Relocating participation within a radical politics of development

In response to (and in sympathy with) many of the critical points that have been lodged against participatory approaches to development and governance within international development, this article seeks to relocate participation within a radical politics of development. We argue that participation needs to be theoretically and strategically informed by a notion of ‘citizenship’, and be located within the ‘critical modernist’ approach to development. Using empirical evidence drawn from a wide range of contemporary approaches to participation, the paper shows that participatory approaches are most likely to succeed where (i) they are pursued as part of a wider radical political project; (ii) where they are aimed specifically at securing citizenship rights and participation for marginal and subordinate groups; and (iii) when they seek to engage with development as an underlying process of social change rather than in the form of discrete technocratic interventions. However, we do not use these findings to argue against using participatory methods where these conditions are not met. Finally, the paper considers the implications of this relocation for participation in both theoretical and strategic terms

Inferring causation from time series in Earth system sciences

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers

Übersterblichkeit während der SARS-CoV-2-Pandemie in Frankfurt am Main, Deutschland, in den Jahren 2020 und 2021, unter Berücksichtigung des Alterstrends der Bevölkerung und der Pandemiephasen

Aims: Excess mortality during the SARS-CoV-2 pandemic has been studied in many countries. Accounting for population aging has important implications for excess mortality estimates. We show the importance of adjustment for age trends in a small-scale mortality analysis as well as the importance of analysing different pandemic phases for mortality in an urban population. Methods: Population data for Frankfurt/Main for 2016-2021 were obtained from the Municipal Office of Statistics, City of Frankfurt/Main. Mortality data from 2016 to 2021 were provided by the Hessian State Authority. For standardized mortality ratios (SMR=observed number of deaths divided by the expected number of deaths), the expected number of deaths was calculated in two ways: For SMRcrude, the mean mortality rate from the years 2016-2019 was multiplied by the total number of residents in 2020 and 2021 separately. For SMRadjusted, this procedure was performed separately for five age groups, and the numbers of expected deaths per age group were added. Results: SMRcrude was 1.006 (95% CI: 0.980-1.031) in 2020, and 1.047 (95% CI: 1.021-1.073) in 2021. SMRadjusted was 0.976 (95% CI: 0.951-1.001) in 2020 and 0.998 (95% CI: 0.973-1.023) in 2021. Excess mortality was observed during pandemic wave 2, but not during pandemic waves 1 and 3. Conclusion: Taking the aging of the population into account, no excess mortality was observed in Frankfurt/Main in 2020 and 2021. Without adjusting for population aging trends in Frankfurt /Main, mortality would have been greatly overestimated.Ziele: Die Übersterblichkeit während der SARS-CoV-2-Pandemie wurde in vielen Ländern untersucht. Die Berücksichtigung der Alterung der Bevölkerung hat wichtige Auswirkungen auf die Schätzung der Übersterblichkeit. Wir zeigen, wie wichtig die Berücksichtigung von Alterstrends in einer kleinmaßstäblichen Mortalitätsanalyse ist, und wie wichtig es ist, verschiedene Pandemiephasen für die Mortalität in einer städtischen Bevölkerung zu analysieren. Methoden: Die Bevölkerungsdaten für Frankfurt am Main für die Jahre 2016 bis 2021 wurden vom Bürgeramt Statistik und Wahlen der Stadt Frankfurt am Main erhalten. Die Mortalitätsdaten 2016 bis 2021 wurden vom Hessischen Landesamt zur Verfügung gestellt. Für die Standardisierte Mortalitätsratio (SMR=beobachtete Zahl der Sterbefälle geteilt durch die erwartete Zahl der Sterbefälle) wurde die erwartete Zahl der Sterbefälle auf zwei Arten berechnet: Für die SMRroh wurde die mittlere Mortalitätsrate aus den Jahren 2016-2019 mit der Gesamtzahl der Bürger im Jahr 2020 bzw. 2021 multipliziert. Für die SMRadjustiert wurde dieses Verfahren für fünf Altersgruppen getrennt durchgeführt und die Zahlen der erwarteten Todesfälle pro Altersgruppe wurden addiert. Ergebnisse: Die SMRroh betrug 1,006 (95% CI: 0,980-1,031) im Jahr 2020 und 1,047 (95% CI: 1,021-1,073) im Jahr 2021. Die SMRadjustiert betrug 0,976 (95% CI: 0,951-1,001) und 0,998 (95% CI: 0,973-1,023) im Jahr 2021. Eine Übersterblichkeit wurde während der Pandemiewelle 2 beobachtet, nicht jedoch während der Pandemiewellen 1 und 3. Schlussfolgerung: Unter Berücksichtigung der Alterung der Bevölkerung wurde in den Jahren 2020 und 2021 in Frankfurt am Main keine Übersterblichkeit beobachtet. Ohne Anpassung an die Alterung der Bevölkerung in Frankfurt am Main wäre die Sterblichkeit stark überschätzt worden

Avoiding future Famines : Strengthening the Ecological Foundation of Food Security through Sustainable Food Systems

A UNEP Synthesis Repor

BOBA FRET: Bootstrap-based analysis of single-molecule FRET data

Time-binned single-molecule Förster resonance energy transfer (smFRET) experiments with surface-tethered nucleic acids or proteins permit to follow folding and catalysis of single molecules in real-time. Due to the intrinsically low signal-to-noise ratio (SNR) in smFRET time traces, research over the past years has focused on the development of new methods to extract discrete states (conformations) from noisy data. However, limited observation time typically leads to pronounced cross-sample variability, i.e., single molecules display differences in the relative population of states and the corresponding conversion rates. Quantification of cross-sample variability is necessary to perform statistical testing in order to assess whether changes observed in response to an experimental parameter (metal ion concentration, the presence of a ligand, etc.) are significant. However, such hypothesis testing has been disregarded to date, precluding robust biological interpretation. Here, we address this problem by a bootstrap-based approach to estimate the experimental variability. Simulated time traces are presented to assess the robustness of the algorithm in conjunction with approaches commonly used in thermodynamic and kinetic analysis of time-binned smFRET data. Furthermore, a pair of functionally important sequences derived from the self-cleaving group II intron Sc.ai5γ (d3'EBS1*/IBS1*) is used as a model system. Through statistical hypothesis testing, divalent metal ions are shown to have a statistically significant effect on both thermodynamic and kinetic aspects of their interaction. The Matlab source code used for analysis (bootstrap-based analysis of smFRET data, BOBA FRET), as well as a graphical user interface, is available via http://www.aci.uzh.ch/rna/