Showing papers by "University of New South Wales published in 2019"
Northern Arizona University1, National Institutes of Health2, University of Minnesota3, University of California, Davis4, Woods Hole Oceanographic Institution5, Massachusetts Institute of Technology6, University of Copenhagen7, University of Trento8, Chinese Academy of Sciences9, University of California, San Francisco10, University of Pennsylvania11, Pacific Northwest National Laboratory12, North Carolina State University13, University of California, San Diego14, Institute for Systems Biology15, Dalhousie University16, University of British Columbia17, Statens Serum Institut18, Anschutz Medical Campus19, University of Washington20, Michigan State University21, Stanford University22, Broad Institute23, Harvard University24, Australian National University25, University of Düsseldorf26, University of New South Wales27, Sookmyung Women's University28, San Diego State University29, Howard Hughes Medical Institute30, Cornell University31, Max Planck Society32, Colorado State University33, Google34, Syracuse University35, Webster University36, United States Department of Agriculture37, University of Arkansas for Medical Sciences38, Colorado School of Mines39, National Oceanic and Atmospheric Administration40, University of Southern Mississippi41, University of California, Merced42, Wageningen University and Research Centre43, University of Arizona44, Environment Agency45, University of Florida46, Merck & Co.47
TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.
Abstract: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. Partial support was also provided by the following: grants NIH U54CA143925 (J.G.C. and T.P.) and U54MD012388 (J.G.C. and T.P.); grants from the Alfred P. Sloan Foundation (J.G.C. and R.K.); ERCSTG project MetaPG (N.S.); the Strategic Priority Research Program of the Chinese Academy of Sciences QYZDB-SSW-SMC021 (Y.B.); the Australian National Health and Medical Research Council APP1085372 (G.A.H., J.G.C., Von Bing Yap and R.K.); the Natural Sciences and Engineering Research Council (NSERC) to D.L.G.; and the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University. All NCI coauthors were supported by the Intramural Research Program of the National Cancer Institute. S.M.G. and C. Diener were supported by the Washington Research Foundation Distinguished Investigator Award.
8,821 citations
TL;DR: This paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development ofDTs, and the major DT applications in industry and outlines the current challenges and some possible directions for future work.
Abstract: Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.
1,467 citations
Edith Cowan University1, University of Queensland2, Utah State University3, Deakin University4, Autonomous University of Barcelona5, Charles Darwin University6, Commonwealth Scientific and Industrial Research Organisation7, University of Western Australia8, Sao Paulo State University9, Griffith University10, Southern Cross University11, University of New South Wales12, University of Wollongong13, Department of Water14, The Chinese University of Hong Kong15, Spanish National Research Council16, University of Tasmania17, University of Technology, Sydney18, National University of Malaysia19, Hasanuddin University20, University of Costa Rica21, Woods Hole Research Center22, King Abdullah University of Science and Technology23
TL;DR: This assessment, the most comprehensive for any nation to-date, demonstrates the potential of conservation and restoration of VCE to underpin national policy development for reducing greenhouse gas emissions.
Abstract: Policies aiming to preserve vegetated coastal ecosystems (VCE; tidal marshes, mangroves and seagrasses) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here, we present organic carbon (C) storage in VCE across Australian climate regions and estimate potential annual CO2 emission benefits of VCE conservation and restoration. Australia contributes 5–11% of the C stored in VCE globally (70–185 Tg C in aboveground biomass, and 1,055–1,540 Tg C in the upper 1 m of soils). Potential CO2 emissions from current VCE losses are estimated at 2.1–3.1 Tg CO2-e yr-1, increasing annual CO2 emissions from land use change in Australia by 12–21%. This assessment, the most comprehensive for any nation to-date, demonstrates the potential of conservation and restoration of VCE to underpin national policy development for reducing greenhouse gas emissions. Policies aiming to preserve vegetated coastal ecosystems (VCE) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here the authors assessed organic carbon storage in VCE across Australian and the potential annual CO2 emission benefits of VCE conservation and find that Australia contributes substantially the carbon stored in VCE globally.
