Instrument Bias Correction With Machine Learning Algorithms: Application to Field-Portable Mass Spectrometry

In situ sensors for environmental chemistry promise more thorough observations, which are necessary for high confidence predictions in earth systems science. However, these can be a challenge to interpret because the sensors are strongly influenced by temperature, humidity, pressure, or other secondary environmental conditions that are not of direct interest. We present a comparison of two statistical learning methods—a generalized additive model and a long short-term memory neural network model for bias correction of in situ sensor data. We discuss their performance and tradeoffs when the two bias correction methods are applied to data from submersible and shipboard mass spectrometers. Both instruments measure the most abundant gases dissolved in water and can be used to reconstruct biochemical metabolisms, including those that regulate atmospheric carbon dioxide. Both models demonstrate a high degree of skill at correcting for instrument bias using correlated environmental measurements; the difference in their respective performance is less than 1% in terms of root mean squared error. Overall, the long short-term memory bias correction produced an error of 5% for O2 and 8.5% for CO2 when compared against independent membrane DO and laser spectrometer instruments. This represents a predictive accuracy of 92–95% for both gases. It is apparent that the most important factor in a skillful bias correction is the measurement of the secondary environmental conditions that are likely to correlate with the instrument bias. These statistical learning methods are extremely flexible and permit the inclusion of nearly an infinite number of correlates in finding the best bias correction solution

Robust Multi-Image HDR Reconstruction for the Modulo Camera

Photographing scenes with high dynamic range (HDR) poses great challenges to consumer cameras with their limited sensor bit depth. To address this, Zhao et al. recently proposed a novel sensor concept - the modulo camera - which captures the least significant bits of the recorded scene instead of going into saturation. Similar to conventional pipelines, HDR images can be reconstructed from multiple exposures, but significantly fewer images are needed than with a typical saturating sensor. While the concept is appealing, we show that the original reconstruction approach assumes noise-free measurements and quickly breaks down otherwise. To address this, we propose a novel reconstruction algorithm that is robust to image noise and produces significantly fewer artifacts. We theoretically analyze correctness as well as limitations, and show that our approach significantly outperforms the baseline on real data.Comment: to appear at the 39th German Conference on Pattern Recognition (GCPR) 201

myGRN: a database and visualisation system for the storage and analysis of developmental genetic regulatory networks

<p>Abstract</p> <p>Background</p> <p>Biological processes are regulated by complex interactions between transcription factors and signalling molecules, collectively described as Genetic Regulatory Networks (GRNs). The characterisation of these networks to reveal regulatory mechanisms is a long-term goal of many laboratories. However compiling, visualising and interacting with such networks is non-trivial. Current tools and databases typically focus on GRNs within simple, single celled organisms. However, data is available within the literature describing regulatory interactions in multi-cellular organisms, although not in any systematic form. This is particularly true within the field of developmental biology, where regulatory interactions should also be tagged with information about the time and anatomical location of development in which they occur.</p> <p>Description</p> <p>We have developed myGRN (<url>http://www.myGRN.org</url>), a web application for storing and interrogating interaction data, with an emphasis on developmental processes. Users can submit interaction and gene expression data, either curated from published sources or derived from their own unpublished data. All interactions associated with publications are publicly visible, and unpublished interactions can only be shared between collaborating labs prior to publication. Users can group interactions into discrete networks based on specific biological processes. Various filters allow dynamic production of network diagrams based on a range of information including tissue location, developmental stage or basic topology. Individual networks can be viewed using myGRV, a tool focused on displaying developmental networks, or exported in a range of formats compatible with third party tools. Networks can also be analysed for the presence of common network motifs. We demonstrate the capabilities of myGRN using a network of zebrafish interactions integrated with expression data from the zebrafish database, ZFIN.</p> <p>Conclusion</p> <p>Here we are launching myGRN as a community-based repository for interaction networks, with a specific focus on developmental networks. We plan to extend its functionality, as well as use it to study networks involved in embryonic development in the future.</p

Switch-independent task representations in frontal and parietal cortex

Alternating between two tasks is effortful and impairs performance. Previous fMRI studies have found increased activity in frontoparietal cortex when task switching is required. One possibility is that the additional control demands for switch trials are met by strengthening task representations in the human brain. Alternatively, on switch trials, the residual representation of the previous task might impede the buildup of a neural task representation. This would predict weaker task representations on switch trials, thus also explaining the performance costs. To test this, male and female participants were cued to perform one of two similar tasks, with the task being repeated or switched between successive trials. Multivoxel pattern analysis was used to test which regions encode the tasks and whether this encoding differs between switch and repeat trials. As expected, we found information about task representations in frontal and parietal cortex, but there was no difference in the decoding accuracy of task-related information between switch and repeat trials. Using cross-classification, we found that the frontoparietal cortex encodes tasks using a generalizable spatial pattern in switch and repeat trials. Therefore, task representations in frontal and parietal cortex are largely switch independent. We found no evidence that neural information about task representations in these regions can explain behavioral costs usually associated with task switching

