Showing papers by "University of Delaware published in 2020"

2019 American College of Rheumatology/Arthritis Foundation Guideline for the Management of Osteoarthritis of the Hand, Hip, and Knee

Abstract: Objective To develop an evidence-based guideline for the comprehensive management of osteoarthritis (OA) as a collaboration between the American College of Rheumatology (ACR) and the Arthritis Foundation, updating the 2012 ACR recommendations for the management of hand, hip, and knee OA. Methods We identified clinically relevant population, intervention, comparator, outcomes questions and critical outcomes in OA. A Literature Review Team performed a systematic literature review to summarize evidence supporting the benefits and harms of available educational, behavioral, psychosocial, physical, mind-body, and pharmacologic therapies for OA. Grading of Recommendations Assessment, Development and Evaluation methodology was used to rate the quality of the evidence. A Voting Panel, including rheumatologists, an internist, physical and occupational therapists, and patients, achieved consensus on the recommendations. Results Based on the available evidence, either strong or conditional recommendations were made for or against the approaches evaluated. Strong recommendations were made for exercise, weight loss in patients with knee and/or hip OA who are overweight or obese, self-efficacy and self-management programs, tai chi, cane use, hand orthoses for first carpometacarpal (CMC) joint OA, tibiofemoral bracing for tibiofemoral knee OA, topical nonsteroidal antiinflammatory drugs (NSAIDs) for knee OA, oral NSAIDs, and intraarticular glucocorticoid injections for knee OA. Conditional recommendations were made for balance exercises, yoga, cognitive behavioral therapy, kinesiotaping for first CMC OA, orthoses for hand joints other than the first CMC joint, patellofemoral bracing for patellofemoral knee OA, acupuncture, thermal modalities, radiofrequency ablation for knee OA, topical NSAIDs, intraarticular steroid injections and chondroitin sulfate for hand OA, topical capsaicin for knee OA, acetaminophen, duloxetine, and tramadol. Conclusion This guideline provides direction for clinicians and patients making treatment decisions for the management of OA. Clinicians and patients should engage in shared decision-making that accounts for patients' values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies.

TRY plant trait database : Enhanced coverage and open access

Abstract: Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Garnet-Type Solid-State Electrolytes: Materials, Interfaces, and Batteries.

Abstract: Solid-state batteries with desirable advantages, including high-energy density, wide temperature tolerance, and fewer safety-concerns, have been considered as a promising energy storage technology to replace organic liquid electrolyte-dominated Li-ion batteries. Solid-state electrolytes (SSEs) as the most critical component in solid-state batteries largely lead the future battery development. Among different types of solid-state electrolytes, garnet-type Li7La3Zr2O12 (LLZO) solid-state electrolytes have particularly high ionic conductivity (10-3 to 10-4 S/cm) and good chemical stability against Li metal, offering a great opportunity for solid-state Li-metal batteries. Since the discovery of garnet-type LLZO in 2007, there has been an increasing interest in the development of garnet-type solid-state electrolytes and all solid-state batteries. Garnet-type electrolyte has been considered one of the most promising and important solid-state electrolytes for batteries with potential benefits in energy density, electrochemical stability, high temperature stability, and safety. In this Review, we will survey recent development of garnet-type LLZO electrolytes with discussions of experimental studies and theoretical results in parallel, LLZO electrolyte synthesis strategies and modifications, stability of garnet solid electrolytes/electrodes, emerging nanostructure designs, degradation mechanisms and mitigations, and battery architectures and integrations. We will also provide a target-oriented research overview of garnet-type LLZO electrolyte and its application in various types of solid-state battery concepts (e.g., Li-ion, Li-S, and Li-air), and we will show opportunities and perspectives as guides for future development of solid electrolytes and solid-state batteries.