1,462 citations
TL;DR: The proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households and is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting.
Abstract: As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.
1,415 citations
Heidelberg University1, University of Marburg2, Queen Mary University of London3, University of Leeds4, Rutgers University5, University of New South Wales6, University of Münster7, Aarhus University8, Columbia University9, University of Chieti-Pescara10, University of Oslo11, Karolinska Institutet12, Université catholique de Louvain13, University of Sydney14, Paris Descartes University15, Royal Perth Hospital16, University of Western Australia17, University of Dundee18, Maastricht University19, Katholieke Universiteit Leuven20, Taipei Veterans General Hospital21, National Yang-Ming University22
TL;DR: In conditions such as fibromyalgia or nonspecific low-back pain, chronic pain may be conceived as a disease in its own right; in this proposal, this subgroup is called “chronic primary pain,” and in 6 other subgroups, pain is secondary to an underlying disease.
Abstract: Chronic pain is a major source of suffering. It interferes with daily functioning and often is accompanied by distress. Yet, in the International Classification of Diseases, chronic pain diagnoses are not represented systematically. The lack of appropriate codes renders accurate epidemiological investigations difficult and impedes health policy decisions regarding chronic pain such as adequate financing of access to multimodal pain management. In cooperation with the WHO, an IASP Working Group has developed a classification system that is applicable in a wide range of contexts, including pain medicine, primary care, and low-resource environments. Chronic pain is defined as pain that persists or recurs for more than 3 months. In chronic pain syndromes, pain can be the sole or a leading complaint and requires special treatment and care. In conditions such as fibromyalgia or nonspecific low-back pain, chronic pain may be conceived as a disease in its own right; in our proposal, we call this subgroup "chronic primary pain." In 6 other subgroups, pain is secondary to an underlying disease: chronic cancer-related pain, chronic neuropathic pain, chronic secondary visceral pain, chronic posttraumatic and postsurgical pain, chronic secondary headache and orofacial pain, and chronic secondary musculoskeletal pain. These conditions are summarized as "chronic secondary pain" where pain may at least initially be conceived as a symptom. Implementation of these codes in the upcoming 11th edition of International Classification of Diseases will lead to improved classification and diagnostic coding, thereby advancing the recognition of chronic pain as a health condition in its own right.
1,311 citations
TL;DR: Genome-wide analysis identifies 30 loci associated with bipolar disorder, allowing for comparisons of shared genes and pathways with other psychiatric disorders, including schizophrenia and depression.
Abstract: Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.
1,090 citations
TL;DR: A comprehensive review of recent research efforts on deep learning-based recommender systems is provided in this paper, along with a comprehensive summary of the state-of-the-art.
Abstract: With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.
1,070 citations
TL;DR: An insight into the analogies, state-of-the-art technologies, concepts, and prospects under the umbrella of perovskite materials (both inorganic-organic hybrid halideperovskites and ferroelectric perovkites) for future multifunctional energy conversion and storage devices is provided.
Abstract: An insight into the analogies, state-of-the-art technologies, concepts, and prospects under the umbrella of perovskite materials (both inorganic-organic hybrid halide perovskites and ferroelectric perovskites) for future multifunctional energy conversion and storage devices is provided. Often, these are considered entirely different branches of research; however, considering them simultaneously and holistically can provide several new opportunities. Recent advancements have highlighted the potential of hybrid perovskites for high-efficiency solar cells. The intrinsic polar properties of these materials, including the potential for ferroelectricity, provide additional possibilities for simultaneously exploiting several energy conversion mechanisms such as the piezoelectric, pyroelectric, and thermoelectric effect and electrical energy storage. The presence of these phenomena can support the performance of perovskite solar cells. The energy conversion using these effects (piezo-, pyro-, and thermoelectric effect) can also be enhanced by a change in the light intensity. Thus, there lies a range of possibilities for tuning the structural, electronic, optical, and magnetic properties of perovskites to simultaneously harvest energy using more than one mechanism to realize an improved efficiency. This requires a basic understanding of concepts, mechanisms, corresponding material properties, and the underlying physics involved with these effects.
