-1 "View reference 14 in text" [43]: {openurl}?query=rft.jtitle%253DClim.%2BChange%26rft.volume%253D133%26rft.spage%253D85%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: #xref-ref-15-1 "View reference 15 in text" [45]: {openurl}?query=rft.jtitle%253DInt.%2BJ.%2BDisaster%2BRisk%2BReduct.%26rft.volume%253D24%26rft.spage%253D482%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx