Origin and Evolution of Antibiotic Resistance: The Common Mechanisms of Emergence and Spread in Water Bodies

The environment, and especially freshwater, constitutes a reactor where the evolution and the rise of new resistances occur. In water bodies such as waste water effluents, lakes, and rivers or streams, bacteria from different sources, e.g., urban, industrial, and agricultural waste, probably selected by intensive antibiotic usage, are collected and mixed with environmental species. This may cause two effects on the development of antibiotic resistances: first, the contamination of water by antibiotics or other pollutants lead to the rise of resistances due to selection processes, for instance, of strains over-expressing broad range defensive mechanisms, such as efflux pumps. Second, since environmental species are provided with intrinsic antibiotic resistance mechanisms, the mixture with allochthonous species is likely to cause genetic exchange. In this context, the role of phages and integrons for the spread of resistance mechanisms appears significant. Allochthonous species could acquire new resistances from environmental donors and introduce the newly acquired resistance mechanisms into the clinics. This is illustrated by clinically relevant resistance mechanisms, such as the fluoroquinolones resistance genes qnr. Freshwater appears to play an important role in the emergence and in the spread of antibiotic resistances, highlighting the necessity for strategies of water quality improvement. We assume that further knowledge is needed to better understand the role of the environment as reservoir of antibiotic resistances and to elucidate the link between environmental pollution by anthropogenic pressures and emergence of antibiotic resistances. Only an integrated vision of these two aspects can provide elements to assess the risk of spread of antibiotic resistances via water bodies and suggest, in this context, solutions for this urgent health issue

Probabilistic Dataset Reconstruction from Interpretable Models

Interpretability is often pointed out as a key requirement for trustworthy machine learning. However, learning and releasing models that are inherently interpretable leaks information regarding the underlying training data. As such disclosure may directly conflict with privacy, a precise quantification of the privacy impact of such breach is a fundamental problem. For instance, previous work have shown that the structure of a decision tree can be leveraged to build a probabilistic reconstruction of its training dataset, with the uncertainty of the reconstruction being a relevant metric for the information leak. In this paper, we propose of a novel framework generalizing these probabilistic reconstructions in the sense that it can handle other forms of interpretable models and more generic types of knowledge. In addition, we demonstrate that under realistic assumptions regarding the interpretable models' structure, the uncertainty of the reconstruction can be computed efficiently. Finally, we illustrate the applicability of our approach on both decision trees and rule lists, by comparing the theoretical information leak associated to either exact or heuristic learning algorithms. Our results suggest that optimal interpretable models are often more compact and leak less information regarding their training data than greedily-built ones, for a given accuracy level

Adversarial training approach for local data debiasing

The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting discriminations. In this work, we propose a novel approach called GANsan whose objective is to prevent the possibility of any discrimination i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our sanitization algorithm GANsan is partially inspired by the powerful framework of generative adversarial networks (in particular the Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible and thus preserving the interpretability of the sanitized data. As a consequence, once the sanitizer is trained, it can be applied to new data, such as for instance, locally by an individual on his profile before releasing it. Finally, experiments on a real dataset demonstrate the effectiveness of the proposed approach as well as the achievable trade-off between fairness and utility

Universal Loss Dynamics in a Unitary Bose Gas

The low temperature unitary Bose gas is a fundamental paradigm in few-body and many-body physics, attracting wide theoretical and experimental interest. Here we first present a theoretical model that describes the dynamic competition between two-body evaporation and three-body re-combination in a harmonically trapped unitary atomic gas above the condensation temperature. We identify a universal magic trap depth where, within some parameter range, evaporative cooling is balanced by recombination heating and the gas temperature stays constant. Our model is developed for the usual three-dimensional evaporation regime as well as the 2D evaporation case. Experiments performed with unitary 133 Cs and 7 Li atoms fully support our predictions and enable quantitative measurements of the 3-body recombination rate in the low temperature domain. In particular, we measure for the first time the Efimov inelasticity parameter $\eta$ * = 0.098(7) for the 47.8-G d-wave Feshbach resonance in 133 Cs. Combined 133 Cs and 7 Li experimental data allow investigations of loss dynamics over two orders of magnitude in temperature and four orders of magnitude in three-body loss. We confirm the 1/T 2 temperature universality law up to the constant $\eta$ *

Functional interplay between NTP leaving group and base pair recognition during RNA polymerase II nucleotide incorporation revealed by methylene substitution.

RNA polymerase II (pol II) utilizes a complex interaction network to select and incorporate correct nucleoside triphosphate (NTP) substrates with high efficiency and fidelity. Our previous 'synthetic nucleic acid substitution' strategy has been successfully applied in dissecting the function of nucleic acid moieties in pol II transcription. However, how the triphosphate moiety of substrate influences the rate of P-O bond cleavage and formation during nucleotide incorporation is still unclear. Here, by employing β,γ-bridging atom-'substituted' NTPs, we elucidate how the methylene substitution in the pyrophosphate leaving group affects cognate and non-cognate nucleotide incorporation. Intriguingly, the effect of the β,γ-methylene substitution on the non-cognate UTP/dT scaffold (∼3-fold decrease in kpol) is significantly different from that of the cognate ATP/dT scaffold (∼130-fold decrease in kpol). Removal of the wobble hydrogen bonds in U:dT recovers a strong response to methylene substitution of UTP. Our kinetic and modeling studies are consistent with a unique altered transition state for bond formation and cleavage for UTP/dT incorporation compared with ATP/dT incorporation. Collectively, our data reveals the functional interplay between NTP triphosphate moiety and base pair hydrogen bonding recognition during nucleotide incorporation

Ultrahigh finesse Fabry-Perot superconducting resonator

We have built a microwave Fabry-Perot resonator made of diamond-machined copper mirrors coated with superconducting niobium. Its damping time (Tc = 130 ms at 51 GHz and 0.8 K) corresponds to a finesse of 4.6 x 109, the highest ever reached for a Fabry-Perot in any frequency range. This result opens novel perspectives for quantum information, decoherence and non-locality studies

The Impact of Corporate Climate Action on Financial Markets: Evidence from Climate-Related Patents

We study the impact of climate-related patents on financial markets. We exploit the quasi-random assignment of patent examiners with different degrees of leniency as an exogenous shock in patent approvals to allow for causal interpretations. We find that firms with more lucky climate-related patents subsequently display higher positive cumulative abnormal stock returns and enjoy a lower cost of capital, compared with similarly innovative but unlucky firms. These results hold especially during periods of high attention towards climate change and for initial climate patent granting. Firms with more lucky climate-related patents also exhibit better environmental ratings and attract more responsible institutional investors. OLS regressions show that firms developing more climate-related technologies reduce more direct carbon emission intensity