How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn-Classical Statistics versus Machine Learning

Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days

Supervised Machine Learning to Assess Methane Emissions of a Dairy Building with Natural Ventilation

A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach

Static Disorder in Excitation Energies of the Fenna-Matthews-Olson Protein: Structure-Based Theory Meets Experiment

Inhomogeneous broadening of optical lines of the Fenna-Matthews-Olson (FMO) light-harvesting protein is investigated by combining a Monte Carlo sampling of low-energy conformational substates of the protein with a quantum chemical/electrostatic calculation of local transition energies (site energies) of the pigments. The good agreement between the optical spectra calculated for the inhomogeneous ensemble and the experimental data demonstrates that electrostatics is the dominant contributor to static disorder in site energies. Rotamers of polar amino acid side chains are found to cause bimodal distribution functions of site energy shifts, which can be probed by hole burning and single-molecule spectroscopy. When summing over the large number of contributions, the resulting distribution functions of the site energies become Gaussians, and the correlations in site energy fluctuations at different sites practically average to zero. These results demonstrate that static disorder in the FMO protein is in the realm of the central limit theorem of statistics. © 2020 American Chemical Society

Theorie des Anregungsenergie-Transfers in Pigment-Protein Komplexen

1\. Title and Index 1 2\. Introduction 5 3\. Theory and Parameter 15 4\. Results (FMO and PSI) 57 5\. Summary (English and German) 95 6\. Bibliography and Publications 103Since the first high resolution X-ray crystal structure of a pigment-protein- complex (FMO-protein) appeared, there have been numerous approaches to elucidate the structure-function-relationships of pigment-protein-complexes by spectroscopic methods, theory and simulations. In the present work, optical spectra of photosynthetic pigment-protein-complexes have been calculated structure-based with the aid of non-Markovian dynamic theories. The focus was set on a direct structure based calculation of the parameters of the pigment- protein Hamiltonian, used in the simulation of the spectra. A method for structure based calculations of local transition energies of pigments in their protein surrounding (the so-called site energies) has been developed and tested independently by means of a genetic algorithm. Furthermore, a quantum- chemical/ electrostatic method was developed for the couplings between the optical transitions of the pigments (the so-called excitonic couplings), considering the electronic polarizability of the protein explicitly for the first time. With this method the effective transition dipole moment of bacteriochlorophyll a in the FMO-protein was directly (i.e. not as a fit parameter) obtained. By means of molecular dynamics simulations and the structure based method for site energy calculations, the spectral density can be calculated by a time-domain correlation function of the site energies. First results are shown in the present work and compared to the spectral density extracted from fluorescence-line-narrowing-spectra. With these quantities it is now possible to calculate optical spectra structure based, practically without fit parameters, and to draw conclusions on the structure- function-relationships. From the pigment transition energies of the FMO- complex, together with exciton-relaxation-calculations, conclusions could be drawn about the orientation of the FMO-complex relative to the reaction center. For one additional pigment-protein-complex, namely photosystem I, also excitonic couplings and transition energies were calculated. The spectra calculated with these parameters, are more similar to the experiment than all previously published results.Seit der Aufklärung der ersten hochaufgelösten Röntgen-Kristallstruktur eines Pigment-Protein-Komplexes (FMO-Protein) vor mehr als 30 Jahren, wird versucht mit Hilfe von spektroskopischen Methoden, Theorie und Simulation, die Struktur-Funktions-Beziehung von Pigment-Protein-Komplexen aufzuklären. In der vorliegenden Arbeit wurden optische Spektren photosynthetischer Pigment- Protein-Komplexe strukturbasiert mit Hilfe dynamischer nicht-Markov-Theorie berechnet. Dabei lag der Schwerpunkt auf einer direkten strukturbasierten Berechnung der Parameter des Pigment-Protein-Hamiltonoperators, auf dem die Berechnung der Spektren beruht. Es wurde eine Methode zur strukturbasierten Berechnung von lokalen Übergangsenergien von Pigmenten in ihrer Proteinumgebung entwickelt und mit Hilfe eines genetischen Algorithmus getestet. Darüber hinaus wurde eine quantenchemische/elektrostatische Methode zur Berechnung von Kopplungen zwischen den optischen Übergängen der Pigmente (die sogenannten exzitonischen Kopplungen) entwickelt, in der erstmals die elektronische Polarisierbarkeit des Proteins explizit berücksichtigt wird. Damit gelang es, das effektive Übergangsdipolmoment von Bakteriochlorophyll a im FMO-Protein direkt (also nicht als Fit-Parameter) zu bestimmen. Mit Hilfe von Molekül-Dynamik-Simulationen und der strukturbasierten Methode zur Berechnung von Übergangsenergien kann über eine zeitliche Korrelationsfunktion der Übergangsenergien die Spektraldichte berechnet werden. Erste Ergebnisse werden in der vorliegenden Arbeit präsentiert und mit der Spektraldichte aus fluorescence-line-narrowing-Spektren verglichen. Mit diesen Größen ist es nun möglich, optische Spektren praktisch ohne Fit-Parameter nur auf der Röntgen- Struktur basierend, zu berechnen, um daraus Rückschlüsse auf Struktur- Funktions-Beziehungen ziehen zu können. Aus den Übergangsenergien der Pigmente des FMO-Komplexes konnte, zusammen mit Exzitonen-Relaxations-Rechnungen, auf die Orientierung des FMO-Komplexes relativ zum Reaktionszentrum, geschlossen werden. Für einen weiteren Pigment-Protein-Komplex, das Photosystem I, konnten ebenfalls exzitonische Kopplungen und Übergangsenergien berechnet werden. Die mit diesen Parametern berechneten optischen Spektren sehen dem Experiment ähnlicher, als alle bisher veröffentlichten Ergebnisse

