Showing papers by "University of Luxembourg published in 2018"
TL;DR: This review discusses the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates, proteins, plant polyphenols, bile acids, and vitamins, and the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism.
Abstract: The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.
1,294 citations
TL;DR: SchNet as mentioned in this paper is a deep learning architecture specifically designed to model atomistic systems by making use of continuous-filter convolutional layers, where the model learns chemically plausible embeddings of atom types across the periodic table.
Abstract: Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.
1,104 citations
TL;DR: This paper identifies and provides a detailed description of various potential emerging technologies for the fifth generation communications with SWIPT/WPT and provides some interesting research challenges and recommendations with the objective of stimulating future research in this emerging domain.
Abstract: Initial efforts on wireless power transfer (WPT) have concentrated toward long-distance transmission and high power applications. Nonetheless, the lower achievable transmission efficiency and potential health concerns arising due to high power applications, have caused limitations in their further developments. Due to tremendous energy consumption growth with ever-increasing connected devices, alternative wireless information and power transfer techniques have been important not only for theoretical research but also for the operational costs saving and for the sustainable growth of wireless communications. In this regard, radio frequency energy harvesting (RF-EH) for a wireless communications system presents a new paradigm that allows wireless nodes to recharge their batteries from the RF signals instead of fixed power grids and the traditional energy sources. In this approach, the RF energy is harvested from ambient electromagnetic sources or from the sources that directionally transmit RF energy for EH purposes. Notable research activities and major advances have occurred over the last decade in this direction. Thus, this paper provides a comprehensive survey of the state-of-art techniques, based on advances and open issues presented by simultaneous wireless information and power transfer (SWIPT) and WPT assisted technologies. More specifically, in contrast to the existing works, this paper identifies and provides a detailed description of various potential emerging technologies for the fifth generation communications with SWIPT/WPT. Moreover, we provide some interesting research challenges and recommendations with the objective of stimulating future research in this emerging domain.
621 citations
27 May 2018
TL;DR: The paper provides a comprehensive classification of code issues in Solidity and implements SmartCheck -- an extensible static analysis tool that detects them and reflects the current state of knowledge on Solidity vulnerabilities and shows significant improvements over alternatives.
Abstract: Ethereum is a major blockchain-based platform for smart contracts - Turing complete programs that are executed in a decentralized network and usually manipulate digital units of value. Solidity is the most mature high-level smart contract language. Ethereum is a hostile execution environment, where anonymous attackers exploit bugs for immediate financial gain. Developers have a very limited ability to patch deployed contracts. Hackers steal up to tens of millions of dollars from flawed contracts, a well-known example being "The DAO", broken in June 2016. Advice on secure Ethereum programming practices is spread out across blogs, papers, and tutorials. Many sources are outdated due to a rapid pace of development in this field. Automated vulnerability detection tools, which help detect potentially problematic language constructs, are still underdeveloped in this area. We provide a comprehensive classification of code issues in Solidity and implement SmartCheck -- an extensible static analysis tool that detects them. The source code is available at https://github.com/smartdec/smartcheck. SmartCheck translates Solidity source code into an XML-based intermediate representation and checks it against XPath patterns. We evaluated our tool on a big dataset of real-world contracts and compared the results with manual audit on three contracts. Our tool reflects the current state of knowledge on Solidity vulnerabilities and shows significant improvements over alternatives. SmartCheck has its limitations, as detection of some bugs requires more sophisticated techniques such as taint analysis or even manual audit. We believe though that a static analyzer should be an essential part of contract developers' toolbox, letting them fix simple bugs fast and allocate more effort to complex issues.
479 citations
TL;DR: Recon3D is presented, a computational resource that includes three-dimensional metabolite and protein structure data and enables integrated analyses of metabolic functions in humans and is used to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs.
Abstract: Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.
454 citations
TL;DR: A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.
Abstract: Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.
445 citations
TL;DR: It is unknown whether differences in the abundances of specific bacterial taxa can be observed in individuals at high risk, for example, with idiopathic rapid eye movement sleep behavior disorder, a prodromal condition of α‐synuclein aggregation disorders including PD.
Abstract: Background
Increasing evidence connects the gut microbiota and the onset and/or phenotype of Parkinson's disease (PD). Differences in the abundances of specific bacterial taxa have been reported in PD patients. It is, however, unknown whether these differences can be observed in individuals at high risk, for example, with idiopathic rapid eye movement sleep behavior disorder, a prodromal condition of α-synuclein aggregation disorders including PD.