The physical oceanography of the transport of floating marine debris

Abstract: Marine plastic debris floating on the ocean surface is a major environmental problem. However, its distribution in the ocean is poorly mapped, and most of the plastic waste estimated to have entered the ocean from land is unaccounted for. Better understanding of how plastic debris is transported from coastal and marine sources is crucial to quantify and close the global inventory of marine plastics, which in turn represents critical information for mitigation or policy strategies. At the same time, plastic is a unique tracer that provides an opportunity to learn more about the physics and dynamics of our ocean across multiple scales, from the Ekman convergence in basin-scale gyres to individual waves in the surfzone. In this review, we comprehensively discuss what is known about the different processes that govern the transport of floating marine plastic debris in both the open ocean and the coastal zones, based on the published literature and referring to insights from neighbouring fields such as oil spill dispersion, marine safety recovery, plankton connectivity, and others. We discuss how measurements of marine plastics (both in situ and in the laboratory), remote sensing, and numerical simulations can elucidate these processes and their interactions across spatio-temporal scales.

Metal contamination and bioremediation of agricultural soils for food safety and sustainability

Abstract: Agricultural soil is a non-renewable natural resource that requires careful stewardship in order to achieve the United Nations’ Sustainable Development Goals However, industrial and agricultural activity is often detrimental to soil health and can distribute heavy metal(loid)s into the soil environment, with harmful effects on human and ecosystem health In this Review, we examine processes that can lead to the contamination of agricultural land with heavy metal(loid)s, which range from mine tailings runoff entering local irrigation channels to the atmospheric deposition of incinerator and coal-fired power-plant emissions We discuss the relationship between heavy metal(loid) biogeochemical transformations in the soil and their bioavailability We then review two biological solutions for remediation of contaminated agricultural land, plant-based remediation and microbial bioremediation, which offer cost-effective and sustainable alternatives to traditional physical or chemical remediation technologies Finally, we discuss how integrating these innovative technologies with profitable and sustainable land use could lead to green and sustainable remediation strategies, and conclude by identifying research challenges and future directions for the biological remediation of agricultural soils Contamination of agricultural soils by heavy metals and metalloids has severe consequences on human and ecosystem health This Review discusses the sources of heavy metal(loid) contamination, the mechanisms by which these contaminants interact with biological and geochemical soil elements, and plant-based and microorganism-based remediation strategies

Plasma membranes are asymmetric in lipid unsaturation, packing and protein shape.

Abstract: A fundamental feature of cellular plasma membranes (PMs) is an asymmetric lipid distribution between the bilayer leaflets. However, neither the detailed, comprehensive compositions of individual PM leaflets nor how these contribute to structural membrane asymmetries have been defined. We report the distinct lipidomes and biophysical properties of both monolayers in living mammalian PMs. Phospholipid unsaturation is dramatically asymmetric, with the cytoplasmic leaflet being approximately twofold more unsaturated than the exoplasmic leaflet. Atomistic simulations and spectroscopy of leaflet-selective fluorescent probes reveal that the outer PM leaflet is more packed and less diffusive than the inner leaflet, with this biophysical asymmetry maintained in the endocytic system. The structural asymmetry of the PM is reflected in the asymmetric structures of protein transmembrane domains. These structural asymmetries are conserved throughout Eukaryota, suggesting fundamental cellular design principles.

OpenVINS: A Research Platform for Visual-Inertial Estimation

Abstract: In this paper, we present an open platform, termed OpenVINS, for visual-inertial estimation research for both the academic community and practitioners from industry. The open sourced codebase provides a foundation for researchers and engineers to quickly start developing new capabilities for their visual-inertial systems. This codebase has out of the box support for commonly desired visual-inertial estimation features, which include: (i) on-manifold sliding window Kalman filter, (ii) online camera intrinsic and extrinsic calibration, (iii) camera to inertial sensor time offset calibration, (iv) SLAM landmarks with different representations and consistent First-Estimates Jacobian (FEJ) treatments, (v) modular type system for state management, (vi) extendable visual-inertial system simulator, and (vii) extensive toolbox for algorithm evaluation. Moreover, we have also focused on detailed documentation and theoretical derivations to support rapid development and research, which are greatly lacked in the current open sourced algorithms. Finally, we perform comprehensive validation of the proposed OpenVINS against state-of-the-art open sourced algorithms, showing its competing estimation performance.

Learning to Learn Single Domain Generalization

Abstract: We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose a new method named adversarial domain augmentation to solve this Out-of-Distribution (OOD) generalization problem. The key idea is to leverage adversarial training to create "fictitious" yet "challenging" populations, from which a model can learn to generalize with theoretical guarantees. To facilitate fast and desirable domain augmentation, we cast the model training in a meta-learning scheme and use a Wasserstein Auto-Encoder (WAE) to relax the widely used worst-case constraint. Detailed theoretical analysis is provided to testify our formulation, while extensive experiments on multiple benchmark datasets indicate its superior performance in tackling single domain generalization.

