Showing papers by "University of Nevada, Reno published in 2018"

Search for New Physics with Atoms and Molecules

Abstract: Advances in atomic physics, such as cooling and trapping of atoms and molecules and developments in frequency metrology, have added orders of magnitude to the precision of atom-based clocks and sensors. Applications extend beyond atomic physics and this article reviews using these new techniques to address important challenges in physics and to look for variations in the fundamental constants, search for interactions beyond the standard model of particle physics, and test the principles of general relativity.

Recent Asian origin of chytrid fungi causing global amphibian declines

Abstract: Globalized infectious diseases are causing species declines worldwide, but their source often remains elusive. We used whole-genome sequencing to solve the spatiotemporal origins of the most devastating panzootic to date, caused by the fungus Batrachochytrium dendrobatidis, a proximate driver of global amphibian declines. We traced the source of B. dendrobatidis to the Korean peninsula, where one lineage, BdASIA-1, exhibits the genetic hallmarks of an ancestral population that seeded the panzootic. We date the emergence of this pathogen to the early 20th century, coinciding with the global expansion of commercial trade in amphibians, and we show that intercontinental transmission is ongoing. Our findings point to East Asia as a geographic hotspot for B. dendrobatidis biodiversity and the original source of these lineages that now parasitize amphibians worldwide.

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

Abstract: The growing importance of massive datasets with the advent of deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automatic labeling for large datasets, non-expert labeling, and label corruption by data poisoning adversaries. In the latter case, corruptions may be arbitrarily bad, even so bad that a classifier predicts the wrong labels with high confidence. To protect against such sources of noise, we leverage the fact that a small set of clean labels is often easy to procure. We demonstrate that robustness to label noise up to severe strengths can be achieved by using a set of trusted data with clean labels, and propose a loss correction that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers. Across vision and natural language processing tasks, we experiment with various label noises at several strengths, and show that our method significantly outperforms existing methods.

ALKBH5-dependent m6A demethylation controls splicing and stability of long 3′-UTR mRNAs in male germ cells

Abstract: N6-methyladenosine (m6A) represents one of the most common RNA modifications in eukaryotes. Specific m6A writer, eraser, and reader proteins have been identified. As an m6A eraser, ALKBH5 specifically removes m6A from target mRNAs and inactivation of Alkbh5 leads to male infertility in mice. However, the underlying molecular mechanism remains unknown. Here, we report that ALKBH5-mediated m6A erasure in the nuclei of spermatocytes and round spermatids is essential for correct splicing and the production of longer 3′-UTR mRNAs, and failure to do so leads to aberrant splicing and production of shorter transcripts with elevated levels of m6A that are rapidly degraded. Our study identified reversible m6A modification as a critical mechanism of posttranscriptional control of mRNA fate in late meiotic and haploid spermatogenic cells.

Dnmt2 mediates intergenerational transmission of paternally acquired metabolic disorders through sperm small non-coding RNAs.

Abstract: The discovery of RNAs (for example, messenger RNAs, non-coding RNAs) in sperm has opened the possibility that sperm may function by delivering additional paternal information aside from solely providing the DNA 1 . Increasing evidence now suggests that sperm small non-coding RNAs (sncRNAs) can mediate intergenerational transmission of paternally acquired phenotypes, including mental stress2,3 and metabolic disorders4-6. How sperm sncRNAs encode paternal information remains unclear, but the mechanism may involve RNA modifications. Here we show that deletion of a mouse tRNA methyltransferase, DNMT2, abolished sperm sncRNA-mediated transmission of high-fat-diet-induced metabolic disorders to offspring. Dnmt2 deletion prevented the elevation of RNA modifications (m5C, m2G) in sperm 30-40 nt RNA fractions that are induced by a high-fat diet. Also, Dnmt2 deletion altered the sperm small RNA expression profile, including levels of tRNA-derived small RNAs and rRNA-derived small RNAs, which might be essential in composing a sperm RNA 'coding signature' that is needed for paternal epigenetic memory. Finally, we show that Dnmt2-mediated m5C contributes to the secondary structure and biological properties of sncRNAs, implicating sperm RNA modifications as an additional layer of paternal hereditary information.

