Towards the spatial resolution of metalloprotein charge states by detailed modeling of XFEL crystallographic diffraction.

Oxidation states of individual metal atoms within a metalloprotein can be assigned by examining X-ray absorption edges, which shift to higher energy for progressively more positive valence numbers. Indeed, X-ray crystallography is well suited for such a measurement, owing to its ability to spatially resolve the scattering contributions of individual metal atoms that have distinct electronic environments contributing to protein function. However, as the magnitude of the shift is quite small, about +2 eV per valence state for iron, it has only been possible to measure the effect when performed with monochromated X-ray sources at synchrotron facilities with energy resolutions in the range 2-3 × 10-4 (ΔE/E). This paper tests whether X-ray free-electron laser (XFEL) pulses, which have a broader bandpass (ΔE/E = 3 × 10-3) when used without a monochromator, might also be useful for such studies. The program nanoBragg is used to simulate serial femtosecond crystallography (SFX) diffraction images with sufficient granularity to model the XFEL spectrum, the crystal mosaicity and the wavelength-dependent anomalous scattering factors contributed by two differently charged iron centers in the 110-amino-acid protein, ferredoxin. Bayesian methods are then used to deduce, from the simulated data, the most likely X-ray absorption curves for each metal atom in the protein, which agree well with the curves chosen for the simulation. The data analysis relies critically on the ability to measure the incident spectrum for each pulse, and also on the nanoBragg simulator to predict the size, shape and intensity profile of Bragg spots based on an underlying physical model that includes the absorption curves, which are then modified to produce the best agreement with the simulated data. This inference methodology potentially enables the use of SFX diffraction for the study of metalloenzyme mechanisms and, in general, offers a more detailed approach to Bragg spot data reduction

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Everything, Everywhere All in One Evaluation: Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. When designed well, these systems promise more objective decisions while saving large amounts of resources and freeing up human time. However, when ADM systems are not designed well, they can lead to unfair decisions which discriminate against societal groups. The downstream effects of ADMs critically depend on the decisions made during the systems' design and implementation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these design decisions are made implicitly, without knowing exactly how they will influence the final system. It is therefore important to make explicit the decisions made during the design of ADM systems and understand how these decisions affect the fairness of the resulting system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit design decisions into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible "universes" of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand variability and robustness of algorithmic fairness using an exemplary case study of predicting public health coverage of vulnerable populations for potential interventions. Our results illustrate how decisions during the design of a machine learning system can have surprising effects on its fairness and how to detect these effects using multiverse analysis

Outdoor performance of a motion-sensitive neuron in the blowfly

Egelhaaf M, Grewe J, Kern R, Warzecha A-K. Outdoor performance of a motion-sensitive neuron in the blowfly. Vision research. 2001;41(27):3627-3637.We studied an identified motion-sensitive neuron of the blowfly under outdoor conditions. The neuron was stimulated by oscillating the fly in a rural environment. We analysed whether the motion-induced neuronal activity is affected by brightness changes ranging between bright sunlight and dusk, In addition, the relationship between spike rate and ambient temperature was determined. The main results are: (1) The mean spike rate elicited by visual motion is largely independent of brightness changes over several orders of magnitude as they occur as a consequence of positional changes of the sun. Even during dusk the neuron responds strongly and directionally selective to motion. (2) The neuronal spike rate is not significantly affected by short-term brightness changes caused by clouds temporarily occluding the sun. (3) In contrast, the neuronal activity is much affected by changes in ambient temperature. (C) 2001 Elsevier Science Ltd. All rights reserved

Strength training in elderly people improves static balance

Aim of this study was to investigate the effects of two different types of strength training programs on static balance in elderly subjects. Subjects older than 65 years of age were enrolled and assigned to control group (CG, n =19), electrical stimulation group (ES, n = 27) or leg press group (LP, n = 28). Subjects in both the training groups were exposed to training (2-3x/week) for a period of 9 weeks. In the ES group the subjects received neuromuscular electrical stimulation of the anterior thigh muscles. In the LP group the subjects performed strength training on a computer-controlled leg press machine. Before and after the training period, static balance of the subject was tested using a quiet stance task. Average velocity, amplitude and frequency of the center-of-pressure (CoP) were calculated from the acquired force plate signal. The data was statistically tested with analysis of (co)variance and t-tests. The three groups of subjects showed statistically significant differences (p < 0.05) regarding the pre-training vs. post-training changes in CoP velocity, amplitude and frequency. The differences were more pronounced for CoP velocity and amplitude, while they were less evident in case of mean frequency. The mean improvements were higher in the LP group than in the ES group. Our results provide supportive evidence to the existence of the strength-balance relationship. Additionally, results indicate the role of recruiting central processes and activation of functional kinetic chains for the better end effec

Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Similar to the basic structure of a brain, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.Comment: 83 pages, 22 figures, 9 tables, 100 reference