Sustainable minerals and metals for a low-carbon future

Abstract: Climate change mitigation will create new natural resource and supply chain opportunities and dilemmas, as substantial amounts of raw materials will be required to build new low carbon energy devices and infrastructure (1). Between 2015 and 2050, the global electric vehicle stock needs to jump from 1.2 million light-duty passenger cars to 965 million passenger cars; battery storage capacity needs to climb from 0.5 gigawatt -hours (GWh) to 12,380 GWh; and the amount of installed solar PV capacity must rise from 223 gigawatts (GW) to over 7,100 GW (2). The materials and metals demanded by a low-carbon economy will be immense. (3).

Time-Integrated Neutrino Source Searches with 10 Years of IceCube Data.

Abstract: This Letter presents the results from pointlike neutrino source searches using ten years of IceCube data collected between April 6, 2008 and July 10, 2018. We evaluate the significance of an astrophysical signal from a pointlike source looking for an excess of clustered neutrino events with energies typically above ∼1 TeV among the background of atmospheric muons and neutrinos. We perform a full-sky scan, a search within a selected source catalog, a catalog population study, and three stacked Galactic catalog searches. The most significant point in the northern hemisphere from scanning the sky is coincident with the Seyfert II galaxy NGC 1068, which was included in the source catalog search. The excess at the coordinates of NGC 1068 is inconsistent with background expectations at the level of 2.9σ after accounting for statistical trials from the entire catalog. The combination of this result along with excesses observed at the coordinates of three other sources, including TXS 0506+056, suggests that, collectively, correlations with sources in the northern catalog are inconsistent with background at 3.3σ significance. The southern catalog is consistent with background. These results, all based on searches for a cumulative neutrino signal integrated over the 10 years of available data, motivate further study of these and similar sources, including time-dependent analyses, multimessenger correlations, and the possibility of stronger evidence with coming upgrades to the detector.

Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways.

Abstract: Urban land expansion is one of the most visible, irreversible, and rapid types of land cover/land use change in contemporary human history, and is a key driver for many environmental and societal changes across scales. Yet spatial projections of how much and where it may occur are often limited to short-term futures and small geographic areas. Here we produce a first empirically-grounded set of global, spatial urban land projections over the 21st century. We use a data-science approach exploiting 15 diverse datasets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations. We find the global total amount of urban land could increase by a factor of 1.8–5.9, and the per capita amount by a factor of 1.1–4.9, across different socioeconomic scenarios over the century. Though the fastest urban land expansion occurs in Africa and Asia, the developed world experiences a similarly large amount of new development. Here the authors develop a set of global, long-term, spatial projections of urban land expansion for understanding the planet’s potential urban futures. The global total amount of urban land increases by a factor of 1.8-5.9 over the 21st century, and the developed world experiences as much new urban development as the developing world.

COVID-19 conspiracy beliefs, health behaviors, and policy support.

Abstract: Conspiracy theories have been proliferating during the COVID-19 pandemic. Evidence suggests that belief in conspiracy theories undermines engagement in pro-health behaviors and support for public health policies. Moreover, previous work suggests that inoculating messages from opinion leaders that expose conspiracy theories as false before people are exposed to them can help to prevent belief in new conspiracies. Goals of this study were to: (a) explore associations between COVID-19 conspiracy beliefs with SARS-CoV-2 vaccine intentions, cooperation with public health recommendations, and support for public health policies among U.S. adults and (b) investigate trusted sources of COVID-19 information to inform strategies to address conspiracy beliefs. A cross-sectional, online survey was conducted with 845 U.S. adults in April 2020. Data were analyzed using analyses of variance and multivariable regressions. One-third (33%) of participants believed one or more conspiracies about COVID-19. Participants who believed conspiracies reported that their intentions to vaccinate were 3.9 times lower and indicated less support for COVID-19 public health policies than participants who disbelieved conspiracies. There were no differences in cooperation with public health recommendations by conspiracy belief endorsement in the multivariable regression analysis. Although there were some key differences in trusted sources of COVID-19 information, doctor(s) were the most trusted source of information about COVID-19 overall with 90% of participants trusting doctor(s). Doctor(s) may play a role in addressing COVID-19 conspiracy theories before people are exposed to them to promote COVID-19 prevention efforts.