1,015 citations
University of New South Wales1, Oregon State University2, Braunschweig University of Technology3, University of California, San Diego4, Norwegian University of Life Sciences5, University of Liverpool6, Max Planck Society7, University of Tasmania8, University of Vermont9, ETH Zurich10, Stazione Zoologica Anton Dohrn11, Montana State University12, University of Amsterdam13, University of Southern California14, Pacific Northwest National Laboratory15, University of Hawaii at Manoa16, University of California, Berkeley17, Marine Biological Laboratory18, University of California, Irvine19, University of Georgia20, California Institute of Technology21, University of Edinburgh22, Ohio State University23, University of Sydney24, University of Alberta25, Georgia Institute of Technology26, Australian Institute of Marine Science27, University of Melbourne28, University of Texas Medical Branch29, University of Queensland30
TL;DR: This Consensus Statement documents the central role and global importance of microorganisms in climate change biology and puts humanity on notice that the impact of climate change will depend heavily on responses of micro organisms, which are essential for achieving an environmentally sustainable future.
Abstract: In the Anthropocene, in which we now live, climate change is impacting most life on Earth. Microorganisms support the existence of all higher trophic life forms. To understand how humans and other life forms on Earth (including those we are yet to discover) can withstand anthropogenic climate change, it is vital to incorporate knowledge of the microbial 'unseen majority'. We must learn not just how microorganisms affect climate change (including production and consumption of greenhouse gases) but also how they will be affected by climate change and other human activities. This Consensus Statement documents the central role and global importance of microorganisms in climate change biology. It also puts humanity on notice that the impact of climate change will depend heavily on responses of microorganisms, which are essential for achieving an environmentally sustainable future.
963 citations
TL;DR: In this paper, the authors proposed a new dataset, called Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks, and evaluated the reliability of the dataset using different statistical and machine learning methods for forensics purposes.
736 citations
University of Western Australia1, Marine Biological Association of the United Kingdom2, University of Tasmania3, Dalhousie University4, Australian Research Council5, University of Canterbury6, University of Tsukuba7, Aberystwyth University8, Scottish Association for Marine Science9, University of New South Wales10, Australian Institute of Marine Science11, Barcelona Supercomputing Center12, Commonwealth Scientific and Industrial Research Organisation13, Hobart Corporation14, University of Washington15, Edith Cowan University16
TL;DR: In this article, the authors quantify trends and attributes of extreme regional ocean warming (marine heatwaves, MHWs) across all ocean basins and examine their biological impacts from species to ecosystems.
Abstract: The global ocean has warmed substantially over the past century, with far-reaching implications for marine ecosystems. Concurrent with long-term persistent warming, discrete periods of extreme regional ocean warming (marine heatwaves, MHWs) have increased in frequency. Here we quantify trends and attributes of MHWs across all ocean basins and examine their biological impacts from species to ecosystems. Multiple regions in the Pacific, Atlantic and Indian Oceans are particularly vulnerable to MHW intensification, due to the co-existence of high levels of biodiversity, a prevalence of species found at their warm range edges or concurrent non-climatic human impacts. The physical attributes of prominent MHWs varied considerably, but all had deleterious impacts across a range of biological processes and taxa, including critical foundation species (corals, seagrasses and kelps). MHWs, which will probably intensify with anthropogenic climate change, are rapidly emerging as forceful agents of disturbance with the capacity to restructure entire ecosystems and disrupt the provision of ecological goods and services in coming decades.
TL;DR: Among adults with type 2 diabetes and high CV and renal risk, linagliptin added to usual care compared with placebo added tousual care resulted in a noninferior risk of a composite CV outcome over a median 2.2 years.
