Now showing items 21-40 of 3506

• #### Proteomic responses to ocean acidification in the brain of juvenile coral reef fish

(Cold Spring Harbor Laboratory, 2020-05-28) [Preprint]
AbstractElevated CO2 levels predicted to occur by the end of the century can affect the physiology and behaviour of marine fishes. For one important survival mechanism, the response to chemical alarm cues from conspecifics, substantial among-individual variation in the extent of behavioural impairment when exposed to elevated CO2 has been observed in previous studies. Whole brain transcriptomic data has further emphasized the importance of parental phenotypic variation in the response of juvenile fish to elevated CO2. In this study, we investigate the genome-wide proteomic responses of this variation in the brain of 5-week old spiny damselfish, Acanthochromis polyacanthus. We compared the expression of proteins in the brains of juvenile A. polyacanthus from two different parental behavioural phenotypes (sensitive and tolerant) that had been experimentally exposed to short-term, long-term and inter-generational elevated CO2. Our results show differential expression of key proteins related to stress response and epigenetic markers with elevated CO2 exposure. Proteins related to neurological development were also differentially expressed particularly in the long-term developmental treatment, which might be critical for juvenile development. By contrast, exposure to elevated CO2 in the parental generation resulted in only three differentially expressed proteins in the offspring, revealing potential for inter-generational acclimation. Lastly, we found a distinct proteomic pattern in juveniles due to the behavioural sensitivity of parents to elevated CO2, even though the behaviour of the juvenile fish was impaired regardless of parental phenotype. Our data shows that developing juveniles are affected in their brain protein expression by elevated CO2, but the effect varies with the length of exposure as well as due to variation of parental phenotypes in the population.
• #### Assessing variable activity for Bayesian regression trees

(arXiv, 2020-05-27) [Preprint]
Bayesian Additive Regression Trees (BART) are non-parametric models that can capture complex exogenous variable effects. In any regression problem, it is often of interest to learn which variables are most active. Variable activity in BART is usually measured by counting the number of times a tree splits for each variable. Such one-way counts have the advantage of fast computations. Despite their convenience, one-way counts have several issues. They are statistically unjustified, cannot distinguish between main effects and interaction effects, and become inflated when measuring interaction effects. An alternative method well-established in the literature is Sobol' indices, a variance-based global sensitivity analysis technique. However, these indices often require Monte Carlo integration, which can be computationally expensive. This paper provides analytic expressions for Sobol' indices for BART predictors. These expressions are easy to interpret and are computationally feasible. Furthermore, we will show a fascinating connection between main-effects Sobol' indices and one-way counts. We also introduce a novel ranking method, and use this to demonstrate that the proposed indices preserve the Sobol'-based rank order of variable importance. Finally, we compare these methods using analytic test functions and the En-ROADS climate impacts simulator.
• #### Water and Metal–Organic Frameworks: From Interaction toward Utilization

(Chemical Reviews, American Chemical Society (ACS), 2020-05-16) [Article]
The steep stepwise uptake of water vapor and easy release at low relative pressures and moderate temperatures together with high working capacities make metal−organic frameworks (MOFs) attractive, promising materials for energy efficient applications in adsorption devices for humidity control (evaporation and condensation processes) and heat reallocation (heating and cooling) by utilizing water as benign sorptive and low-grade renewable or waste heat. Emerging MOF-based process applications covered are desiccation, heat pumps/chillers, water harvesting, air conditioning, and desalination. Governing parameters of the intrinsic sorption properties and stability under humid conditions and cyclic operation are identified. Transport of mass and heat in MOF structures, at least as important, is still an underexposed topic. Essential engineering elements of operation and implementation are presented. An update on stability of MOFs in water vapor and liquid systems is provided, and a suite of 18 MOFs are identified for selective use in heat pumps and chillers, while several can be used for air conditioning, water harvesting, and desalination. Most applications with MOFs are still in an exploratory state. An outlook is given for further R&D to realize these applications, providing essential kinetic parameters, performing smart engineering in the design of systems, and conceptual process designs to benchmark them against existing technologies. A concerted effort bridging chemistry, materials science, and engineering is required.
• #### Landscape of the non-coding transcriptome response of two Arabidopsis ecotypes to phosphate starvation