Observed and Potential Impacts of the COVID-19 Pandemic on the Environment.

Abstract: Various environmental factors influence the outbreak and spread of epidemic or even pandemic events which, in turn, may cause feedbacks on the environment. The novel coronavirus disease (COVID-19) was declared a pandemic on 13 March 2020 and its rapid onset, spatial extent and complex consequences make it a once-in-a-century global disaster. Most countries responded by social distancing measures and severely diminished economic and other activities. Consequently, by the end of April 2020, the COVID-19 pandemic has led to numerous environmental impacts, both positive such as enhanced air and water quality in urban areas, and negative, such as shoreline pollution due to the disposal of sanitary consumables. This study presents an early overview of the observed and potential impacts of the COVID-19 on the environment. We argue that the effects of COVID-19 are determined mainly by anthropogenic factors which are becoming obvious as human activity diminishes across the planet, and the impacts on cities and public health will be continued in the coming years.

Efficient and tunable one-dimensional charge transport in layered lanthanide metal-organic frameworks

Abstract: The emergence of electrically conductive metal–organic frameworks (MOFs) has led to applications in chemical sensing and electrical energy storage, among others. The most conductive MOFs are made from organic ligands and square-planar transition metal ions connected into two-dimensional (2D) sheets stacked on top of each other. Their electrical properties are thought to depend critically on the covalency of the metal–ligand bond, and less importance is given to out-of-plane charge transport. Here, we report a series of lanthanide-based MOFs that allow fine tuning of the sheet stacking. In these materials, the Ln3+ ions lie between the planes of the ligands, thus connecting organic layers into a 3D framework through lanthanide–oxygen chains. Here, efficient charge transport is found to occur primarily perpendicular to the 2D sheets. These results demonstrate that high conductivity in layered MOFs does not necessarily require a metal–ligand bond with highly covalent character, and that interactions between organic ligands alone can produce efficient charge transport pathways. High electrical conductivities in metal–organic frameworks—attractive for applications in sensing and energy storage—typically arise in layered MOFs from metal–ligand bonds with strong covalent character. Now, lanthanide-based MOFs have shown high out-of-plane conductivities originating instead from the π-stacking of organic ligands.

Iron-mediated organic matter decomposition in humid soils can counteract protection.

Abstract: Soil organic matter (SOM) is correlated with reactive iron (Fe) in humid soils, but Fe also promotes SOM decomposition when oxygen (O2) becomes limited. Here we quantify Fe-mediated OM protection vs. decomposition by adding 13C dissolved organic matter (DOM) and 57FeII to soil slurries incubated under static or fluctuating O2. We find Fe uniformly protects OM only under static oxic conditions, and only when Fe and DOM are added together: de novo reactive FeIII phases suppress DOM and SOM mineralization by 35 and 47%, respectively. Conversely, adding 57FeII alone increases SOM mineralization by 8% following oxidation to 57FeIII. Under O2 limitation, de novo reactive 57FeIII phases are preferentially reduced, increasing anaerobic mineralization of DOM and SOM by 74% and 32‒41%, respectively. Periodic O2 limitation is common in humid soils, so Fe does not intrinsically protect OM; rather reactive Fe phases require their own physiochemical protection to contribute to OM persistence.

Mandatory Non-financial Disclosure and Its Influence on CSR: An International Comparison

Abstract: The article examines the effects of non-financial disclosure (NFD) on corporate social responsibility (CSR). We conceptualise trade-offs between two ideal types (government regulation and business self-regulation) in relation to CSR. Whereas self-regulation is associated with greater flexibility for businesses to develop best practices, it can also lead to complacency if firms feel no external pressure to engage with CSR. In contrast, government regulation is associated with greater stringency around minimum standards, but can also result in rigidity owing to a ‘one-size-fits-all’ approach. Given these potential trade-offs, we ask how mandatory non-financial disclosure has been shaping CSR practices and examine its potential effectiveness as a regulatory instrument. Our analysis of 24 OECD countries using the Asset4 database shows that firms in countries that require non-financial disclosure adopt significantly more CSR activities. However, we also find that NFD regulation does not lead to lower levels of corporate irresponsibility. Furthermore, our analysis demonstrates that, over time, the variation in CSR activities declines as firms adopt increasingly similar practices. Our study thereby contributes to understanding the impact of government regulation on CSR at firm level. We also discuss the limits of mandatory NFD in addressing regulatory trade-offs between stringency and flexibility in the field of corporate social responsibility.

