Showing papers by "Clemson University published in 2021"

Towards complete and error-free genome assemblies of all vertebrate species

Abstract: High-quality and complete reference genome assemblies are fundamental for the application of genomics to biology, disease, and biodiversity conservation. However, such assemblies are available for only a few non-microbial species1-4. To address this issue, the international Genome 10K (G10K) consortium5,6 has worked over a five-year period to evaluate and develop cost-effective methods for assembling highly accurate and nearly complete reference genomes. Here we present lessons learned from generating assemblies for 16 species that represent six major vertebrate lineages. We confirm that long-read sequencing technologies are essential for maximizing genome quality, and that unresolved complex repeats and haplotype heterozygosity are major sources of assembly error when not handled correctly. Our assemblies correct substantial errors, add missing sequence in some of the best historical reference genomes, and reveal biological discoveries. These include the identification of many false gene duplications, increases in gene sizes, chromosome rearrangements that are specific to lineages, a repeated independent chromosome breakpoint in bat genomes, and a canonical GC-rich pattern in protein-coding genes and their regulatory regions. Adopting these lessons, we have embarked on the Vertebrate Genomes Project (VGP), an international effort to generate high-quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences.

Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States.

Abstract: Background University students are increasingly recognized as a vulnerable population, suffering from higher levels of anxiety, depression, substance abuse, and disordered eating compared to the general population. Therefore, when the nature of their educational experience radically changes—such as sheltering in place during the COVID-19 pandemic—the burden on the mental health of this vulnerable population is amplified. The objectives of this study are to 1) identify the array of psychological impacts COVID-19 has on students, 2) develop profiles to characterize students' anticipated levels of psychological impact during the pandemic, and 3) evaluate potential sociodemographic, lifestyle-related, and awareness of people infected with COVID-19 risk factors that could make students more likely to experience these impacts. Methods Cross-sectional data were collected through web-based questionnaires from seven U.S. universities. Representative and convenience sampling was used to invite students to complete the questionnaires in mid-March to early-May 2020, when most coronavirus-related sheltering in place orders were in effect. We received 2,534 completed responses, of which 61% were from women, 79% from non-Hispanic Whites, and 20% from graduate students. Results Exploratory factor analysis on close-ended responses resulted in two latent constructs, which we used to identify profiles of students with latent profile analysis, including high (45% of sample), moderate (40%), and low (14%) levels of psychological impact. Bivariate associations showed students who were women, were non-Hispanic Asian, in fair/poor health, of below-average relative family income, or who knew someone infected with COVID-19 experienced higher levels of psychological impact. Students who were non-Hispanic White, above-average social class, spent at least two hours outside, or less than eight hours on electronic screens were likely to experience lower levels of psychological impact. Multivariate modeling (mixed-effects logistic regression) showed that being a woman, having fair/poor general health status, being 18 to 24 years old, spending 8 or more hours on screens daily, and knowing someone infected predicted higher levels of psychological impact when risk factors were considered simultaneously. Conclusion Inadequate efforts to recognize and address college students’ mental health challenges, especially during a pandemic, could have long-term consequences on their health and education.

Cell-like-carbon-micro-spheres for robust potassium anode.

Abstract: Large-scale low-cost synthesis methods for potassium ion battery (PIB) anodes with long cycle life and high capacity have remained challenging. Here, inspired by the structure of a biological cell, biomimetic carbon cells (BCCs) were synthesized and used as PIB anodes. The protruding carbon nanotubes across the BCC wall mimicked the ion-transporting channels present in the cell membrane, and enhanced the rate performance of PIBs. In addition, the robust carbon shell of the BCC could protect its overall structure, and the open space inside the BCC could accommodate the volume changes caused by K+ insertion, which greatly improved the stability of PIBs. For the first time, a stable solid electrolyte interphase layer is formed on the surface of amorphous carbon. Collectively, the unique structural characteristics of the BCCs resulted in PIBs that showed a high reversible capacity (302 mAh g-1 at 100 mA g-1 and 248 mAh g-1 at 500 mA g-1), excellent cycle stability (reversible capacity of 226 mAh g-1 after 2100 cycles and a continuous running time of more than 15 months at a current density of 100 mA g-1), and an excellent rate performance (160 mAh g-1 at 1 A g-1). This study represents a new strategy for boosting battery performance, and could pave the way for the next generation of battery-powered applications.