Abstract: Importance Type 2 diabetes is associated with increased cardiovascular (CV) risk. Prior trials have demonstrated CV safety of 3 dipeptidyl peptidase 4 (DPP-4) inhibitors but have included limited numbers of patients with high CV risk and chronic kidney disease. Objective To evaluate the effect of linagliptin, a selective DPP-4 inhibitor, on CV outcomes and kidney outcomes in patients with type 2 diabetes at high risk of CV and kidney events. Design, Setting, and Participants Randomized, placebo-controlled, multicenter noninferiority trial conducted from August 2013 to August 2016 at 605 clinic sites in 27 countries among adults with type 2 diabetes, hemoglobin A 1c of 6.5% to 10.0%, high CV risk (history of vascular disease and urine-albumin creatinine ratio [UACR] >200 mg/g), and high renal risk (reduced eGFR and micro- or macroalbuminuria). Participants with end-stage renal disease (ESRD) were excluded. Final follow-up occurred on January 18, 2018. Interventions Patients were randomized to receive linagliptin, 5 mg once daily (n = 3494), or placebo once daily (n = 3485) added to usual care. Other glucose-lowering medications or insulin could be added based on clinical need and local clinical guidelines. Main Outcomes and Measures Primary outcome was time to first occurrence of the composite of CV death, nonfatal myocardial infarction, or nonfatal stroke. Criteria for noninferiority of linagliptin vs placebo was defined by the upper limit of the 2-sided 95% CI for the hazard ratio (HR) of linagliptin relative to placebo being less than 1.3. Secondary outcome was time to first occurrence of adjudicated death due to renal failure, ESRD, or sustained 40% or higher decrease in eGFR from baseline. Results Of 6991 enrollees, 6979 (mean age, 65.9 years; eGFR, 54.6 mL/min/1.73 m 2 ; 80.1% with UACR >30 mg/g) received at least 1 dose of study medication and 98.7% completed the study. During a median follow-up of 2.2 years, the primary outcome occurred in 434 of 3494 (12.4%) and 420 of 3485 (12.1%) in the linagliptin and placebo groups, respectively, (absolute incidence rate difference, 0.13 [95% CI, −0.63 to 0.90] per 100 person-years) (HR, 1.02; 95% CI, 0.89-1.17; P P = .62). Adverse events occurred in 2697 (77.2%) and 2723 (78.1%) patients in the linagliptin and placebo groups; 1036 (29.7%) and 1024 (29.4%) had 1 or more episodes of hypoglycemia; and there were 9 (0.3%) vs 5 (0.1%) events of adjudication-confirmed acute pancreatitis. Conclusions and Relevance Among adults with type 2 diabetes and high CV and renal risk, linagliptin added to usual care compared with placebo added to usual care resulted in a noninferior risk of a composite CV outcome over a median 2.2 years. Trial Registration ClinicalTrials.gov Identifier:NCT01897532
RMIT University1, University of Sydney2, University of Melbourne3, Hull York Medical School4, Catalan Institution for Research and Advanced Studies5, University of Barcelona6, Carlos III Health Institute7, University of Queensland8, University of New South Wales9, University of Adelaide10, University of Glasgow11, Pontifical Catholic University of Chile12, Centre for Addiction and Mental Health13, University of Toronto14, Deakin University15, Hofstra University16, Charité17, King's College London18, South London and Maudsley NHS Foundation Trust19, Manchester Academic Health Science Centre20, University of Manchester21, University of Salford22, University College London23, Dalhousie University24, Seconda Università degli Studi di Napoli25, Universidade Federal de Santa Maria26, Anglia Ruskin University27, University of Padua28, Park Centre for Mental Health29, Beth Israel Deaconess Medical Center30, The George Institute for Global Health31, Katholieke Universiteit Leuven32, National Research Council33
TL;DR: This Commission summarises advances in understanding on the topic of physical health in people with mental illness, and presents clear directions for health promotion, clinical care, and future research.
TL;DR: In this paper, the authors provide a comprehensive survey of all of these developments promoting smooth integration of UAVs into cellular networks, including the types of consumer UAV currently available off-the-shelf, the interference issues and potential solutions addressed by standardization bodies for serving aerial users with the existing terrestrial BSs, challenges and opportunities for assisting cellular communications with UAV-based flying relays and BSs.