Deep convolutional networks do not classify based on global object shape.

Abstract: Deep convolutional networks (DCNNs) are achieving previously unseen performance in object classification, raising questions about whether DCNNs operate similarly to human vision. In biological vision, shape is arguably the most important cue for recognition. We tested the role of shape information in DCNNs trained to recognize objects. In Experiment 1, we presented a trained DCNN with object silhouettes that preserved overall shape but were filled with surface texture taken from other objects. Shape cues appeared to play some role in the classification of artifacts, but little or none for animals. In Experiments 2–4, DCNNs showed no ability to classify glass figurines or outlines but correctly classified some silhouettes. Aspects of these results led us to hypothesize that DCNNs do not distinguish object’s bounding contours from other edges, and that DCNNs access some local shape features, but not global shape. In Experiment 5, we tested this hypothesis with displays that preserved local features but disrupted global shape, and vice versa. With disrupted global shape, which reduced human accuracy to 28%, DCNNs gave the same classification labels as with ordinary shapes. Conversely, local contour changes eliminated accurate DCNN classification but caused no difficulty for human observers. These results provide evidence that DCNNs have access to some local shape information in the form of local edge relations, but they have no access to global object shapes.

Phylogenomics and the evolution of hemipteroid insects.

Abstract: Hemipteroid insects (Paraneoptera), with over 10% of all known insect diversity, are a major component of terrestrial and aquatic ecosystems. Previous phylogenetic analyses have not consistently resolved the relationships among major hemipteroid lineages. We provide maximum likelihood-based phylogenomic analyses of a taxonomically comprehensive dataset comprising sequences of 2,395 single-copy, protein-coding genes for 193 samples of hemipteroid insects and outgroups. These analyses yield a well-supported phylogeny for hemipteroid insects. Monophyly of each of the three hemipteroid orders (Psocodea, Thysanoptera, and Hemiptera) is strongly supported, as are most relationships among suborders and families. Thysanoptera (thrips) is strongly supported as sister to Hemiptera. However, as in a recent large-scale analysis sampling all insect orders, trees from our data matrices support Psocodea (bark lice and parasitic lice) as the sister group to the holometabolous insects (those with complete metamorphosis). In contrast, four-cluster likelihood mapping of these data does not support this result. A molecular dating analysis using 23 fossil calibration points suggests hemipteroid insects began diversifying before the Carboniferous, over 365 million years ago. We also explore implications for understanding the timing of diversification, the evolution of morphological traits, and the evolution of mitochondrial genome organization. These results provide a phylogenetic framework for future studies of the group.

{PCC} Vivace: Online-Learning Congestion Control

Abstract: TCP’s congestion control architecture suffers from notoriously bad performance. Consequently, recent years have witnessed a surge of interest in both academia and industry in novel approaches to congestion control. We show, however, that past approaches fall short of attaining ideal performance. We leverage ideas from the rich literature on online (convex) optimization in machine learning to design Vivace, a novel rate-control protocol, designed within the recently proposed PCC framework. Our theoretical and experimental analyses establish that Vivace significantly outperforms traditional TCP variants, the previous realization of the PCC framework, and BBR in terms of performance (throughput, latency, loss), convergence speed, alleviating bufferbloat, reactivity to changing network conditions, and friendliness towards legacy TCP in a range of scenarios. Vivace requires only sender-side changes and is thus readily deployable.

Ultrathin WO3·0.33H2O Nanotubes for CO2 Photoreduction to Acetate with High Selectivity

Abstract: Artificial photosynthesis from CO2 reduction is severely hampered by the kinetically challenging multi-electron reaction process. Oxygen vacancies (Vo) with abundant localized electrons have great potential to overcome this limitation. However, surface Vo usually have low concentrations and are easily oxidized, causing them to lose their activities. For practical application of CO2 photoreduction, fabricating and enhancing the stability of Vo on semiconductors is indispensable. Here we report the first synthesis of ultrathin WO3·0.33H2O nanotubes with a large amount of exposed surface Vo sites, which can realize excellent and stable CO2 photoreduction to CH3COOH in pure water under solar light. The selectivity for acetum generation is up to 85%, with an average productivity of about 9.4 μmol g–1 h–1. More importantly, Vo in the catalyst are sustainable, and their concentration was not decreased even after 60 h of reaction. Quantum chemical calculations and in situ DRIFT studies revealed that the main reac...