Objectives
To compare microbiota in carefully preserved nasal wash and stool samples of subjects with idiopathic rapid eye movement sleep behavior disorder, manifest PD, and healthy individuals.
Methods
Microbiota of flash-frozen stool and nasal wash samples from 76 PD patients, 21 idiopathic rapid eye movement sleep behavior disorder patients, and 78 healthy controls were assessed by 16S and 18S ribosomal RNA amplicon sequencing. Seventy variables, related to demographics, clinical parameters including nonmotor symptoms, and sample processing, were analyzed in relation to microbiome variability and controlled differential analyses were performed.
Results
Differentially abundant gut microbes, such as Akkermansia, were observed in PD, but no strong differences in nasal microbiota. Eighty percent of the differential gut microbes in PD versus healthy controls showed similar trends in idiopathic rapid eye movement sleep behavior disorder, for example, Anaerotruncus and several Bacteroides spp., and correlated with nonmotor symptoms. Metagenomic sequencing of select samples enabled the reconstruction of genomes of so far uncharacterized differentially abundant organisms.
Conclusion
Our study reveals differential abundances of gut microbial taxa in PD and its prodrome idiopathic rapid eye movement sleep behavior disorder in comparison to the healthy controls, and highlights the potential of metagenomics to identify and characterize microbial taxa, which are enriched or depleted in PD and/or idiopathic rapid eye movement sleep behavior disorder. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
383 citations
TL;DR: The authors perform genome-wide association studies for 3 broad and 7 subtypes of epilepsy and identify 16 loci - 11 novel - that are further annotated by eQTL and partitioned heritability analyses that provide leads for epilepsy therapies based on underlying pathophysiology.
Abstract: The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.
299 citations
TL;DR: In this review, a general summary and evaluation of the applications ofPBAs for rechargeable batteries are given and an outlook toward the further development of PBAs in electrochemical energy storage is included.
281 citations
TL;DR: In this article, the authors discuss the extensive studies on nickelate heterostructures as well as outline different approaches to tune and control their physical properties and, finally, review application concepts for future devices.
Abstract: This review stands in the larger framework of functional materials by focussing on heterostructures of rare-earth nickelates, described by the chemical formula RNiO3 where R is a trivalent rare-earth R = La, Pr, Nd, Sm, …, Lu. Nickelates are characterized by a rich phase diagram of structural and physical properties and serve as a benchmark for the physics of phase transitions in correlated oxides where electron-lattice coupling plays a key role. Much of the recent interest in nickelates concerns heterostructures, that is single layers of thin film, multilayers or superlattices, with the general objective of modulating their physical properties through strain control, confinement or interface effects. We will discuss the extensive studies on nickelate heterostructures as well as outline different approaches to tuning and controlling their physical properties and, finally, review application concepts for future devices.
280 citations
University of Bonn1, University of Luxembourg2, ETH Zurich3, University of Memphis4, Max Planck Society5, Katholieke Universiteit Leuven6, Harvard University7, Novartis8, University of Toronto9, RWTH Aachen University10, University of Tübingen11, Bosch12, University of Regensburg13, Georgia Institute of Technology14, Pfizer15, University of Ljubljana16
TL;DR: The potential of state-of-the-art data science approaches for personalized medicine is reviewed, open challenges are discussed, and directions that may help to overcome them in the future are highlighted.
Abstract: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
03 Dec 2018
TL;DR: In this article, a framework that combines symbolic execution and taint analysis is proposed to find integer bugs in Ethereum smart contracts, a class of bugs that is particularly difficult to avoid due to some characteristics of the Ethereum Virtual Machine and the Solidity programming language.
Abstract: The capability of executing so-called smart contracts in a decentralised manner is one of the compelling features of modern blockchains. Smart contracts are fully fledged programs which cannot be changed once deployed to the blockchain. They typically implement the business logic of distributed apps and carry billions of dollars worth of coins. In that respect, it is imperative that smart contracts are correct and have no vulnerabilities or bugs. However, research has identified different classes of vulnerabilities in smart contracts, some of which led to prominent multi-million dollar fraud cases. In this paper we focus on vulnerabilities related to integer bugs, a class of bugs that is particularly difficult to avoid due to some characteristics of the Ethereum Virtual Machine and the Solidity programming language. In this paper we introduce Osiris -- a framework that combines symbolic execution and taint analysis, in order to accurately find integer bugs in Ethereum smart contracts. Osiris detects a greater range of bugs than existing tools, while providing a better specificity of its detection. We have evaluated its performance on a large experimental dataset containing more than 1.2 million smart contracts. We found that 42,108 contracts contain integer bugs. Besides being able to identify several vulnerabilities that have been reported in the past few months, we were also able to identify a yet unknown critical vulnerability in a couple of smart contracts that are currently deployed on the Ethereum blockchain.