The Giant Radio Array for Neutrino Detection (GRAND): Science and design

Abstract: The Giant Radio Array for Neutrino Detection (GRAND) is a planned large-scale observatory of ultra-high-energy (UHE) cosmic particles, with energies exceeding 108 GeV. Its goal is to solve the long-standing mystery of the origin of UHE cosmic rays. To do this, GRAND will detect an unprecedented number of UHE cosmic rays and search for the undiscovered UHE neutrinos and gamma rays associated to them with unmatched sensitivity. GRAND will use large arrays of antennas to detect the radio emission coming from extensive air showers initiated by UHE particles in the atmosphere. Its design is modular: 20 separate, independent sub-arrays, each of 10000 radio antennas deployed over 10000 km2. A staged construction plan will validate key detection techniques while achieving important science goals early. Here we present the science goals, detection strategy, preliminary design, performance goals, and construction plans for GRAND.

Permanently Microporous Metal-Organic Polyhedra.

Abstract: As compared to porous network solids, including metal-organic frameworks, covalent-organic frameworks, porous aromatic frameworks, and zeolites, porous molecular materials are relatively unexplored. Additionally, within porous molecular space, porous organic cages (POCs) have been the most widely reported over the past decade. Relatively recently, however, porous hybrid metal-organic molecular complexes have received considerable attention with a large fraction of surface areas for these coordination cages reported over the past three years. This review focuses on advances in this area. We highlight the recent work with permanently microporous metal-organic polyhedra (MOPs). Analogous to early work in the area of MOFs, the vast majority of MOPs for which surface areas have been reported have been based on paddlewheel building units and carboxylate ligands. We describe the synthesis of porous cages and highlight those based on monometallic, bimetallic, trimetallic, tetrametallic, and higher nuclearity clusters. Finally, we showcase work wherein the porosity of MOPs has been leveraged for applications related to the storage and separation of small molecules and the incorporation of these porous and potentially porous cages into membranes.

Characteristics of the Diffuse Astrophysical Electron and Tau Neutrino Flux with Six Years of IceCube High Energy Cascade Data.

Abstract: We report on the first measurement of the astrophysical neutrino flux using particle showers (cascades) in IceCube data from 2010-2015. Assuming standard oscillations, the astrophysical neutrinos in this dedicated cascade sample are dominated (∼90%) by electron and tau flavors. The flux, observed in the sensitive energy range from 16 TeV to 2.6 PeV, is consistent with a single power-law model as expected from Fermi-type acceleration of high energy particles at astrophysical sources. We find the flux spectral index to be γ=2.53±0.07 and a flux normalization for each neutrino flavor of ϕ_{astro}=1.66_{-0.27}^{+0.25} at E_{0}=100 TeV, in agreement with IceCube's complementary muon neutrino results and with all-neutrino flavor fit results. In the measured energy range we reject spectral indices γ≤2.28 at ≥3σ significance level. Because of high neutrino energy resolution and low atmospheric neutrino backgrounds, this analysis provides the most detailed characterization of the neutrino flux at energies below ∼100 TeV compared to previous IceCube results. Results from fits assuming more complex neutrino flux models suggest a flux softening at high energies and a flux hardening at low energies (p value ≥0.06). The sizable and smooth flux measured below ∼100 TeV remains a puzzle. In order to not violate the isotropic diffuse gamma-ray background as measured by the Fermi Large Area Telescope, it suggests the existence of astrophysical neutrino sources characterized by dense environments which are opaque to gamma rays.