Hole-Burning Spectroscopy on Excitonically Coupled Pigments in Proteins: Theory Meets Experiment

A theory for the calculation of resonant and nonresonant hole-burning (HB) spectra of pigment–protein complexes is presented and applied to the water-soluble chlorophyll-binding protein (WSCP) from cauliflower. The theory is based on a non-Markovian line shape theory (Renger and Marcus J. Chem. Phys. 2002, 116, 9997) and includes exciton delocalization, vibrational sidebands, and lifetime broadening. An earlier approach by Reppert (J. Phys. Chem. Lett. 2011, 2, 2716) is found to describe nonresonant HB spectra only. Here we present a theory that can be used for a quantitative description of HB data for both nonresonant and resonant burning conditions. We find that it is important to take into account the excess energy of the excitation in the HB process. Whereas excitation of the zero-phonon transition of the lowest exciton state, that is, resonant burning allows the protein to access only its conformational substates in the neighborhood of the preburn state, any higher excitation gives the protein full access to all conformations present in the original inhomogeneous ensemble. Application of the theory to recombinant WSCP from cauliflower, reconstituted with chlorophyll <i>a</i> or chlorophyll <i>b</i>, gives excellent agreement with experimental data by Pieper et al. (J. Phys. Chem. B 2011, 115, 4053) and allows us to obtain an upper bound of the lifetime of the upper exciton state directly from the HB experiments in agreement with lifetimes measured recently in time domain 2D experiments by Alster et al. (J. Phys. Chem. B 2014, 118, 3524)

Neural signatures of social inferences predict the number of real-life social contacts and autism severity

Abstract We regularly infer other people’s thoughts and feelings from observing their actions, but how this ability contributes to successful social behavior and interactions remains unknown. We show that neural activation patterns during social inferences obtained in the laboratory predict the number of social contacts in the real world, as measured by the social network index, in three neurotypical samples (total n = 126) and one sample of autistic adults (n = 23). We also show that brain patterns during social inference generalize across individuals in these groups. Cross-validated associations between brain activations and social inference localize selectively to the right posterior superior temporal sulcus and were specific for social, but not nonsocial, inference. Activation within this same brain region also predicts autism-like trait scores from questionnaires and autism symptom severity. Thus, neural activations produced while thinking about other people’s mental states predict variance in multiple indices of social functioning in the real world

Toxicokinetics of Seven Perfluoroalkyl Sulfonic and Carboxylic Acids in Pigs Fed a Contaminated Diet

The transfer of a mixture of perfluoroalkyl acids (PFAAs) from contaminated feed into the edible tissues of 24 fattening pigs was investigated. Four perfluoroalkyl sulfonic (PFSAs) and three perfluoroalkyl carboxylic acids (PFCAs) were quantifiable in feed, plasma, edible tissues, and urine. As percentages of unexcreted PFAA, the substances accumulated in plasma (up to 51%), fat, and muscle tissues (collectively, meat 40–49%), liver (under 7%), and kidney (under 2%) for most substances. An exception was perfluorooctanesulfonic acid (PFOS), with lower affinity for plasma (23%) and higher for liver (35%). A toxicokinetic model is developed to quantify the absorption, distribution, and excretion of PFAAs and to calculate elimination half-lives. Perfluorohexanoic acid (PFHxA), a PFCA, had the shortest half-life at 4.1 days. PFSAs are eliminated more slowly (e.g., half-life of 634 days for PFOS). PFAAs in pigs exhibit longer elimination half-lives than in most organisms reported in the literature, but still shorter than in humans