TL;DR: DynaMOSA is presented, a novel many-objective solver specifically designed to address the test case generation problem in the context of coverage testing and outperforms its predecessor MOSA for all the three coverage criteria.
Abstract: The test case generation is intrinsically a multi-objective problem, since the goal is covering multiple test targets (e.g., branches). Existing search-based approaches either consider one target at a time or aggregate all targets into a single fitness function (whole-suite approach). Multi and many-objective optimisation algorithms (MOAs) have never been applied to this problem, because existing algorithms do not scale to the number of coverage objectives that are typically found in real-world software. In addition, the final goal for MOAs is to find alternative trade-off solutions in the objective space, while in test generation the interesting solutions are only those test cases covering one or more uncovered targets. In this paper, we present Dynamic Many-Objective Sorting Algorithm (DynaMOSA), a novel many-objective solver specifically designed to address the test case generation problem in the context of coverage testing. DynaMOSA extends our previous many-objective technique Many-Objective Sorting Algorithm (MOSA) with dynamic selection of the coverage targets based on the control dependency hierarchy. Such extension makes the approach more effective and efficient in case of limited search budget. We carried out an empirical study on 346 Java classes using three coverage criteria (i.e., statement, branch, and strong mutation coverage) to assess the performance of DynaMOSA with respect to the whole-suite approach (WS), its archive-based variant (WSA) and MOSA. The results show that DynaMOSA outperforms WSA in 28 percent of the classes for branch coverage (+8 percent more coverage on average) and in 27 percent of the classes for mutation coverage (+11 percent more killed mutants on average). It outperforms WS in 51 percent of the classes for statement coverage, leading to +11 percent more coverage on average. Moreover, DynaMOSA outperforms its predecessor MOSA for all the three coverage criteria in 19 percent of the classes with +8 percent more code coverage on average.
Broad Institute1, Harvard University2, University of Antwerp3, Leipzig University4, Boğaziçi University5, Carol Davila University of Medicine and Pharmacy6, University of Florence7, Children's Hospital of Philadelphia8, Utrecht University9, University of Helsinki10, University of Luxembourg11, University of Kiel12, University of Southern Denmark13, University of Genoa14, Boston Children's Hospital15, University of Tübingen16, Epilepsy Society17, UCL Institute of Neurology18
TL;DR: Analysis of individuals with neurodevelopmental disorders (NDDs) with epilepsy identifies 33 genes with a significant excess of de novo variants, of which SNAP25 and GABRB2 had previously only limited evidence of disease association.
Abstract: Epilepsy is a frequent feature of neurodevelopmental disorders (NDDs), but little is known about genetic differences between NDDs with and without epilepsy. We analyzed de novo variants (DNVs) in 6,753 parent-offspring trios ascertained to have different NDDs. In the subset of 1,942 individuals with NDDs with epilepsy, we identified 33 genes with a significant excess of DNVs, of which SNAP25 and GABRB2 had previously only limited evidence of disease association. Joint analysis of all individuals with NDDs also implicated CACNA1E as a novel disease-associated gene. Comparing NDDs with and without epilepsy, we found missense DNVs, DNVs in specific genes, age of recruitment, and severity of intellectual disability to be associated with epilepsy. We further demonstrate the extent to which our results affect current genetic testing as well as treatment, emphasizing the benefit of accurate genetic diagnosis in NDDs with epilepsy.
27 May 2018
TL;DR: This work proposes an automated testing algorithm that builds on learnable evolutionary algorithms that outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm.
Abstract: Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures.