Abstract: The rapid growth of consumer unmanned aerial vehicles (UAVs) is creating promising new business opportunities for cellular operators On the one hand, UAVs can be connected to cellular networks as new types of user equipment, therefore generating significant revenues for the operators that can guarantee their stringent service requirements On the other hand, UAVs offer the unprecedented opportunity to realize UAV-mounted flying base stations (BSs) that can dynamically reposition themselves to boost coverage, spectral efficiency, and user quality of experience Indeed, the standardization bodies are currently exploring possibilities for serving commercial UAVs with cellular networks Industries are beginning to trial early prototypes of flying BSs or user equipments, while academia is in full swing researching mathematical and algorithmic solutions to address interesting new problems arising from flying nodes in cellular networks In this paper, we provide a comprehensive survey of all of these developments promoting smooth integration of UAVs into cellular networks Specifically, we survey: 1) the types of consumer UAVs currently available off-the-shelf; 2) the interference issues and potential solutions addressed by standardization bodies for serving aerial users with the existing terrestrial BSs; 3) the challenges and opportunities for assisting cellular communications with UAV-based flying relays and BSs; 4) the ongoing prototyping and test bed activities; 5) the new regulations being developed to manage the commercial use of UAVs; and 6) the cyber-physical security of UAV-assisted cellular communications
Daniel Taliun1, Daniel N. Harris2, Michael D. Kessler2, Jedidiah Carlson1 +191 more•Institutions (61)
TL;DR: The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation as well as resources and early insights from the sequence data.
Abstract: Summary paragraph The Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency
King Mongkut's University of Technology Thonburi1, Ferdowsi University of Mashhad2, Xi'an Jiaotong University3, University of Monastir4, Shahid Beheshti University5, University of Rennes6, Clarkson University7, North Carolina State University8, University of Vermont9, Iran University of Science and Technology10, University of New South Wales11, Royal Society12, King Abdulaziz University13, Quaid-i-Azam University14, University of Tehran15, Babeș-Bolyai University16
TL;DR: A review of the latest developments in modeling of nanofluid flows and heat transfer with an emphasis on 3D simulations can be found in this paper, where the main models used to calculate the thermophysical properties of Nanofluids are reviewed.
TL;DR: This paper presents a new method for product design based on the digital twin approach, which places emphasis on the analysis of physical data rather than the virtual models and illustrates the application of the proposed DTPD method.
Abstract: With the advent of new generation information technologies in industry and product design, the big data-driven product design era has arrived. However, the big data-driven product design mainly pla...
TL;DR: Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture as discussed by the authors, which had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist.
Abstract: Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture. Our knowledge of many of the characteristics of working memory, long-term memory and the relations between them had been well-established for many decades prior to the introduction of the theory. Curiously, this knowledge had had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist. In contrast, cognitive load theory emphasised that all novel information first is processed by a capacity and duration limited working memory and then stored in an unlimited long-term memory for later use. Once information is stored in long-term memory, the capacity and duration limits of working memory disappear transforming our ability to function. By the late 1990s, sufficient data had been collected using the theory to warrant an extended analysis resulting in the publication of Sweller et al. (Educational Psychology Review, 10, 251–296, 1998). Extensive further theoretical and empirical work have been carried out since that time and this paper is an attempt to summarise the last 20 years of cognitive load theory and to sketch directions for future research.
TL;DR: Density functional theory studies reveal that the neighboring Ni-Fe centers not only function in synergy to decrease the reaction barrier for the formation of COOH* and desorption of CO, but also undergo distinct structural evolution into a CO-adsorbed moiety upon CO2 uptake.
Abstract: Polynary single-atom structures can combine the advantages of homogeneous and heterogeneous catalysts while providing synergistic functions based on different molecules and their interfaces. However, the fabrication and identification of such an active-site prototype remain elusive. Here we report isolated diatomic Ni-Fe sites anchored on nitrogenated carbon as an efficient electrocatalyst for CO2 reduction. The catalyst exhibits high selectivity with CO Faradaic efficiency above 90 % over a wide potential range from -0.5 to -0.9 V (98 % at -0.7 V), and robust durability, retaining 99 % of its initial selectivity after 30 hours of electrolysis. Density functional theory studies reveal that the neighboring Ni-Fe centers not only function in synergy to decrease the reaction barrier for the formation of COOH* and desorption of CO, but also undergo distinct structural evolution into a CO-adsorbed moiety upon CO2 uptake.