Harm Minimization and Tobacco Control: Reframing Societal Views of Nicotine Use to Rapidly Save Lives.

Abstract: Inhalation of the toxic smoke produced by combusting tobacco products, primarily cigarettes, is the overwhelming cause of tobacco-related disease and death in the United States and globally. A diverse class of alternative nicotine delivery systems (ANDS) has recently been developed that do not combust tobacco and are substantially less harmful than cigarettes. ANDS have the potential to disrupt the 120-year dominance of the cigarette and challenge the field on how the tobacco pandemic could be reversed if nicotine is decoupled from lethal inhaled smoke. ANDS may provide a means to compete with, and even replace, combusted cigarette use, saving more lives more rapidly than previously possible. On the basis of the scientific evidence on ANDS, we explore benefits and harms to public health to guide practice, policy, and regulation. A reframing of societal nicotine use through the lens of harm minimization is an extraordinary opportunity to enhance the impact of tobacco control efforts.

Receding horizon path planning for 3D exploration and surface inspection

Abstract: Within this paper a new path planning algorithm for autonomous robotic exploration and inspection is presented. The proposed method plans online in a receding horizon fashion by sampling possible future configurations in a geometric random tree. The choice of the objective function enables the planning for either the exploration of unknown volume or inspection of a given surface manifold in both known and unknown volume. Application to rotorcraft Micro Aerial Vehicles is presented, although planning for other types of robotic platforms is possible, even in the absence of a boundary value solver and subject to nonholonomic constraints. Furthermore, the method allows the integration of a wide variety of sensor models. The presented analysis of computational complexity and thorough simulations-based evaluation indicate good scaling properties with respect to the scenario complexity. Feasibility and practical applicability are demonstrated in real-life experimental test cases with full on-board computation.

Mobile Edge Computing Empowered Energy Efficient Task Offloading in 5G

Abstract: Mobile edge computing has been proposed in recent years to offload computation tasks from user equipments (UEs) to the network edge to break hardware limitations and resource constraints at UEs. Although there have been some existing works on computation offloading in 5G, most of them fail to take into account the unique property of 5G in their scheme design. In this paper, we consider small-cell network architecture for task offloading. In order to achieve energy efficiency, we model the energy consumption of offloading from both task computation and communication aspects. Besides, transmission scheduling are carried over both the fronthaul and backhaul links. We first formulate an energy optimization problem of offloading, which aims at minimizing the overall energy consumption at all system entities and takes into account of the constraints from both computation capabilities and service delay requirement. We then develop an artificial fish swarm algorithm based scheme to solve the energy optimization problem. Besides, the global convergence property of the our scheme is formally proven. Finally, various simulation results demonstrate the efficiency of our scheme.

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

Abstract: The growing importance of massive datasets used for deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automatic labeling, non-expert labeling, and label corruption by data poisoning adversaries. Numerous previous works assume that no source of labels can be trusted. We relax this assumption and assume that a small subset of the training data is trusted. This enables substantial label corruption robustness performance gains. In addition, particularly severe label noise can be combated by using a set of trusted data with clean labels. We utilize trusted data by proposing a loss correction technique that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers. Across vision and natural language processing tasks, we experiment with various label noises at several strengths, and show that our method significantly outperforms existing methods.

Bioinspired leaves-on-branchlet hybrid carbon nanostructure for supercapacitors

Abstract: Designing electrodes in a highly ordered structure simultaneously with appropriate orientation, outstanding mechanical robustness, and high electrical conductivity to achieve excellent electrochemical performance remains a daunting challenge. Inspired by the phenomenon in nature that leaves significantly increase exposed tree surface area to absorb carbon dioxide (like ions) from the environments (like electrolyte) for photosynthesis, we report a design of micro-conduits in a bioinspired leaves-on-branchlet structure consisting of carbon nanotube arrays serving as branchlets and graphene petals as leaves for such electrodes. The hierarchical all-carbon micro-conduit electrodes with hollow channels exhibit high areal capacitance of 2.35 F cm-2 (~500 F g-1 based on active material mass), high rate capability and outstanding cyclic stability (capacitance retention of ~95% over 10,000 cycles). Furthermore, Nernst-Planck-Poisson calculations elucidate the underlying mechanism of charge transfer and storage governed by sharp graphene petal edges, and thus provides insights into their outstanding electrochemical performance.