(Plant Physiology, American Society of Plant Biologists (ASPB), 2020-05-13) [Article]
Root architecture varies widely between species, and even between ecotypes of the same species, despite the strong conservation of the coding portion of their genomes. By contrast, non-coding RNAs evolve rapidly between ecotypes and may control their differential responses to the environment, since several long non-coding RNAs (lncRNAs) are known to quantitatively regulate gene expression. Roots from Columbia (Col) and Landsberg erecta (Ler) ecotypes respond differently to phosphate starvation. Here, we compared transcriptomes (mRNAs, lncRNAs, and small RNAs) of root tips from these two ecotypes during early phosphate starvation. We identified thousands of lncRNAs that were largely conserved at the DNA level in these ecotypes. In contrast to coding genes, many lncRNAs were specifically transcribed in one ecotype and/or differentially expressed between ecotypes independent of phosphate availability. We further characterized these ecotype-related lncRNAs and studied their link with siRNAs. Our analysis identified 675 lncRNAs differentially expressed between the two ecotypes, including antisense RNAs targeting key regulators of root-growth responses. Mis-regulation of several intergenic lncRNAs showed that at least two ecotype-related lncRNAs regulate primary root growth in Col. RNA-seq analysis following the deregulation of the lncRNA NPC48 revealed a potential link with root growth and transport functions. This exploration of the non-coding transcriptome identified ecotype-specific lncRNAs-mediated regulation in root apexes. The non-coding genome may harbor further mechanisms involved in ecotype adaptation of roots to different soil environments.
• #### High-purity orbital angular momentum states from a visible metasurface laser

(Nature Photonics, Springer Science and Business Media LLC, 2020-04-27) [Article]
Orbital angular momentum (OAM) from lasers holds promise for compact, at-source solutions for applications ranging from imaging to communications. However, conjugate symmetry between circular spin and opposite helicity OAM states (±ℓ) from conventional spin–orbit approaches has meant that complete control of light’s angular momentum from lasers has remained elusive. Here, we report a metasurface-enhanced laser that overcomes this limitation. We demonstrate new high-purity OAM states with quantum numbers reaching ℓ= 100 and non-symmetric vector vortex beams that lase simultaneously on independent OAM states as much as Δℓ= 90 apart, an extreme violation of previous symmetric spin–orbit lasing devices. Our laser conveniently outputs in the visible, producing new OAM states of light as well as all previously reported OAM modes from lasers, offering a compact and power-scalable source that harnesses intracavity structured matter for the creation of arbitrary chiral states of structured light.
• #### Stabilisation of dianion dimers trapped inside cyanostar macrocycles

(Physical Chemistry Chemical Physics, Royal Society of Chemistry (RSC), 2020-04-21) [Article]
<p>Interanionic H-bonds (IAHBs) are unfavourable interactions in the gas phase becoming favoured when anions are in solution. Dianion dimers are also susceptible to be trapped inside the cavities of cyanostar...</p>
• #### SportsXR -- Immersive Analytics in Sports

(arXiv, 2020-04-17) [Preprint]
We present our initial investigation of key challenges and potentials of immersive analytics (IA) in sports, which we call SportsXR. Sports are usually highly dynamic and collaborative by nature, which makes real-time decision making ubiquitous. However, there is limited support for athletes and coaches to make informed and clear-sighted decisions in real-time. SportsXR aims to support situational awareness for better and more agile decision making in sports. In this paper, we identify key challenges in SportsXR, including data collection, in-game decision making, situated sport-specific visualization design, and collaborating with domain experts. We then present potential user scenarios in training, coaching, and fan experiences. This position paper aims to inform and inspire future SportsXR research.
• #### Forecasting Multi-Dimensional Processes over Graphs

(arXiv, 2020-04-17) [Preprint]
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and propose new methodologies based on the graph vector autoregressive model. More explicitly, we leverage product graphs to model the high-dimensional graph data and develop multi-dimensional graph-based vector autoregressive models to forecast future trends with a number of parameters that is independent of the number of time series and a linear computational complexity. Numerical results demonstrating the prediction of moving point clouds corroborate our findings.
• #### Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis

(Genetics in Medicine, Springer Science and Business Media LLC, 2020-04-15) [Article]
The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease.
• #### A comparative analysis of drinking water employing metagenomics

(PLOS ONE, Public Library of Science (PLoS), 2020-04-09) [Article]
The microbiological content of drinking water traditionally is determined by employing culture- dependent methods that are unable to detect all microorganisms, especially those that are not culturable. High-throughput sequencing now makes it possible to determine the microbiome of drinking water. Thus, the natural microbiota of water and water distribution systems can now be determined more accurately and analyzed in significantly greater detail, providing comprehensive understanding of the microbial community of drinking water applicable to public health. In this study, shotgun metagenomic analysis was performed to determine the microbiological content of drinking water and to provide a preliminary assessment of tap, drinking fountain, sparkling natural mineral, and non-mineral bottled water. Predominant bacterial species detected were members of the phyla Actinobacteria and Proteobacteria, notably the genera Alishewanella, Salmonella, and Propionibacterium in non-carbonated non-mineral bottled water, Methyloversatilis and Methylibium in sparkling natural mineral water, and Mycobacterium and Afipia in tap and drinking fountain water. Fecal indicator bacteria, i.e., Escherichia coli or enterococci, were not detected in any samples examined in this study. Bacteriophages and DNA encoding a few virulence-associated factors were detected but determined to be present only at low abundance. Antibiotic resistance markers were detected only at abundance values below our threshold of confidence. DNA of opportunistic plant and animal pathogens was identified in some samples and these included bacteria (Mycobacterium spp.), protozoa (Acanthamoeba mauritaniensis and Acanthamoeba palestinensis), and fungi (Melampsora pinitorqua and Chryosporium queenslandicum). Archaeal DNA (Candidatus Nitrosoarchaeum) was detected only in sparkling natural mineral water. This preliminary study reports the complete microbiome (bacteria, viruses, fungi, and protists) of selected types of drinking water employing whole-genome high-throughput sequencing and bioinformatics. Investigation into activity and function of the organisms detected is in progress.
• #### Forecasting Multi-Dimensional Processes Over Graphs

(IEEE, 2020-04-09) [Conference Paper]
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and propose new methodologies based on the graph vector autoregressive model. More explicitly, we leverage product graphs to model the high-dimensional graph data and develop multidimensional graph-based vector autoregressive models to forecast future trends with a number of parameters that is independent of the number of time series and a linear computational complexity. Numerical results demonstrating the prediction of moving point clouds corroborate our findings.
• #### Event Based, Near Eye Gaze Tracking Beyond 10,000Hz

(arXiv, 2020-04-07) [Preprint]
Fast and accurate eye tracking is crucial for many applications. Current camera-based eye tracking systems, however, are fundamentally limited by their bandwidth, forcing a tradeoff between image resolution and framerate, i.e. between latency and update rate. Here, we propose a hybrid frame-event-based near-eye gaze tracking system offering update rates beyond 10,000 Hz with an accuracy that matches that of high-end desktop-mounted commercial eye trackers when evaluated in the same conditions. Our system builds on emerging event cameras that simultaneously acquire regularly sampled frames and adaptively sampled events. We develop an online 2D pupil fitting method that updates a parametric model every one or few events. Moreover, we propose a polynomial regressor for estimating the gaze vector from the parametric pupil model in real time. Using the first hybrid frame-event gaze dataset, which will be made public, we demonstrate that our system achieves accuracies of 0.45 degrees -- 1.75 degrees for fields of view ranging from 45 degrees to 98 degrees.
• #### Entropy stabilization and property-preserving limiters for discontinuous Galerkin discretizations of nonlinear hyperbolic equations