Positive psychology in a pandemic: buffering, bolstering, and building mental health

Abstract: As the COVID-19 global health disaster continues to unfold across the world, calls have been made to address the associated mental illness public crisis. The current paper seeks to broaden these ca...

Sulfur-assisted large-scale synthesis of graphene microspheres for superior potassium-ion batteries

Abstract: Large-scale low-cost preparation methods for high quality graphene are critical for advancing graphene-based applications in energy storage, and beyond. Here, we present a sulfur-assisted method that converts benzene rings of tetraphenyltin into high purity crystalline graphene. Specifically, three dimensional few layer graphene microspheres (FLGMs) were prepared which proved ideal for energy storage applications. For a potassium ion battery, the FLGM-based anodes exhibited a low discharge platform (average discharge platform about 0.1 V), a high initial capacity of 285 mA h g−1 at 50 mA g−1, and a high rate performance (252 mA h g−1 at 100 mA g−1 and 95 mA h g−1 at 1000 mA g−1). Additionally, the FLGM-based anodes exhibited excellent cycling stability with no capacity loss after 1000 cycles at 200 mA g−1. A process of this nature which does not require substrates, and is scalable for continuous or semi-continuous production of graphene, paves the way for graphene-based energy storage devices.

A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements

Abstract: In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I-VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.

Chemical Processing of Ceramics

Abstract: Powder Synthesis and Characterization Hydrothermal Synthesis of Ceramic Oxide Powders, S. Somiya, R. Roy, and S. Komarneni Solvothermal Synthesis, M. Inoue Mechanochemical Synthesis of Ceramics, A.C. Dodd Cryochemical Synthesis of Materials, O.A. Shlyakhtin, N.N. Oleynikov, and Y.D. Tretyakov Environmentally Benign Approach to Synthesis of Titanium-Based Oxides by Use of Water-Soluble Titanium Complex, K. Tomita, D. Dey, V. Petrykin, and M. Kakihana Peroxoniobium-Mediated Route toward the Low-Temperature Synthesis of Alkali Metal Niobates Free from Organics and Chlorides, D. Dey and M. Kakihana Synthesis and Modification of Submicron Barium Titanate Powders, B.I. Lee, M. Wang, D.H. Yoon, P. Badheka, L. Qi, and L.-Q. Wang Magnetic Particles: Synthesis and Characterization, M. Ozaki Synthesis and Surface Modification of Zinc Sulfide-Based Phosphors, L. Qi, B.I. Lee, D. Morton, and E. Forsythe Characterization of Fine Dry Powders, H.K. Kammler and L. Madler Powder Processing at Nanoscale Theory and Applications of Colloidal Processing, W. Sigmund, G. Pyrgiotakis, and A. Daga Nanomicrostructure and Property Control of Single and Multiphase Materials, P. Colomban Nanocomposite Materials, S. Komarneni Molecular Engineering Route to Two Dimensional Heterostructural Nanohybrid Materials, J.-H. Choy and M. Park Nanoceramic Particulates for Chemical Mechanical Planarization in the Ultra Large Scale Integration Fabrication Process, U. Paik, S.K. Kim, T. Katoh, and J.G. Park Sol-Gel Processing Chemical Control of Defect Formation During Spin-Coating of Sol-Gels, D.P. Birnie, III Preparation and Properties of SiO2 Thin Films by the Sol-Gel Method Using Photoirradiation and Its Application to Surface Coating for Display, T. Ohishi Ceramic Via Polymers Organosilicon Polymers as Precursors for Ceramics, M. Weinmann Polymer Pyrolysis, M. Narisawa Processing of Specialty Ceramics Chemical Vapor Deposition of Ceramics, G. Cao and Y. Wang Ceramic Photonic Crystals: Materials, Synthesis, and Applications, J. DiMaio and J. Ballato Tailoring Dielectric Properties of Perovskite Ceramics at Microwave Frequencies, E.S. Kim, K.H. Yoon, and B.I. Lee Synthesis and Processing of High-Temperature Superconductors, T. Doi Synthesis of Bone-Like Hydroxyapatite/Collagen Self-Organized Nanocomposites in Chemical Processing of Ceramics, M. Kikuchi Ceramic Membrane Processing: New Approaches in Design and Applications, A. Ayral, A. Julbe, and C. Guizard Ceramic Materials for Lithium-Ion Battery Applications, J.P. Maranchi, O.I. Velikokhatnyi, M.K. Datta, I.-S. Kim, and P.N. Kumta Chemical Solution Deposition of Ferroelectric Thin Films, R. Schwartz, T. Schneller, R. Waser, and H. Dobberstein Index

Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems

Abstract: On solving a convex-concave bilinear saddle-point problem (SPP), there have been many works studying the complexity results of first-order methods. These results are all about upper complexity bounds, which can determine at most how many iterations would guarantee a solution of desired accuracy. In this paper, we pursue the opposite direction by deriving lower complexity bounds of first-order methods on large-scale SPPs. Our results apply to the methods whose iterates are in the linear span of past first-order information, as well as more general methods that produce their iterates in an arbitrary manner based on first-order information. We first work on the affinely constrained smooth convex optimization that is a special case of SPP. Different from gradient method on unconstrained problems, we show that first-order methods on affinely constrained problems generally cannot be accelerated from the known convergence rate O(1 / t) to $$O(1/t^2)$$ , and in addition, O(1 / t) is optimal for convex problems. Moreover, we prove that for strongly convex problems, $$O(1/t^2)$$ is the best possible convergence rate, while it is known that gradient methods can have linear convergence on unconstrained problems. Then we extend these results to general SPPs. It turns out that our lower complexity bounds match with several established upper complexity bounds in the literature, and thus they are tight and indicate the optimality of several existing first-order methods.

Genomic mechanisms of climate adaptation in polyploid bioenergy switchgrass.

Abstract: Long-term climate change and periodic environmental extremes threaten food and fuel security1 and global crop productivity2-4. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience5, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation6-knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass (Panicum virgatum). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate-gene-biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene-trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.

Efficient assembly of nanopore reads via highly accurate and intact error correction.

Abstract: Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection. Nanopore reads have been advantageous for de novo genome assembly; however these reads have high error rates. Here, the authors develop an error correction and de novo assembly tool, NECAT, which produces efficient, high quality assemblies of nanopore reads.

Machine learning in manufacturing and industry 4.0 applications

Abstract: The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices,...

The deal.II finite element library: Design, features, and insights

Abstract: deal.II is a state-of-the-art finite element library focused on generality, dimension-independent programming, parallelism, and extensibility. Herein, we outline its primary design considerations and its sophisticated features such as distributed meshes, h p -adaptivity, support for complex geometries, and matrix-free algorithms. But deal.II is more than just a software library: It is also a diverse and worldwide community of developers and users, as well as an educational platform. We therefore also discuss some of the technical and social challenges and lessons learned in running a large community software project over the course of two decades.

Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review

Abstract: Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information wit...

COVID-19 Outbreak and Hospital Air Quality: A Systematic Review of Evidence on Air Filtration and Recirculation.

Abstract: The outbreak of SARS-CoV-2 has made us all think critically about hospital indoor air quality and the approaches to remove, dilute, and disinfect pathogenic organisms from the hospital environment. While specific aspects of the coronavirus infectivity, spread, and routes of transmission are still under rigorous investigation, it seems that a recollection of knowledge from the literature can provide useful lessons to cope with this new situation. As a result, a systematic literature review was conducted on the safety of air filtration and air recirculation in healthcare premises. This review targeted a wide range of evidence from codes and regulations, to peer-reviewed publications, and best practice standards. The literature search resulted in 394 publications, of which 109 documents were included in the final review. Overall, even though solid evidence to support current practice is very scarce, proper filtration remains one important approach to maintain the cleanliness of indoor air in hospitals. Given the rather large physical footprint of the filtration system, a range of short-term and long-term solutions from the literature are collected. Nonetheless, there is a need for a rigorous and feasible line of research in the area of air filtration and recirculation in healthcare facilities. Such efforts can enhance the performance of healthcare facilities under normal conditions or during a pandemic. Past innovations can be adopted for the new outbreak at low-to-minimal cost.