TL;DR: The authors suggest that with the potential of these nanomaterials in sensing more research is needed on understanding their optical properties and why the synthetic methods influence their properties so much, into methods of surface functionalization that provide greater selectivity in sensing and into new sensing concepts that utilise the virtues of these nano-materials.
Abstract: Carbon and graphene quantum dots (CQDs and GQDs), known as zero-dimensional (0D) nanomaterials, have been attracting increasing attention in sensing and bioimaging. Their unique electronic, fluores...
University of Texas MD Anderson Cancer Center1, University of Chicago2, Roswell Park Cancer Institute3, Seattle Cancer Care Alliance4, University of Southern Denmark5, University of New South Wales6, Jikei University School of Medicine7, University of Oklahoma8, Kaplan Medical Center9, University of Utah10, Texas Oncology11, University of Pennsylvania12, University of Ulsan13, University of Alabama at Birmingham14, University of Oxford15, Washington University in St. Louis16, Kaiser Permanente17, Memorial Sloan Kettering Cancer Center18, AbbVie19
TL;DR: Across all trial populations, a regimen of carboplatin, paclitaxel, and veliparib induction therapy followed by veliporib maintenance therapy led to significantly longer progression-free survival than carboplati plus pac Litaxel induction therapy alone.
Abstract: Background Data are limited regarding the use of poly(adenosine diphosphate [ADP]–ribose) polymerase inhibitors, such as veliparib, in combination with chemotherapy followed by maintenance...
TL;DR: This paper highlights three different energy harvester models, namely, one linear model and two nonlinear models, and shows how WIPT designs differ for each of them in single-user and multi-user deployments, and identifies the fundamental tradeoff between conveying information and power wirelessly.
Abstract: Radio waves carry both energy and information simultaneously. Nevertheless, radio-frequency (RF) transmissions of these quantities have traditionally been treated separately. Currently, the community is experiencing a paradigm shift in wireless network design, namely, unifying wireless transmission of information and power so as to make the best use of the RF spectrum and radiation as well as the network infrastructure for the dual purpose of communicating and energizing. In this paper, we review and discuss recent progress in laying the foundations of the envisioned dual purpose networks by establishing a signal theory and design for wireless information and power transmission (WIPT) and identifying the fundamental tradeoff between conveying information and power wirelessly. We start with an overview of WIPT challenges and technologies, namely, simultaneous WIPT (SWIPT), wirelessly powered communication networks (WPCNs), and wirelessly powered backscatter communication (WPBC). We then characterize energy harvesters and show how WIPT signal and system designs crucially revolve around the underlying energy harvester model. To that end, we highlight three different energy harvester models, namely, one linear model and two nonlinear models, and show how WIPT designs differ for each of them in single-user and multi-user deployments. Topics discussed include rate-energy region characterization, transmitter and receiver architectures, waveform design, modulation, beamforming and input distribution optimizations, resource allocation, and RF spectrum use. We discuss and check the validity of the different energy harvester models and the resulting signal theory and design based on circuit simulations, prototyping, and experimentation. We also point out numerous directions that are promising for future research.
University of Texas Southwestern Medical Center1, Queen's University2, Laval University3, Osaka University4, Ben-Gurion University of the Negev5, VU University Amsterdam6, University of Milan7, University of São Paulo8, Harvard University9, University of Surrey10, University of Padua11, University of New South Wales12, University of Colorado Denver13
TL;DR: The evidence for visceral adiposity and ectopic fat as emerging risk factors for type 2 diabetes, atherosclerosis, and cardiovascular disease, with a focus on practical recommendations for health professionals and future directions for research and clinical practice is summarised.