(arXiv, 2020-04-07) [Preprint]
The methodology proposed in this paper bridges the gap between entropy stable and positivity-preserving discontinuous Galerkin (DG) methods for nonlinear hyperbolic problems. The entropy stability property and, optionally, preservation of local bounds for the cell averages are enforced using flux limiters based on entropy conditions and discrete maximum principles, respectively. Entropy production by the (limited) gradients of the piecewise-linear DG approximation is constrained using Rusanov-type entropy viscosity, as proposed by Abgrall in the context of nodal finite element approximations. We cast his algebraic entropy fix into a form suitable for arbitrary polynomial bases and, in particular, for modal DG approaches. The Taylor basis representation of the entropy stabilization term reveals that it penalizes the solution gradients in a manner similar to slope limiting and requires semi-implicit treatment to achieve the desired effect. The implicit Taylor basis version of the Rusanov entropy fix preserves the sparsity pattern of the element mass matrix. Hence, no linear systems need to be solved if the Taylor basis is orthogonal and an explicit treatment of the remaining terms is adopted. The optional application of a vertex-based slope limiter constrains the piecewise-linear DG solution to be bounded by local maxima and minima of the cell averages. The combination of entropy stabilization with flux and slope limiting leads to constrained approximations that possess all desired properties. Numerical studies of the new limiting techniques and entropy correction procedures are performed for two scalar two-dimensional test problems with nonlinear and nonconvex flux functions.
• #### Nanoscale Elemental Mapping of Intact Solid-Liquid Interfaces and Reactive Materials in Energy Devices Enabled by Cryo-FIB/SEM

(ACS Energy Letters, American Chemical Society (ACS), 2020-03-16) [Article]
Many modern energy devices rely on solid-liquid interfaces, highly reactive materials, or both, for their operation and performance. The difficulty of characterizing such materials means these devices often lack high-resolution characterization in an unaltered state. Here, we demonstrate how cryogenic sample preparation and transfer can extend the capabilities of FIB/SEM techniques to the solid-liquid interfaces and reactive materials common to energy devices by preserving their integrity through all stages of preparation and characterization. We additionally show how cryo-FIB/SEM paired with energy dispersive X-ray spectroscopy enables nanoscale elemental mapping of cross-sections produced in these materials and discuss strategies to achieve optimal results. Finally, we consider current limitations of the technique and propose future developments that could enhance its capabilities. Our results illustrate that cryo-FIB/SEM will be a useful technique for fields where solid-liquid interfaces or reactive materials play an important role and could, thus far, not be characterized at high resolution.
• #### Instant recovery of shape from spectrum via latent space connections

(arXiv, 2020-03-14) [Preprint]
We introduce the first learning-based method for recovering shapes from Laplacian spectra. Given an auto-encoder, our model takes the form of a cycle-consistent module to map latent vectors to sequences of eigenvalues. This module provides an efficient and effective linkage between spectrum and geometry of a given shape. Our data-driven approach replaces the need for ad-hoc regularizers required by prior methods, while providing more accurate results at a fraction of the computational cost. Our learning model applies without modifications across different dimensions (2D and 3D shapes alike), representations (meshes, contours and point clouds), as well as across different shape classes, and admits arbitrary resolution of the input spectrum without affecting complexity. The increased flexibility allows us to provide a proxy to differentiable eigendecomposition and to address notoriously difficult tasks in 3D vision and geometry processing within a unified framework, including shape generation from spectrum, mesh super-resolution, shape exploration, style transfer, spectrum estimation from point clouds, segmentation transfer and point-to-point matching.

(Journal of Guidance, Control, and Dynamics, American Institute of Aeronautics and Astronautics (AIAA), 2020-03-14) [Article]
Collisions may be harnessed as a way to improve the overall safety and navigational effectiveness of some spacecraft. However, leveraging this capability in autonomous platforms requires the ability to plan trajectories comprising impulsive contact. This paper addresses this problem through the development of a collision-inclusive approach to optimal trajectory planning for a three-degree-of-freedom free-flying spacecraft. First, experimental data are used to formulate a physically realistic collision model for the spacecraft. It is shown that this model is linear over the expected operational range, enabling a piecewise affine representation of the full hybrid vehicle dynamics. Next, the dynamics model and vehicle constraints are incorporated into a mixed integer program. Experimental comparisons of trajectories with and without collision-avoidance requirements demonstrate the capability of the collision-inclusive strategy to achieve significant performance improvements in realistic scenarios. A simulated case study illustrates the potential for this approach to find damage-mitigating paths in online implementations.
• #### Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems

(arXiv, 2020-03-12) [Preprint]
We study the problem of adaptive control in partially observable linear quadratic Gaussian control systems, where the model dynamics are unknown a priori. We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty, to effectively minimize the overall control cost. We employ the predictor state evolution representation of the system dynamics and propose a new approach for closed-loop system identification, estimation, and confidence bound construction. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model for further exploration and exploitation. We provide stability guarantees for LqgOpt, and prove the regret upper bound of $\tilde{\mathcal{O}}(\sqrt{T})$ for adaptive control of linear quadratic Gaussian (LQG) systems, where $T$ is the time horizon of the problem.
• #### The theoretical and practical dimension of sustainability in architecture: Analytical study of a number of architectural projects

(IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2020-03-06) [Article]
In the challenges facing the world, contemporary architecture is oriented towards sustainability as a radical and comprehensive solution that reorients architecture and its currents on both sides of thought and practice. On the other hand, in the context of the technological architectural practice, there are a number of failures that raise many questions about sustainability and what it means and how to achieve it. In this context, this study aims to: Analyze the specificity of sustainable practice in architecture in its applied side compared to the theoretical aspect. According to the aim of this study, a number of procedural steps were outlined that identified general aspects of sustainability, theoretically & practically. First of them was analyzing a number of selected studies with sustainability in architecture and extracting the most important aspectsrelated to achieving sustainability as a theoretical framework. Through which to measure the achievement of sustainability or not, and then apply it on a number of architectural projects with a global classification of sustainability. To drive indicators and aspects resulting from application and to draw general and specific conclusions of the identification of aspects of sustainability in architectural practice, which emphasized the separation between practice and application, and the lack of inclusiveness of sustainable practice as compared to the theoretical frameworks of sustainability.
• #### Homoacetogenesis and microbial community composition are shaped by pH and total sulfide concentration

(Microbial Biotechnology, Wiley, 2020-03-03) [Article]
Biological CO2 sequestration through acetogenesis with H2 as electron donor is a promising technology to reduce greenhouse gas emissions. Today, a major issue is the presence of impurities such as hydrogen sulfide (H2S) in CO2 containing gases, as they are known to inhibit acetogenesis in CO2-based fermentations. However, exact values of toxicity and inhibition are not well-defined. To tackle this uncertainty, a series of toxicity experiments were conducted, with a mixed homoacetogenic culture, total dissolved sulfide concentrations ([TDS]) varied between 0 and 5 mM and pH between 5 and 7. The extent of inhibition was evaluated based on acetate production rates and microbial growth. Maximum acetate production rates of 0.12, 0.09 and 0.04 mM h-1 were achieved in the controls without sulfide at pH 7, pH 6 and pH 5. The half-maximal inhibitory concentration (IC50qAc) was 0.86, 1.16 and 1.36 mM [TDS] for pH 7, pH 6 and pH 5. At [TDS] above 3.33 mM, acetate production and microbial growth were completely inhibited at all pHs. 16S rRNA gene amplicon sequencing revealed major community composition transitions that could be attributed to both pH and [TDS]. Based on the observed toxicity levels, treatment approaches for incoming industrial CO2 streams can be determined.
• #### Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data

(Nature Communications, Springer Science and Business Media LLC, 2020-03-03) [Article]
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. Here, we present Sub-Compartment Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict sub-compartments using Hi-C chromatin interaction data. We find that the network topological centrality and clustering performance of SCI sub-compartment predictions are superior to those of hidden Markov model (HMM) subcompartment predictions. Moreover, using orthogonal Chromatin Interaction Analysis by insitu Paired-End Tag Sequencing (ChIA-PET) data, we confirmed that SCI sub compartment prediction outperforms HMM. We show that SCI-predicted sub-compartments have distinct epigenetic marks, transcriptional activities, and transcription factor enrichment. Moreover, we present a deep neural network to predict sub-compartments using epigenome, replication timing, and sequence data. Our neural network predicts more accurate sub-compartment predictions when SCI-determined sub-compartments are used as labels for training