Consumers’ reactions to nutrition and ingredient labelling for wine – A cross-country discrete choice experiment

The purpose of this study is to examine consumers' reactions to the introduction of nutrition and ingredient labelling for wine, a product that is so far still exempt from mandatory nutrition and ingredient labelling. It also analyses the effect of positive and negative information about the use of ingredients in wine on consumers' choice. Representative samples for wine consumers from three distinctly different countries representing old and new wine markets (Australia, n = 745; Germany, n = 716; Italy, n = 715) completed a discrete choice experiment (DCE) with graphically simulated wine back labels. For each country, respondents were randomly allocated to a reference group and two different treatment conditions where they received newspaper-like information (positive, negative) before making choices. Results for the reference condition show that consumers across all three countries have a significant positive utility for detailed nutrition information. Instead, ingredient information only receives a positive utility in Italy, whereas German and Australian respondents do not receive utility from ingredient labelling. When consumers in the treatment group are confronted with negative media information the attribute importance of ingredients significantly increases across all three countries, clean labelled products without ingredients are preferred, and a significantly higher share of consumers in Germany and Italy prefer not to buy any wine. The treatment effect of positive media information on consumers’ wine choice is lower than that of negative information. The results of the study have implications for the pending new regulation of wine labelling and for communication strategies of the wine industry that should actively inform consumers about the necessity of ingredients in wine production

Coarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models

Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits, the most significant of which is accuracy. Neural networks can inherently incorporate multi-body effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields even when accounting for specialized hardware which accelerates the training and integration of such networks. The second, and the focus of this paper, is the need for the considerable amount of data needed to train such force fields. It is common to use tens of microseconds of molecular dynamics data to train a single CG model, which approaches the point of eliminating the CG models usefulness in the first place. As we investigate in this work, it is apparent that this data-hunger trap from neural networks for predicting molecular energies and forces is caused in large part by the difficulty in learning force equivariance, i.e., the fact that force vectors should rotate while maintaining their magnitude in response to an equivalent rotation of the system. We demonstrate that for CG water, networks that inherently incorporate this equivariance into their embedding can produce functional models using datasets as small as a single frame of reference data, which networks without inherent symmetry equivariance cannot

Pairwise dwarf galaxy formation and galaxy downsizing: some clues from extremely metal-poor Blue Compact Dwarf galaxies

Some of the extremely metal-poor Blue Compact Dwarf galaxies (XBCDs) in the nearby universe form galaxy pairs with remarkably similar properties. This fact points to an intriguing degree of synchronicity in the formation history of these binary dwarf galaxies and raises the question as to whether some of them form and co-evolve pairwise (or in loose galaxy groups), experiencing recurrent mild interactions and minor tidally induced star formation episodes throughout their evolution. We argue that this hypothesis offers a promising conceptual framework for the exploration of the retarded previous evolution and recent dominant formation phase of XBCDs.Comment: To appear in the proceedings of the JENAM 2010 Symposium "Dwarf Galaxies: Keys to Galaxy Formation and Evolution" (Lisbon, 9-10 September 2010), P. Papaderos, S. Recchi, G. Hensler (eds.), Springer Verlag (2011), in pres

Cardiolipin-containing lipid membranes attract the bacterial cell division protein diviva

DivIVA is a protein initially identified as a spatial regulator of cell division in the model organism Bacillus subtilis, but its homologues are present in many other Gram-positive bacteria, including Clostridia species. Besides its role as topological regulator of the Min system during bacterial cell division, DivIVA is involved in chromosome segregation during sporulation, genetic competence, and cell wall synthesis. DivIVA localizes to regions of high membrane curvature, such as the cell poles and cell division site, where it recruits distinct binding partners. Previously, it was suggested that negative curvature sensing is the main mechanism by which DivIVA binds to these specific regions. Here, we show that Clostridioides difficile DivIVA binds preferably to membranes containing negatively charged phospholipids, especially cardiolipin. Strikingly, we observed that upon binding, DivIVA modifies the lipid distribution and induces changes to lipid bilayers containing cardiolipin. Our observations indicate that DivIVA might play a more complex and so far unknown active role during the formation of the cell division septal membrane