Protection Motivation and the COVID-19 Virus.

Abstract: The current study examined the role of the components of the Protection Motivation Theory of Health (PMT) in predicting protective health behaviors related to the COVID-19 virus. Through a snowball sampling procedure, in Wave 1 424 respondents completed a survey in March 2020. One hundred thirteen of these participants completed the same survey in Wave 2 in May 2020. Consistent with research on SARS, females and older individuals engaged in the behaviors more often than men and younger individuals. After accounting for these variables in predicting frequency of protective health behaviors, components of the PMT accounted for an additional 12% of the variance in Wave 1 and 16% in Wave 2, with perceived severity and outcome efficaciousness correlating positively with frequency. Anticipatory regret mediated the relationship between PMT and protective health behavior frequency. The results suggest that public health announcements that are tailored toward the severity of the virus and the efficacy of the health behaviors in decreasing the spread of the virus may meet with more success than those that heighten people's vulnerability to the disease.

A review on resilience studies in active distribution systems

Abstract: The world has been experiencing natural disasters and man-made attacks on power system networks over the past few decades These occurrences directly affect electricity infrastructures, thereby resulting in immense economic loss The electric infrastructure is the backbone and one of the most essential components of human life Thus, a resilient infrastructure must be constructed to cope with events of high-impact, low-possibility Moreover, achieving resilience in the active distribution system (ADS) has been a vital research field of planning and operation of electric power systems The incorporation of recent breakthrough technologies, such as micro- and smart grids, can make the distribution system become considerably resilient through planning-operation activities prior, during, and after an extreme event This study offers the concepts premised on a systematic review of available literature by distinguishing characteristics between reliability and resiliency Thereafter, the most relevant proceedings in conformity with an overview of the major blackouts, hardening and its guidelines, weather-related scenarios, taxonomies, and remedial actions are discussed In addition, this research presents the planning, operational, and planning-operational attributes in response to catastrophes Furthermore, a case study is conducted to support the review work, where the reliability and resilience of the ADS (IEEE 33-bus test system) are evaluated as performance indices with and without the addition of PV units The performed research is laying out the importance of the distributed generation, such as PV, in the context of resilience, with the inclusion of different faults

Regulating Solvent Molecule Coordination with KPF6 for Superstable Graphite Potassium Anodes.

Abstract: Graphite is one of the most attractive anode materials due to its low cost, environmental friendliness, and high energy density for potassium ion batteries (PIBs). However, the severe capacity fade...

Operator learning for predicting multiscale bubble growth dynamics.

Abstract: Simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming "hand-shaking" interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh-Plesset (R-P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R-P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.

Deep Learning-based Anomaly Detection in Cyber-physical Systems: Progress and Opportunities

Abstract: Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods, which face the growing volume of data and need domain-specific knowledge, cannot be directly applied to address these challenges. To this end, deep learning-based anomaly detection (DLAD) methods have been proposed. In this article, we review state-of-the-art DLAD methods in CPSs. We propose a taxonomy in terms of the type of anomalies, strategies, implementation, and evaluation metrics to understand the essential properties of current methods. Further, we utilize this taxonomy to identify and highlight new characteristics and designs in each CPS domain. Also, we discuss the limitations and open problems of these methods. Moreover, to give users insights into choosing proper DLAD methods in practice, we experimentally explore the characteristics of typical neural models, the workflow of DLAD methods, and the running performance of DL models. Finally, we discuss the deficiencies of DL approaches, our findings, and possible directions to improve DLAD methods and motivate future research.