Showing papers by "Oklahoma State University–Stillwater published in 2017"

The Hierarchical Taxonomy of Psychopathology (HiTOP): A Dimensional Alternative to Traditional Nosologies

Abstract: The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record

Where less may be more: How the rare biosphere pulls ecosystems strings

Abstract: Rare species are increasingly recognized as crucial, yet vulnerable components of Earth’s ecosystems. This is also true for microbial communities, which are typically composed of a high number of relatively rare species. Recent studies have demonstrated that rare species can have an over-proportional role in biogeochemical cycles and may be a hidden driver of microbiome function. In this review, we provide an ecological overview of the rare microbial biosphere, including causes of rarity and the impacts of rare species on ecosystem functioning. We discuss how rare species can have a preponderant role for local biodiversity and species turnover with rarity potentially bound to phylogenetically conserved features. Rare microbes may therefore be overlooked keystone species regulating the functioning of host-associated, terrestrial and aquatic environments. We conclude this review with recommendations to guide scientists interested in investigating this rapidly emerging research area.

A multi-species synthesis of physiological mechanisms in drought-induced tree mortality

Abstract: Widespread tree mortality associated with drought has been observed on all forested continents and global change is expected to exacerbate vegetation vulnerability. Forest mortality has implications for future biosphere-atmosphere interactions of carbon, water and energy balance, and is poorly represented in dynamic vegetation models. Reducing uncertainty requires improved mortality projections founded on robust physiological processes. However, the proposed mechanisms of drought-induced mortality, including hydraulic failure and carbon starvation, are unresolved. A growing number of empirical studies have investigated these mechanisms, but data have not been consistently analysed across species and biomes using a standardized physiological framework. Here, we show that xylem hydraulic failure was ubiquitous across multiple tree taxa at drought-induced mortality. All species assessed had 60% or higher loss of xylem hydraulic conductivity, consistent with proposed theoretical and modelled survival thresholds. We found diverse responses in non-structural carbohydrate reserves at mortality, indicating that evidence supporting carbon starvation was not universal. Reduced non-structural carbohydrates were more common for gymnosperms than angiosperms, associated with xylem hydraulic vulnerability, and may have a role in reducing hydraulic function. Our finding that hydraulic failure at drought-induced mortality was persistent across species indicates that substantial improvement in vegetation modelling can be achieved using thresholds in hydraulic function.

Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network

Abstract: The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel-perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel-perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F-measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F-measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software.

Performance of the ATLAS trigger system in 2015

Abstract: During 2015 the ATLAS experiment recorded 3.8 fb(-1) of proton-proton collision data at a centre-of-mass energy of 13 TeV. The ATLAS trigger system is a crucial component of the experiment, respons ...

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

Abstract: The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Carbon fiber reinforced metal matrix composites: Fabrication processes and properties

Abstract: This paper reviews the research and development works conducted over the past few decades on carbon fiber reinforced metal matrix composites (CFR-MMC). The structure and composition of carbon fiber and its bonding to metal matrix have an impact on the properties of the resulting CFR-MMC remarkably. The research efforts on process optimization and utilizing of carbon fibers are discussed in this review. The effect of carbon fiber on structural, physical and mechanical properties of metal matrix composite are studied as well. This review also provide an overview of the research to date on various fabrication methods that is used for production of CFR-MMC.

Search for new high-mass phenomena in the dilepton final state using 36 fb−1 of proton-proton collision data at √s=13 TeV with the ATLAS detector

Abstract: A search is conducted for new resonant and non-resonant high-mass phenomena in dielectron and dimuon fi nal states. The search uses 36 : 1 fb(-1) of proton-proton collision data, collected at root ...

The Impact of Parenting on Emotion Regulation During Childhood and Adolescence

Abstract: Regulating emotions well is critical for promoting social and emotional health among children and adolescents. Parents play a prominent role in how children develop emotion regulation. In 2007, Morris et al. proposed a tripartite model suggesting that parents influence children's emotion regulation through three mechanisms: children's observation of parents' emotion regulation, emotion-related parenting practices, and the emotional climate of the family. Over the past decade, we have conducted many studies that support this model, which we summarize here along with other research related to parenting and emotion regulation. We also discuss recent research on the effects of parenting on the neural circuitry involved in emotion regulation and highlight potential directions for research. Finally, we suggest how this research can aid prevention and intervention efforts to help families.

Evolving Deep Convolutional Neural Networks for Image Classification

Abstract: Evolutionary computation methods have been successfully applied to neural networks since two decades ago, while those methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of connection weights. In this paper, we propose a new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems. In the proposed algorithm, an efficient variable-length gene encoding strategy is designed to represent the different building blocks and the unpredictable optimal depth in convolutional neural networks. In addition, a new representation scheme is developed for effectively initializing connection weights of deep convolutional neural networks, which is expected to avoid networks getting stuck into local minima which is typically a major issue in the backward gradient-based optimization. Furthermore, a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource. The proposed algorithm is examined and compared with 22 existing algorithms on nine widely used image classification tasks, including the state-of-the-art methods. The experimental results demonstrate the remarkable superiority of the proposed algorithm over the state-of-the-art algorithms in terms of classification error rate and the number of parameters (weights).

Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments

Abstract: Pearl millet [Cenchrus americanus (L.) Morrone] is a staple food for more than 90 million farmers in arid and semi-arid regions of sub-Saharan Africa, India and South Asia. We report the ~1.79 Gb draft whole genome sequence of reference genotype Tift 23D2B1-P1-P5, which contains an estimated 38,579 genes. We highlight the substantial enrichment for wax biosynthesis genes, which may contribute to heat and drought tolerance in this crop. We resequenced and analyzed 994 pearl millet lines, enabling insights into population structure, genetic diversity and domestication. We use these resequencing data to establish marker trait associations for genomic selection, to define heterotic pools, and to predict hybrid performance. We believe that these resources should empower researchers and breeders to improve this important staple crop.

Deep architectures for modulation recognition

Abstract: We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that ratio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.

Tick-Pathogen Interactions and Vector Competence: Identification of Molecular Drivers for Tick-Borne Diseases

Abstract: Ticks and the pathogens they transmit constitute a growing burden for human and animal health worldwide. Vector competence is a component of vectorial capacity and depends on genetic determinants affecting the ability of a vector to transmit a pathogen. These determinants affect traits such as tick-host-pathogen and susceptibility to pathogen infection. Therefore, the elucidation of the mechanisms involved in tick-pathogen interactions that affect vector competence is essential for the identification of molecular drivers for tick-borne diseases. In this review, we provide a comprehensive overview of tick-pathogen molecular interactions for bacteria, viruses, and protozoa affecting human and animal health. Additionally, the impact of tick microbiome on these interactions was considered. Results show that different pathogens evolved similar strategies such as manipulation of the immune response to infect vectors and facilitate multiplication and transmission. Furthermore, some of these strategies may be used by pathogens to infect both tick and mammalian hosts. Identification of interactions that promote tick survival, spread, and pathogen transmission provides the opportunity to disrupt these interactions and lead to a reduction in tick burden and the prevalence of tick-borne diseases. Targeting some of the similar mechanisms used by the pathogens for infection and transmission by ticks may assist in development of preventative strategies against multiple tick-borne diseases.

How Water's Properties Are Encoded in Its Molecular Structure and Energies.

Abstract: How are water’s material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth’s living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies—water’s solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid. It has more solid phases than other materials. Its supercooled liquid has divergent thermodynamic response functions. Its glassy state is neither fragile nor strong. Its component ions—hydroxide and protons—diffuse much faster than other ions. Aqueous solvation of ions or oils entails large entropies and heat capacities. We review how these properties are encoded within water’s molecular structure and energies, as understood from theories, simulations, and experiments. Like simpler liquids, water molecules are nearly spherical and in...

Search for new phenomena in dijet events using 37 fb-1 of pp collision data collected at √s=13 TeV with the ATLAS detector

Abstract: Dijet events are studied in the proton-proton collision dataset recorded at root s = 13 TeV with the ATLAS detector at the Large Hadron Collider in 2015 and 2016, corresponding to integrated lumino ...

Have combustible cigarettes met their match? The nicotine delivery profiles and harmful constituent exposures of second-generation and third-generation electronic cigarette users

Abstract: Introduction Electronic cigarettes’ (e-cigarettes) viability as a public health strategy to end smoking will likely be determined by their ability to mimic the pharmacokinetic profile of a cigarette while also exposing users to significantly lower levels of harmful/potentially harmful constituents (HPHCs). The present study examined the nicotine delivery profile of third- (G3) versus second-generation (G2) e-cigarette devices and their users9 exposure to nicotine and select HPHCs compared with cigarette smokers. Methods 30 participants (10 smokers, 9 G2 and 11 G3 users) completed baseline questionnaires and provided exhaled carbon monoxide (eCO), saliva and urine samples. Following a 12-hour nicotine abstinence, G2 and G3 users completed a 2-hour vaping session (ie, 5 min, 10-puff bout followed by ad libitum puffing for 115 min). Blood samples, subjective effects, device characteristics and e-liquid consumption were assessed. Results Smokers, G2 and G3 users had similar baseline levels of cotinine, but smokers had 4 and 7 times higher levels of eCO (p Discussion Under normal use conditions, both G2 and G3 devices deliver cigarette-like amounts of nicotine, but G3 devices matched the amount and speed of nicotine delivery of a conventional cigarette. Compared with cigarettes, G2 and G3 e-cigarettes resulted in significantly lower levels of exposure to a potent lung carcinogen and cardiovascular toxicant. These findings have significant implications for understanding the addiction potential of these devices and their viability/suitability as aids to smoking cessation.