Quinoa Abiotic Stress Responses: A Review

Abstract: Quinoa (Chenopodium quinoa Willd.) is a genetically diverse Andean crop that has earned special attention worldwide due to its nutritional and health benefits and its ability to adapt to contrasting environments, including nutrient-poor and saline soils and drought stressed marginal agroecosystems. Drought and salinity are the abiotic stresses most studied in quinoa; however, studies of other important stress factors, such as heat, cold, heavy metals, and UV-B light irradiance, are severely limited. In the last few decades, the incidence of abiotic stress has been accentuated by the increase in unpredictable weather patterns. Furthermore, stresses habitually occur as combinations of two or more. The goals of this review are to: (1) provide an in-depth description of the existing knowledge of quinoa’s tolerance to different abiotic stressors; (2) summarize quinoa’s physiological responses to these stressors; and (3) describe novel advances in molecular tools that can aid our understanding of the mechanisms underlying quinoa’s abiotic stress tolerance.

Development of a Mixed Methods Investigation of Process and Outcomes of Community-Based Participatory Research

Abstract: This article describes a mixed methods study of community-based participatory research (CBPR) partnership practices and the links between these practices and changes in health status and disparities outcomes. Directed by a CBPR conceptual model and grounded in indigenous-transformative theory, our nation-wide, cross-site study showcases the value of a mixed methods approach for better understanding the complexity of CBPR partnerships across diverse community and research contexts. The article then provides examples of how an iterative, integrated approach to our mixed methods analysis yielded enriched understandings of two key constructs of the model: trust and governance. Implications and lessons learned while using mixed methods to study CBPR are provided.

Convolutional Neural Network for Trajectory Prediction

Abstract: Predicting trajectories of pedestrians is quintessential for autonomous robots which share the same environment with humans. In order to effectively and safely interact with humans, trajectory prediction needs to be both precise and computationally efficient. In this work, we propose a convolutional neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our model supports increased parallelism and effective temporal representation. The proposed compact CNN model is faster than the current approaches yet still yields competitive results.

Chasing biomimetic locomotion speeds: Creating untethered soft robots with shape memory alloy actuators.

Abstract: By using compliant lightweight actuators with shape memory alloy, we created untethered soft robots that are capable of dynamic locomotion at biologically relevant speeds.

Global accumulation of circRNAs during aging in Caenorhabditis elegans.

Abstract: Circular RNAs (CircRNAs) are a newly appreciated class of RNAs that lack free 5′ and 3′ ends, are expressed by the thousands in diverse forms of life, and are mostly of enigmatic function. Ostensibly due to their resistance to exonucleases, circRNAs are known to be exceptionally stable. Previous work in Drosophila and mice have shown that circRNAs increase during aging in neural tissues. Here, we examined the global profile of circRNAs in C. elegans during aging by performing ribo-depleted total RNA-seq from the fourth larval stage (L4) through 10-day old adults. Using stringent bioinformatic criteria and experimental validation, we annotated a high-confidence set of 1166 circRNAs, including 575 newly discovered circRNAs. These circRNAs were derived from 797 genes with diverse functions, including genes involved in the determination of lifespan. A massive accumulation of circRNAs during aging was uncovered. Many hundreds of circRNAs were significantly increased among the aging time-points and increases of select circRNAs by over 40-fold during aging were quantified by RT-qPCR. The expression of 459 circRNAs was determined to be distinct from the expression of linear RNAs from the same host genes, demonstrating host gene independence of circRNA age-accumulation. We attribute the global scale of circRNA age-accumulation to the high composition of post-mitotic cells in adult C. elegans, coupled with the high resistance of circRNAs to decay. These findings suggest that the exceptional stability of circRNAs might explain age-accumulation trends observed from neural tissues of other organisms, which also have a high composition of post-mitotic cells. Given the suitability of C. elegans for aging research, it is now poised as an excellent model system to determine whether there are functional consequences of circRNA accumulation during aging.