Switchbacks in the Near-Sun Magnetic Field: Long Memory and Impact on the Turbulence Cascade

Abstract: One of the most striking observations made by Parker Solar Probe during its first solar encounter is the omnipresence of rapid polarity reversals in a magnetic field that is otherwise mostly radial. These so-called switchbacks strongly affect the dynamics of the magnetic field. We concentrate here on their macroscopic properties. First, we find that these structures are self-similar, and have neither a characteristic magnitude, nor a characteristic duration. Their waiting time statistics shows evidence for aggregation. The associated long memory resides in their occurrence rate, and is not inherent to the background fluctuations. Interestingly, the spectral properties of inertial range turbulence differ inside and outside of switchback structures; in the latter the $1/f$ range extends to higher frequencies. These results suggest that outside of these structures we are in the presence of lower amplitude fluctuations with a shorter turbulent inertial range. We conjecture that these correspond to a pristine solar wind.

Clinical Practice Guideline to Improve Locomotor Function Following Chronic Stroke, Incomplete Spinal Cord Injury, and Brain Injury.

Abstract: Background Individuals with acute-onset central nervous system (CNS) injury, including stroke, motor incomplete spinal cord injury, or traumatic brain injury, often experience lasting locomotor deficits, as quantified by decreases in gait speed and distance walked over a specific duration (timed distance). The goal of the present clinical practice guideline was to delineate the relative efficacy of various interventions to improve walking speed and timed distance in ambulatory individuals greater than 6 months following these specific diagnoses. Methods A systematic review of the literature published between 1995 and 2016 was performed in 4 databases for randomized controlled clinical trials focused on these specific patient populations, at least 6 months postinjury and with specific outcomes of walking speed and timed distance. For all studies, specific parameters of training interventions including frequency, intensity, time, and type were detailed as possible. Recommendations were determined on the basis of the strength of the evidence and the potential harm, risks, or costs of providing a specific training paradigm, particularly when another intervention may be available and can provide greater benefit. Results Strong evidence indicates that clinicians should offer walking training at moderate to high intensities or virtual reality-based training to ambulatory individuals greater than 6 months following acute-onset CNS injury to improve walking speed or distance. In contrast, weak evidence suggests that strength training, circuit (ie, combined) training or cycling training at moderate to high intensities, and virtual reality-based balance training may improve walking speed and distance in these patient groups. Finally, strong evidence suggests that body weight-supported treadmill training, robotic-assisted training, or sitting/standing balance training without virtual reality should not be performed to improve walking speed or distance in ambulatory individuals greater than 6 months following acute-onset CNS injury to improve walking speed or distance. Discussion The collective findings suggest that large amounts of task-specific (ie, locomotor) practice may be critical for improvements in walking function, although only at higher cardiovascular intensities or with augmented feedback to increase patient's engagement. Lower-intensity walking interventions or impairment-based training strategies demonstrated equivocal or limited efficacy. Limitations As walking speed and distance were primary outcomes, the research participants included in the studies walked without substantial physical assistance. This guideline may not apply to patients with limited ambulatory function, where provision of walking training may require substantial physical assistance. Summary The guideline suggests that task-specific walking training should be performed to improve walking speed and distance in those with acute-onset CNS injury although only at higher intensities or with augmented feedback. Future studies should clarify the potential utility of specific training parameters that lead to improved walking speed and distance in these populations in both chronic and subacute stages following injury. Disclaimer These recommendations are intended as a guide for clinicians to optimize rehabilitation outcomes for persons with chronic stroke, incomplete spinal cord injury, and traumatic brain injury to improve walking speed and distance.

FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies.

Abstract: Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth's crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie's database will be maintained and continually updated as new genes are discovered. FeGenie is freely available: https://github.com/Arkadiy-Garber/FeGenie.

Massive MIMO Transmission for LEO Satellite Communications

Abstract: Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

Black-White Risk Differentials in COVID-19 (SARS-COV2) Transmission, Mortality and Case Fatality in the United States: Translational Epidemiologic Perspective and Challenges.

