Organohalide respiration: variations on a common theme

Organohalide respiration (OHR) is a bacterial anaerobic respiratory metabolism that makes use of halogenated organic compounds as terminal electron acceptors. While organohalogens have been initially thought to be mainly from anthropogenic origin, thousands of them are naturally produced [1]. Organohalide-respiring bacteria (OHRB) have been isolated for the first time 30 years ago and revealed to be a key player in the bioremediation of environments polluted with organohalogens. Among OHRB, Dehalobacter restrictus and Desulfitobacterium hafniense represent two model bacteria that belong to the Firmicutes. While the former is an obligate OHRB, the latter displays a very versatile energy metabolism. In OHR, reductive dehalogenases (RdhA, RDases) are the terminal reductases and are encoded in a large variety of rdh gene clusters across OHRB. Nevertheless, common features have been recognised: RDases are Tat-dependent redox enzymes, harbour a corrinoid cofactor and two iron-sulphur clusters, and are associated with the cytoplasmic membrane in OHRB [2]. In our laboratory, we focus on the functional characterisation of rdhABCT(K) gene clusters commonly found in D. restrictus and D. hafniense. First, we developed a hybrid protein strategy that will help identifying the targets of new RdhK transcriptional activators [3,4]. The highly conserved pceABCT gene cluster found in D. restrictus strain PER-K23 and D. hafniense strain TCE1, responsible for reductive dehalogenation of tetrachloroethene (PCE), was then investigated for the stoichiometry of its gene products both at RNA and protein levels. While different results were obtained for each of the strains at transcriptional level, quantitative proteomics revealed similar amounts of PceA and PceB proteins in the membrane fraction of both strains, confirming the presence of a membrane-bound reductive dehalogenase complex (RDHC). In contrast, PceC, an FMN-binding membrane protein, was detected at a significantly lower level, likely ruling out its proposed function as direct electron donor to PceA in RDHC [5]. The elucidation of the PceA-containing RDHC is investigated by a combination of mild membrane protein extraction, Clear-Native PAGE and the development of an in-gel RDase assay. So far, a ~180 kDa protein complex could be identified, the composition of which is now under scrutiny. The versatile energy metabolism of D. hafniense was the subject of a comparative physiological and proteomic approach to identify adjustments to the OHR metabolism, highlighting the possible involvement of additional protein key players. [1] Atashgahi, S., Häggblom, M.M., and Smidt, H. (2018) Organohalide respiration in pristine environments: implications for the natural halogen cycle. Environ. Microbiol. 20, 934-948. [2] Fincker, M., and Spormann, A.M. (2017) Biochemistry of catabolic reductive dehalogenation. Annu. Rev. Biochem. 86, 357-386. [3] Willemin, M.S., Vingerhoets, M., Holliger, C., and Maillard, J. (2020) Hybrid transcriptional regulators for the screening of target DNA motifs in organohalide-respiring bacteria. Front. Microbiol. 11, 310. [4] Maillard, J., and Willemin, M.S. (2019) Regulation of organohalide respiration. Adv. Microb. Physiol. 74, 191-238. [5] Buttet, G.F., Willemin, M.S., Hamelin, R., Rupakula, A., and Maillard, J. (2018) The membrane-bound C subunit of reductive dehalogenases: topology analysis and reconstitution of the FMN-binding domain of PceC. Front. Microbiol. 9, 755

Towards understanding the characteristics of new particle formation in the Eastern Mediterranean