Shifts in disease dynamics in a tropical amphibian assemblage are not due to pathogen attenuation.

Abstract: Infectious diseases rarely end in extinction. Yet the mechanisms that explain how epidemics subside are difficult to pinpoint. We investigated host-pathogen interactions after the emergence of a lethal fungal pathogen in a tropical amphibian assemblage. Some amphibian host species are recovering, but the pathogen is still present and is as pathogenic today as it was almost a decade ago. In addition, some species have defenses that are more effective now than they were before the epidemic. These results suggest that host recoveries are not caused by pathogen attenuation and may be due to shifts in host responses. Our findings provide insights into the mechanisms underlying disease transitions, which are increasingly important to understand in an era of emerging infectious diseases and unprecedented global pandemics.

Developing a Coordinated Signal Control System for Urban Ring Road Under the Vehicle-Infrastructure Connected Environment

Abstract: Ring roads have been widely built in many cities, especially in the central districts with excessively heavy traffic demands and frequently generated congestion. In order to improve the operations and reduce traffic delay on urban ring roads, this paper developed a coordinated signal control system for urban ring roads under vehicle-infrastructure connected environment. The speed guidance would be provided to motorists utilizing four sub-systems including detection, communication, signal control, and expected speed calculation in the system. The signal timing parameters such as cycle length, green split, and offset, would be adjusted based on the artificial bee colony-shuffled frog leaping algorithm. The proposed signal control system had been test using VISSIM simulation model and the simulation results showed that the average delay, number of stops, and queue length were significantly improved compared with the conventional traffic control system.

Transitions in electronic cigarette use among adults in the Population Assessment of Tobacco and Health (PATH) Study, Waves 1 and 2 (2013-2015).

Abstract: Introduction This study assessed patterns of e-cigarette and cigarette use from Wave 1 to Wave 2 among adult e-cigarette users at Wave 1 of the Population Assessment of Tobacco and Health (PATH) Study. Methods We examined changes in e-cigarette use frequency at Wave 2 among adult e-cigarette users at Wave 1 (unweighted n=2835). Adjusted prevalence ratios (aPR) were calculated using a predicted marginal probability approach to assess correlates of e-cigarette discontinuance and smoking abstinence at Wave 2. Results Half (48.8%) of adult e-cigarette users at Wave 1 discontinued their use of e-cigarettes at Wave 2. Among dual users of e-cigarettes and cigarettes at Wave 1, 44.3% maintained dual use, 43.5% discontinued e-cigarette use and maintained cigarette smoking and 12.1% discontinued cigarette use at Wave 2, either by abstaining from cigarette smoking only (5.1%) or discontinuing both products (7.0%). Among dual users at Wave 1, daily e-cigarette users were more likely than non-daily users to report smoking abstinence at Wave 2 (aPR=1.40, 95% CI 1.02 to 1.91). Using a customisable device (rather than a non-customisable one) was not significantly related to smoking abstinence at Wave 2 (aPR=1.14, 95% CI 0.81 to 1.60). Conclusions This study suggests that e-cigarette use patterns are highly variable over a 1-year period. This analysis provides the first nationally representative estimates of transitions among US adult e-cigarette users. Future research, including additional waves of the PATH Study, can provide further insight into long-term patterns of e-cigarette use critical to understanding the net population health impact of e-cigarettes in USA.

Reviewing the Assumptions About Men's Mental Health: An Exploration of the Gender Binary.

Abstract: Many researchers take for granted that men's mental health can be explained in the same terms as women's or can be gauged using the same measures. Women tend to have higher rates of internalizing disorders (i.e., depression, anxiety), while men experience more externalizing symptoms (i.e., violence, substance abuse). These patterns are often attributed to gender differences in socialization (including the acquisition of expectations associated with traditional gender roles), help seeking, coping, and socioeconomic status. However, measurement bias (inadequate survey assessment of men's experiences) and clinician bias (practitioner's subconscious tendency to overlook male distress) may lead to underestimates of the prevalence of depression and anxiety among men. Continuing to focus on gender differences in mental health may obscure significant within-gender group differences in men's symptomatology. In order to better understand men's lived experiences and their psychological well-being, it is crucial for scholars to focus exclusively on men's mental health.

