Deammonification Potential of Pig Slurries and Vapor Condensates from Sewage Sludge Drying—Substrate Quality and Inhibition

Deammonification is a well-established process for sludge liquor treatment and promising for wastewaters with high nitrogen loads because of its low energy demand compared to nitrification/denitrification. Two wastewaters with high NH4-N concentrations and a rising significance in Germany—pig slurry (12 samples) and condensates from sewage sludge drying (6 samples)—were studied for their deammonification potential. Furthermore, a comprehensive quality assessment is presented. Both wastewaters show a wide range in terms of CODt, CODs, TN and NH4-N, whereby condensates show a greater variability with no direct relation to dryer type or temperature. In the slurries, CODt shows a relative standard deviation of 106% (mean 21.1 g/L) and NH4-N of 33% (mean 2.29 g/L), while in condensates it reaches 148% for CODt (mean 2.0 g/L) and 122% for NH4-N (mean 0.7 g/L). No inhibition of ammonium-oxidizing-bacteria was detected in the slurries, while two out of five condensates showed an inhibition of >40%, one of >10% and two showed no inhibition at all. Since the inhibition could be avoided by mixing, deammonification can be recommended for condensate treatment. For slurry treatment, the importance of employing some form of solid-liquid-separation as a pretreatment was noted due to the associated COD

IST Austria Technical Report

In this work we present a flexible tool for tumor progression, which simulates the evolutionary dynamics of cancer. Tumor progression implements a multi-type branching process where the key parameters are the fitness landscape, the mutation rate, and the average time of cell division. The fitness of a cancer cell depends on the mutations it has accumulated. The input to our tool could be any fitness landscape, mutation rate, and cell division time, and the tool produces the growth dynamics and all relevant statistics

The effect of one additional driver mutation on tumor progression

Tumor growth is caused by the acquisition of driver mutations, which enhance the net reproductive rate of cells. Driver mutations may increase cell division, reduce cell death, or allow cells to overcome density-limiting effects. We study the dynamics of tumor growth as one additional driver mutation is acquired. Our models are based on two-type branching processes that terminate in either tumor disappearance or tumor detection. In our first model, both cell types grow exponentially, with a faster rate for cells carrying the additional driver. We find that the additional driver mutation does not affect the survival probability of the lesion, but can substantially reduce the time to reach the detectable size if the lesion is slow growing. In our second model, cells lacking the additional driver cannot exceed a fixed carrying capacity, due to density limitations. In this case, the time to detection depends strongly on this carrying capacity. Our model provides a quantitative framework for studying tumor dynamics during different stages of progression. We observe that early, small lesions need additional drivers, while late stage metastases are only marginally affected by them. These results help to explain why additional driver mutations are typically not detected in fast-growing metastases

Forgiver Triumphs in Alternating Prisoner's Dilemma

Cooperative behavior, where one individual incurs a cost to help another, is a wide spread phenomenon. Here we study direct reciprocity in the context of the alternating Prisoner's Dilemma. We consider all strategies that can be implemented by one and two-state automata. We calculate the payoff matrix of all pairwise encounters in the presence of noise. We explore deterministic selection dynamics with and without mutation. Using different error rates and payoff values, we observe convergence to a small number of distinct equilibria. Two of them are uncooperative strict Nash equilibria representing always-defect (ALLD) and Grim. The third equilibrium is mixed and represents a cooperative alliance of several strategies, dominated by a strategy which we call Forgiver. Forgiver cooperates whenever the opponent has cooperated; it defects once when the opponent has defected, but subsequently Forgiver attempts to re-establish cooperation even if the opponent has defected again. Forgiver is not an evolutionarily stable strategy, but the alliance, which it rules, is asymptotically stable. For a wide range of parameter values the most commonly observed outcome is convergence to the mixed equilibrium, dominated by Forgiver. Our results show that although forgiving might incur a short-term loss it can lead to a long-term gain. Forgiveness facilitates stable cooperation in the presence of exploitation and noise

The effect of one additional driver mutation on tumor progression

Tumor growth is caused by the acquisition of driver mutations, which enhance the net reproductive rate of cells. Driver mutations may increase cell division, reduce cell death, or allow cells to overcome density-limiting effects. We study the dynamics of tumor growth as one additional driver mutation is acquired. Our models are based on two-type branching processes that terminate in either tumor disappearance or tumor detection. In our first model, both cell types grow exponentially, with a faster rate for cells carrying the additional driver. We find that the additional driver mutation does not affect the survival probability of the lesion, but can substantially reduce the time to reach the detectable size if the lesion is slow growing. In our second model, cells lacking the additional driver cannot exceed a fixed carrying capacity, due to density limitations. In this case, the time to detection depends strongly on this carrying capacity. Our model provides a quantitative framework for studying tumor dynamics during different stages of progression. We observe that early, small lesions need additional drivers, while late stage metastases are only marginally affected by them. These results help to explain why additional driver mutations are typically not detected in fast-growing metastases.MathematicsOrganismic and Evolutionary Biolog

Photoprotecting uracil by coupling with lossy nanocavities

We analyze how the photorelaxation dynamics of a molecule can be controlled by modifying its electromagnetic environment using a nanocavity mode. In particular, we consider the photorelaxation of the RNA nucleobase uracil, which is the natural mechanism to prevent photodamage. In our theoretical work, we identify the operative conditions in which strong coupling with the cavity mode can open an efficient photoprotective channel, resulting in a relaxation dynamics twice as fast as the natural one. We rely on a state-of-the-art chemically detailed molecular model and a non-Hermitian Hamiltonian propagation approach to perform full-quantum simulations of the system dissipative dynamics. By focusing on the photon decay, our analysis unveils the active role played by cavity-induced dissipative processes in modifying chemical reaction rates, in the context of molecular polaritonics. Remarkably, we find that the photorelaxation efficiency is maximized when an optimal trade-off between light-matter coupling strength and photon decay rate is satisfied. This result is in contrast with the common intuition that increasing the quality factor of nanocavities and plasmonic devices improves their performance. Finally, we use a detailed model of a metal nanoparticle to show that the speedup of the uracil relaxation could be observed via coupling with a nanosphere pseudomode, without requiring the implementation of complex nanophotonic structuresThis work has been funded by the European Research Council through Grants ERC-2016-StG- 714870 (S. Felicetti, J. Feist, and J. Fregoni) and ERC-2015- CoG-681285 (J. Fregoni, PI Stefano Corni) and by the Spanish Ministry for Science, Innovation, and Universities - Agencia Estatal de Investigación through Grants RTI2018- 099737-B-I00, PCI2018-093145 (through the QuantERA program of the European Commission), and MDM-2014- 0377 (through the Marıá de Maeztu program for Units of Excellence in R&D). T. Schnappinger and R. de Vivie-Riedle gratefully acknowledge the DFG Normalverfahren. S. Reiter gratefully acknowledges financial support by the International Max Planck Research School of Advanced Photon Science (IMPRS-APS