To quantify the contribution of new particle formation (NPF) to ultrafine particle number and cloud condensation nuclei (CCN) budgets, one has to understand the mechanisms that govern NPF in different environments and its temporal extent. Here, we study NPF in Cyprus, an Eastern Mediterranean country located at the crossroads of three continents and affected by diverse air masses originating from continental, maritime, and desert-dust source areas. We performed 1-year continuous measurements of aerosol particles down to ∼ 1 nm in diameter for the first time in the Eastern Mediterranean and Middle East (EMME) region. These measurements were complemented with trace gas data, meteorological variables, and retroplume analysis. We show that NPF is a very frequent phenomenon at this site and has higher frequencies of occurrence during spring and autumn. NPF events were both of local and regional origin, and the local events occurred frequently during the month with the lowest NPF frequency. Some NPF events exhibited multiple onsets, while others exhibited apparent particle shrinkage in size. Additionally, NPF events were observed during the nighttime and during episodes of high desert-dust loadings. Particle formation rates and growth rates were comparable to those in urban environments, although our site is a rural one. Meteorological variables and trace gases played a role in explaining the intra-monthly variability of NPF events, but they did not explain why summer months had the least NPF frequency. Similarly, pre-existing aerosol loading did not explain the observed seasonality. The months with the least NPF frequency were associated with higher H2SO4 concentrations but lower NO2 concentrations, which is an indicator of anthropogenic influence. Air masses arriving from the Middle East were not observed during these months, which could suggest that precursor vapors important for nucleation and growth are transported to our site from the Middle East. Further comprehensive measurements of precursor vapors are required to prove this hypothesis

A framework for occupancy detection and tracking using floor-vibration signals

In sensed buildings, information related to occupant movement helps optimize important func-tionalities such as caregiving, energy management, and security enhancement. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. These systems compromise the privacy of building occupants and may affect their behavior. Occupant detection and tracking using floor-vibration measurements that are induced by footsteps is a non-intrusive and inex-pensive sensing method. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. In addition, spurious events such as door closing and falling objects may produce vibrations that are similar to footstep impacts. These events have to be detected and disregarded. Tracking occupants is complicated due to uncertainties associated with walking styles, walking speed, shoe type, health, and mood. Also, spatial variation in structural behavior of floor slabs adds ambiguity to the task of occupant tracking, which cannot be addressed using data-driven strategies alone. In this paper, a frame-work for occupant detection and tracking is developed. Occupant detection is carried out based on signal information. This method outperforms existing threshold-based methods. Support- vector-machine classifiers, trained with time and frequency-domain features, successfully distinguish footsteps from spurious events and determine the number of occupants walking simultaneously. A model-based data-interpretation approach is used for occupant tracking. Structural-mechanics models are used to identify a population of possible occupant locations and trajectories. Up to two occupants can be tracked by accommodating systematic bias and un-certainties from sources such as modeling assumptions and variability in walking gaits. A hybrid framework for occupant detection and tracking that combines model-free approaches for occu-pancy detection with structural behavior models for tracking is developed and tested on two full- scale case studies. These studies successfully validate the utility of the framework for buildings having sparse sensor configurations that measure floor vibrations

Infrared nanoplasmonic metasurfaces augmented by artificial intelligence for universal biosensing

Nanoplasmonic metasurfaces have shown outstanding light-matter interaction enhancement capabilities, leading to their emergence as powerful platforms for highly sensitive biospectroscopy. Metasurface-enhanced biospectroscopy offers unprecedented opportunities for biological studies, and its full potential remains to be unleashed. Mid-IR metasurfaces, in particular, are very promising because they can act as amplifiers of fingerprint-like molecule vibrations, which are plentiful in this rich spectral range. In this thesis, we develop novel nanoplasmonic designs coupled with custom microfluidics and artificial intelligence-based data analysis models to demonstrate real-time, label-free, chemically-specific, and non-destructive monitoring of biomolecules and their interactions in aqueous media. Our first nanoplasmonic design combines optimized grating order-coupled nanoantenna arrays with protein-accessible nanogaps to enable the high sensitivity monitoring of proteins and their three-dimensional structures in aqueous media. The engineered nanoantennas reach electric field intensity enhancements of up to five orders of magnitude and provide chemically specific detection of proteins and their secondary structures down to picograms and nanograms per milliliter, respectively. In the next part of the thesis, we develop multiresonant metasurfaces to monitor interactions between biomolecules with vibrational fingerprints in different parts of the mid-IR range. Our first effort focuses on developing a nanoplasmonic design for simultaneous monitoring of both proteins and lipid molecules. Lipids are another important class of biomolecules as they are the building blocks of biological membranes, and lipid-protein interactions are at the core of many cellular processes. New analytical tools for their study in water and at the monolayer level are of fundamental importance. Therefore, we introduce a dual-resonant nanoplasmonic design coupled to machine learning-based data analysis to overcome current sensor challenges. We apply our technology to a dynamic system involving synaptic vesicle mimics and demonstrate that we can resolve complex mass-preserving biological interactions in real-time. This is a remarkable feat that traditional non-chemically specific analytical measurement tools such as surface plasmon resonance or quartz crystal microbalance spectroscopy could not achieve. In the final part of the thesis, we develop yet another multiresonant design for broadband coverage of the whole mid-IR range. We couple our sensor to a deep learning model to resolve a dynamic biological system including all major classes of biomolecules simultaneously. Specifically, we resolve the toxic peptide-induced release of carbohydrates and nucleotides from exosome-like bionanoparticles