The Latent Structure of Negative Symptoms in Schizophrenia.

Abstract: Importance Negative symptoms are associated with a range of poor clinical outcomes, and currently available treatments generally do not produce a clinically meaningful response Limited treatment progress may be owing in part to poor clarity regarding latent structure Prior studies have inferred latent structure using exploratory factor analysis, which has led to the conclusion that there are 2 dimensions reflecting motivation and pleasure (MAP) and diminished expressivity (EXP) factors However, whether these conclusions are statistically justified remains unclear because exploratory factor analysis does not test latent structure Confirmatory factor analysis (CFA) is needed to test competing models regarding the latent structure of a construct Objective To evaluate the fit of 4 models of the latent structure of negative symptoms in schizophrenia using CFA Design, Setting, and Participants Three cross-sectional studies were conducted on outpatients with schizophrenia who were rated on the 3 most conceptually contemporary measures: Scale for the Assessment of Negative Symptoms (SANS), Brief Negative Symptom Scale (BNSS), and Clinical Assessment Interview for Negative Symptoms (CAINS) Confirmatory factor analysis evaluated the following 4 models: (1) a 1-factor model; (2) a 2-factor model with EXP and MAP factors; (3) a 5-factor model with separate factors for the 5 domains of the National Institute of Mental Health consensus development conference (blunted affect, alogia, anhedonia, avolition, and asociality); and (4) a hierarchical model with 2 second-order factors reflecting EXP and MAP and 5 first-order factors reflecting the 5 consensus domains Main Outcomes and Measures Outcomes included CFA model fit statistics derived from symptom severity scores on the SANS, BNSS, and CAINS Results The study population included 860 outpatients with schizophrenia (680% male; mean [SD] age, 430 [114] years) Confirmatory factor analysis was conducted on each scale, including 268 patients for the SANS, 192 for the BNSS, and 400 for the CAINS The 1- and 2-factor models provided poor fit for the SANS, BNSS, and CAINS as indicated by comparative fit indexes (CFIs) and Tucker Lewis indexes (TLIs) less than 0950, RMSEAs that exceeded the 0080 threshold, and WRMRs greater than 100 The 5-factor and hierarchical models provided excellent fit, with the 5-factor model being more parsimonious The CFIs and TLIs met the 095 threshold and the 100 threshold for both factor models with all 3 measures Interestingly, the RMSEAs for the 5-factor model and the hierarchical model fell under the 008 threshold for the BNSS and the CAINS but not the SANS Conclusions and Relevance These findings suggest that the recent trend toward conceptualizing the latent structure of negative symptoms as 2 distinct dimensions does not adequately capture the complexity of the construct The latent structure of negative symptoms is best conceptualized in relation to the 5 consensus domains Implications for identifying pathophysiological mechanisms and targeted treatments are discussed

Reaction Mechanism and Kinetics for Ammonia Synthesis on the Fe(111) Surface.

Abstract: The Haber-Bosch industrial process for synthesis of ammonia (NH3) from hydrogen and nitrogen produces the millions of tons of ammonia gas annually needed to produce nitrates for fertilizers required to feed the earth’s growing populations. This process has been optimized extensively, but it still uses enormous amounts of energy (2% of the world’s supply), making it essential to dramatically improve its efficiency. To provide guidelines to accelerate this improvement, we used quantum mechanics to predict reaction mechanisms and kinetics for NH3 synthesis on Fe(111)—the best Fe single crystal surface for NH3 synthesis. We predicted the free energies of all reaction barriers for all steps in the mechanism and built these results into a kinetic Monte Carlo model for predicting steady state catalytic rates to compare with single-crystal experiments at 673 K and 20 atm. We find excellent agreement with a predicted turnover frequency (TOF) of 17.7 s–1 per 2 × 2 site (5.3 × 10–9 mol/cm2/sec) compared to TOF = 10 ...