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Systematically Pinching Ideas: A Comparative Approach to Policy Design

Abstract: Policy design,, whether conceptualized as a verb referring to the process of formulating policy ideas,, or as a noun describing the logic through which policy intends to achieve its objectives,, remains relatively uncharted territory.. This paper reviews what we know about how policy designs emerge,, and identifies the kinds of biases and weaknesses that are introduced into designs by the decision heuristics employed.. Theories of policy invention and expert decision--mmaking suggest that individuals search through large amounts of relevant information stored in memory,, reason by analogies,, make comparisons,, and either copy or simulate patterns of information.. Policy scholars may contribute to improved policy design by making more explicit the biases introduced through reliance on decision heuristics,, and by suggesting a more formal,, self conscious search and selection process that enables designers to be more discriminating when they pinch policy ideas from other contexts.. To perform this task,, comparative policy analysis is needed in which common elements that exist in virtually all policies are identified and the underlying structural logic of the policies is made explicit.. In this paperwe set forth generic elements found in policies,, describe and compare some of the more common design patterns,, and discuss the circumstances where these may be inappropriately copied or borrowed,, thereby thwarting the effectiveness of the policy.

PlantCV v2: Image analysis software for high-throughput plant phenotyping

Abstract: Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

A neural network approach for the blind deconvolution of turbulent flows

Abstract: We present a single-layer feed-forward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse-grained computations such as those encountered in large eddy simulations. We stress that the deconvolution procedure proposed in this investigation is blind, i.e. the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel. This may be conceptually contrasted to the celebrated approximate deconvolution approaches where a filter shape is predefined for an iterative deconvolution process. We demonstrate that the proposed blind deconvolution network performs exceptionally well in the a priori testing of two-dimensional Kraichnan, three-dimensional Kolmogorov and compressible stratified turbulence test cases, and shows promise in forming the backbone of a physics-augmented data-driven closure for the Navier–Stokes equations.

Cements in the 21st Century: Challenges, Perspectives, and Opportunities.

Abstract: In a book published in 1906, Richard Meade outlined the history of portland cement up to that point1. Since then there has been great progress in portland cement-based construction materials technologies brought about by advances in the materials science of composites and the development of chemical additives (admixtures) for applications. The resulting functionalities, together with its economy and the sheer abundance of its raw materials, have elevated ordinary portland cement (OPC) concrete to the status of most used synthetic material on Earth. While the 20th century was characterized by the emergence of computer technology, computational science and engineering, and instrumental analysis, the fundamental composition of portland cement has remained surprisingly constant. And, although our understanding of ordinary portland cement (OPC) chemistry has grown tremendously, the intermediate steps in hydration and the nature of calcium silicate hydrate (C-S-H), the major product of OPC hydration, remain clouded in uncertainty. Nonetheless, the century also witnessed great advances in the materials technology of cement despite the uncertain understanding of its most fundamental components. Unfortunately, OPC also has a tremendous consumption-based environmental impact, and concrete made from OPC has a poor strength-to-weight ratio. If these challenges are not addressed, the dominance of OPC could wane over the next 100 years. With this in mind, this paper envisions what the 21st century holds in store for OPC in terms of the driving forces that will shape our continued use of this material. Will a new material replace OPC, and concrete as we know it today, as the preeminent infrastructure construction material?

Impacts of nitrogen and phosphorus: from genomes to natural ecosystems and agriculture

Abstract: Nitrogen (N) and/or phosphorus (P) availability can limit growth of primary producers across most of the world’s aquatic and terrestrial ecosystems. These constraints are commonly overcome in agriculture by applying fertilizers to improve yields. However, excessive anthropogenic N and P inputs impact natural environments and have far-reaching ecological and evolutionary consequences, from individual species up to entire ecosystems. The extent to which global N and P cycles have been perturbed over the past century can be seen as a global fertilization experiment with significant redistribution of nutrients across different ecosystems. Here we explore the effects of N and P availability on stoichiometry and genomic traits of organisms, which, in turn, can influence: i) plant and animal abundances; ii) trophic interactions and population dynamics; and iii) ecosystem dynamics and productivity of agricultural crops. We articulate research priorities for a deeper understanding of how bioavailable N and P move through the environment and exert their ultimate impacts on biodiversity and ecosystem services.

Rosbridge: ROS for non-ROS users

Abstract: We present rosbridge, a middleware abstraction layer which provides robotics technology with a standard, minimalist applications development framework accessible to applications programmers who are not themselves roboticists. Rosbridge provides a simple, socket-based programmatic access to robot interfaces and algorithms provided (for now) by ROS, the open-source “Robot Operating System”, the current state-of-the-art in robot middleware. In particular, it facilitates the use of web technologies such as Javascript for the purpose of broadening the use and usefulness of robotic technology. We demonstrate potential applications in the interface design, education, human-robot interaction and remote laboratory environments.

3D-Printed Transparent Glass.

Abstract: Silica inks are developed, which may be 3D printed and thermally processed to produce optically transparent glass structures with sub-millimeter features in forms ranging from scaffolds to monoliths. The inks are composed of silica powder suspended in a liquid and are printed using direct ink writing. The printed structures are then dried and sintered at temperatures well below the silica melting point to form amorphous, solid, transparent glass structures. This technique enables the mold-free formation of transparent glass structures previously inaccessible using conventional glass fabrication processes.