University of Sydney1, Katholieke Universiteit Leuven2, Maastricht University3, University of Marburg4, Queen Mary University of London5, Rutgers University6, University of New South Wales7, University of Münster8, University of Chieti-Pescara9, University of Paris10, Aarhus University11, Karolinska Institutet12, Heidelberg University13
TL;DR: The goal here is to create a classification that is useful in both primary care and specialized pain management settings for the development of individualized management plans, and to assist both clinicians and researchers by providing a more accurate description of each diagnostic category.
Abstract: This article describes a proposal for the new diagnosis of chronic primary pain (CPP) in ICD-11. Chronic primary pain is chosen when pain has persisted for more than 3 months and is associated with significant emotional distress and/or functional disability, and the pain is not better accounted for by another condition. As with all pain, the article assumes a biopsychosocial framework for understanding CPP, which means all subtypes of the diagnosis are considered to be multifactorial in nature, with biological, psychological, and social factors contributing to each. Unlike the perspectives found in DSM-5 and ICD-10, the diagnosis of CPP is considered to be appropriate independently of identified biological or psychological contributors, unless another diagnosis would better account for the presenting symptoms. Such other diagnoses are called "chronic secondary pain" where pain may at least initially be conceived as a symptom secondary to an underlying disease. The goal here is to create a classification that is useful in both primary care and specialized pain management settings for the development of individualized management plans, and to assist both clinicians and researchers by providing a more accurate description of each diagnostic category.
German Center for Neurodegenerative Diseases1, University of Tübingen2, Washington University in St. Louis3, University Hospital of Basel4, Ludwig Maximilian University of Munich5, Florey Institute of Neuroscience and Mental Health6, Edith Cowan University7, University of New South Wales8, Neuroscience Research Australia9, University College London10, Mayo Clinic11, Brown University12, Indiana University13, University of Southern California14, Columbia University Medical Center15, Harvard University16, Icahn School of Medicine at Mount Sinai17
TL;DR: Serum NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer’s disease, which supports its potential utility as a clinically useful biomarker.
Abstract: Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer’s disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini–Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer’s disease, which supports its potential utility as a clinically useful biomarker.
TL;DR: A detailed classification of CRISPR/Cas biosensing systems is provided and they have the potential to become promising candidates for next-generation diagnostic biosensing platforms.
TL;DR: This work is expected to drive and benefit future research to rationally design surface strategies with multi-parameter synergistic impacts on the selectivity, activity and stability of next-generation CO2 reduction catalysts, thus opening new avenues for sustainable solutions to climate change, energy and environmental issues, and the potential industrial economy.
Abstract: Redox catalysis, including photocatalysis and (photo)electrocatalysis, may alleviate global warming and energy crises by removing excess CO2 from the atmosphere and converting it to value-added resources. Nano-to-atomic two-dimensional (2D) materials, clusters and single atoms are superior catalysts because of their engineerable ultrathin/small dimensions and large surface areas and have attracted worldwide research interest. Given the current gap between research and applications in CO2 reduction, our review systematically and constructively discusses nano-to-atomic surface strategies for catalysts reported to date. This work is expected to drive and benefit future research to rationally design surface strategies with multi-parameter synergistic impacts on the selectivity, activity and stability of next-generation CO2 reduction catalysts, thus opening new avenues for sustainable solutions to climate change, energy and environmental issues, and the potential industrial economy.
University of Oklahoma1, European Institute of Oncology2, Sungkyunkwan University3, Hebron University4, University of New South Wales5, University of Bordeaux6, University of Paris-Sud7, Netherlands Cancer Institute8, University of Edinburgh9, University of Toronto10, University of Texas MD Anderson Cancer Center11, Memorial Sloan Kettering Cancer Center12, Medical College of Wisconsin13, Harvard University14, AstraZeneca15
TL;DR: Standard-of-care therapy for patients newly diagnosed with advanced ovarian cancer consists of frontline cytoreductive surgery and adjuvant platinum chemotherapy and approximately 70% of patients have a relapse within 3 years.
Abstract: (ed from N Engl J Med 2018;379:2495–2505)Standard-of-care therapy for patients newly diagnosed with advanced ovarian cancer consists of frontline cytoreductive surgery and adjuvant platinum chemotherapy. Approximately 70% of these patients have a relapse within 3 years.