The functional characterization of callosal connections

The brain operates through the synaptic interaction of distant neurons within flexible, often heterogeneous, distributed systems. Histological studies have detailed the connections between distant neurons, but their functional characterization deserves further exploration. Studies performed on the corpus callosum in animals and humans are unique in that they capitalize on results obtained from several neuroscience disciplines. Such data inspire a new interpretation of the function of callosal connections and delineate a novel road map, thus paving the way toward a general theory of cortico-cortical connectivity. Here we suggest that callosal axons can drive their post-synaptic targets preferentially when coupled to other inputs endowing the cortical network with a high degree of conditionality. This might depend on several factors, such as their pattern of convergence-divergence, the excitatory and inhibitory operation mode, the range of conduction velocities, the variety of homotopic and heterotopic projections and, finally, the state-dependency of their firing. We propose that, in addition to direct stimulation of post-synaptic targets, callosal axons often play a conditional driving or modulatory role, which depends on task contingencies, as documented by several recent studies

A review in snow saltation dynamics and its implications for the surface mass balance

Snow covered regions are frequently subjected to strong winds. This leads to the erosion of the snow surface and the occurrence of drifting and blowing snow events. To correctly predict and model these phenomena is of the utmost importance to assess snowpack stability and avalanche formation, as well as airborne snow sublimation and the resultant surface mass balance. Nonetheless, snow transport is frequently neglected or misrepresented in regional and mesoscale models. One of the main challenges is the accurate representation of snow transport close to the ground, where snow particles are transported by a process called saltation. This shallow layer comprises most of the horizontal mass flux and sets the lower boundary condition to model snow suspension clouds. A detailed study of snow saltation dynamics has been conducted using a Large-Eddy-Simulation flow solver coupled with a Lagrangian model for particle trajectories. The effect of particle size distribution and interparticle cohesion on particle speed, mass flux and surface friction velocity has systematically been investigated. The results show that snow cohesion and grain size heterogeneity can significantly increase saltation mass flux, specially at high friction velocities. Moreover, the agreement between simulation results and the saltation models typically used in large scale atmospheric models is highly dependent on the assumed bed characteristics. These findings can support the development of comprehensive saltation models that specifically take into account snow bed properties. These new saltation models can be implemented in mesoscale atmospheric models and have the potential to significantly improve surface mass balance predictions if grain-scale snow surface properties are available. This is possible in the newly developed CRYOWRF model which expands WRF’s surface modeling suite by including the advanced complexity, grain-scale snow model, SNOWPACK, along with a blowing snow scheme

Spaces

Public space manifests itself as the physical fundament of public life in European democracies. This chapter elaborates on the culture of public space and how it characterises European design. As Europe diverges in political cultures and histories, economies and mentalities, climate and natural conditions, the culture of public space plays out differently at different locations. Landscape architects have adapted public space design repeatedly to the changing circumstances in Europe while building enormous expertise. The projects selected show the ‘quantity of quality’ realised all over the continent, in bigger and smaller cities, for various purposes of public life, in different formal expressions, in single events or according to overarching strategies