Abstract: Background: Social and health inequities predispose vulnerable populations to adverse morbidity and mortality outcomes of epidemics and pandemics While racial disparities in cumulative incidence (CmI) and mortality from the influenza pandemics of 1918 and 2009 implicated Blacks with survival disadvantage relative to Whites in the United States, COVID-19 currently indicates comparable disparities We aimed to: (a) assess COVID-19 CmI by race, (b) determine the Black–White case fatality (CF) and risk differentials, and (c) apply explanatory model for mortality risk differentials Methods: COVID-19 data on confirmed cases and deaths by selective states health departments were assessed using a cross-sectional ecologic design Chi-square was used for CF independence, while binomial regression model for the Black–White risk differentials Results: The COVID-19 mortality CmI indicated Blacks/AA with 34% of the total mortality in the United States, albeit their 13% population size The COVID-19 CF was higher among Blacks/AA relative to Whites; Maryland, (27% vs 25%), Wisconsin (74% vs 48%), Illinois (48% vs 42%), Chicago (59% vs 32%), Detroit (Michigan), 72% and St John the Baptist Parish (Louisiana), 79% Blacks/AA compared to Whites in Michigan were 15% more likely to die, CmI risk ratio (CmIRR) = 115, 95% CI, 101–132 Blacks/AA relative to Whites in Illinois were 13% more likely to die, CmIRR = 113, 95% CI, 093–139, while Blacks/AA compared to Whites in Wisconsin were 51% more likely to die, CmIRR = 151, 95% CI, 110–210 In Chicago, Blacks/AA were more than twice as likely to die, CmIRR = 224, 95% CI, 136–388 Conclusion: Substantial racial/ethnic disparities are observed in COVID-19 CF and mortality with Blacks/AA disproportionately affected across the United States

Block Copolymer Vitrimers.

Abstract: In this report, we merge block copolymers with vitrimers in an effort to realize the prospect of higher-order, nanoscale control over associative cross-link exchange and flow. We show the use of co...

Unmanned Aerial Vehicle Base Station (UAV-BS) Deployment With Millimeter-Wave Beamforming

Abstract: Unmanned aerial vehicle (UAV) with flexible mobility and low cost has been a promising technology for wireless communication. Thus, it can be used for wireless data collection in Internet of Things (IoT). In this article, we consider millimeter-wave (mmWave) communication on a UAV platform, where the UAV base station (UAV-BS) serves multiple ground users, which generate big sensor data. Both the deployment of the UAV-BS and the beamforming design have essential impact on the throughput of the system. Thus, we formulate a problem to maximize the achievable sum rate of all the users, subject to a minimum rate constraint for each user, a position constraint of the UAV-BS, and a constant-modulus (CM) constraint for the beamforming vector. We solve the nonconvex problem with two steps. First, by introducing the approximate beam pattern, we solve the deployment and beam gain allocation subproblem. Then, we utilize the artificial bee colony (ABC) algorithm to solve the beamforming subproblem. For the global optimization problem, we find the near-optimal position of the UAV-BS and the beamforming vector to steer toward each user, subject to an analog beamforming structure. The simulation results demonstrate that the proposed solution can achieve a more superior performance than the present random steering beamforming strategy in terms of achievable sum rate.

Performance evaluation of Botnet DDoS attack detection using machine learning

Abstract: Botnet is regarded as one of the most sophisticated vulnerability threats nowadays. A large portion of network traffic is dominated by Botnets. Botnets are conglomeration of trade PCs (Bots) which are remotely controlled by their originator (BotMaster) under a Command and-Control (C&C) foundation. They are the keys to several Internet assaults like spams, Distributed Denial of Service Attacks (DDoS), rebate distortions, malwares and phishing. To over the problem of DDoS attack, various machine learning methods typically Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT), and Unsupervised Learning (USML) (K-means, X-means etc.) were proposed. With the increasing popularity of Machine Learning in the field of Computer Security, it will be a remarkable accomplishment to carry out performance assessment of the machine learning methods given a common platform. This could assist developers in choosing a suitable method for their case studies and assist them in further research. This paper performed an experimental analysis of the machine learning methods for Botnet DDoS attack detection. The evaluation is done on the UNBS-NB 15 and KDD99 which are well-known publicity datasets for Botnet DDoS attack detection. Machine learning methods typically Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT), and Unsupervised Learning (USML) are investigated for Accuracy, False Alarm Rate (FAR), Sensitivity, Specificity, False positive rate (FPR), AUC, and Matthews correlation coefficient (MCC) of datasets. Performance of KDD99 dataset has been experimentally shown to be better as compared to the UNBS-NB 15 dataset. This validation is significant in computer security and other related fields.

Tailoring PEDOT properties for applications in bioelectronics

Abstract: The authors would like to thank Brett Moore, Adam Williamson, and Xenofon Strakosas for edits.