University of Lübeck1, University of Geneva2, Geneva College3, University of Luxembourg4, Imperial College London5, Eötvös Loránd University6, University of Adelaide7, Heidelberg University8, Yale University9, University of Queensland10, Auckland University of Technology11, All India Institute of Medical Sciences12, Prince of Songkla University13, Erwin L. Hahn Institute for Magnetic Resonance Imaging14, University of Duisburg-Essen15, Hong Kong Department of Health16, McGill University17, National Drug and Alcohol Research Centre18, University of Hertfordshire19, Hertfordshire Partnership University NHS Foundation Trust20, Iowa State University21, Nottingham Trent University22, University of Amsterdam23, Central South University24, University of Calgary25, Li Ka Shing Faculty of Medicine, University of Hong Kong26, Catholic University of Korea27, University of Connecticut28, Tehran University of Medical Sciences29, Dresden University of Technology30, University of Toronto31, Centre for Addiction and Mental Health32, National Institute of Mental Health and Neurosciences33, University of Cape Town34, University of Mainz35
TL;DR: It is repeated that including GD reflects the essence of the ICD and will facilitate treatment and prevention for those who need it and the decision whether or not to include GD is based on clinical evidence and public health needs.
Abstract: The proposed introduction of gaming disorder (GD) in the 11th revision of the International Classification of Diseases (ICD-11) developed by the World Health Organization (WHO) has led to a lively debate over the past year. Besides the broad support for the decision in the academic press, a recent publication by van Rooij et al. (2018) repeated the criticism raised against the inclusion of GD in ICD-11 by Aarseth et al. (2017). We argue that this group of researchers fails to recognize the clinical and public health considerations, which support the WHO perspective. It is important to recognize a range of biases that may influence this debate; in particular, the gaming industry may wish to diminish its responsibility by claiming that GD is not a public health problem, a position which maybe supported by arguments from scholars based in media psychology, computer games research, communication science, and related disciplines. However, just as with any other disease or disorder in the ICD-11, the decision whether or not to include GD is based on clinical evidence and public health needs. Therefore, we reiterate our conclusion that including GD reflects the essence of the ICD and will facilitate treatment and prevention for those who need it.
TL;DR: Big data analytics to advance edge caching capability is proposed, which is considered as a promising approach to improve network efficiency and alleviate the high demand for the radio resource in future networks.
Abstract: The unprecedented growth of wireless data traffic not only challenges the design and evolution of the wireless network architecture, but also brings about profound opportunities to drive and improve future networks. Meanwhile, the evolution of communications and computing technologies can make the network edge, such as BSs or UEs, become intelligent and rich in terms of computing and communications capabilities, which intuitively enables big data analytics at the network edge. In this article, we propose to explore big data analytics to advance edge caching capability, which is considered as a promising approach to improve network efficiency and alleviate the high demand for the radio resource in future networks. The learning-based approaches for network edge caching are discussed, where a vast amount of data can be harnessed for content popularity estimation and proactive caching strategy design. An outlook of research directions, challenges, and opportunities is provided and discussed in depth. To validate the proposed solution, a case study and a performance evaluation are presented. Numerical studies show that several gains are achieved by employing learning- based schemes for edge caching.
University of Cambridge1, University of Hertfordshire2, Hertfordshire Partnership University NHS Foundation Trust3, Eötvös Loránd University4, University of Cape Town5, Yale University6, University of Zurich7, University of Duisburg-Essen8, Erwin L. Hahn Institute for Magnetic Resonance Imaging9, University Hospital of Lausanne10, University of Luxembourg11, Tel Aviv University12, University of Adelaide13, University of Chicago14, Monash University15, Stanford University16, University of Milan17, University of Lübeck18, Albert Einstein College of Medicine19, University of Amsterdam20, Bellvitge University Hospital21, Sheba Medical Center22, Lithuanian University of Health Sciences23, McMaster University24, University of Florence25
TL;DR: Nine critical and achievable research priorities identified by the Network, needed in order to advance understanding of PUI, with a view towards identifying vulnerable individuals for early intervention are described.
TL;DR: A novel communication framework to enable CST in DSS systems by employing a power control-based SI mitigation scheme is proposed and a throughput performance analysis of this proposed framework is carried out.
Abstract: Full-duplex (FD) wireless technology enables a radio to transmit and receive on the same frequency band at the same time, and it is considered to be one of the candidate technologies for the fifth generation (5G) and beyond wireless communication systems due to its advantages, including potential doubling of the capacity and increased spectrum utilization efficiency. However, one of the main challenges of FD technology is the mitigation of strong self-interference (SI). Recent advances in different SI cancellation techniques, such as antenna cancellation, analog cancellation, and digital cancellation methods, have led to the feasibility of using FD technology in different wireless applications. Among potential applications, one important application area is dynamic spectrum sharing (DSS) in wireless systems particularly 5G networks, where FD can provide several benefits and possibilities such as concurrent sensing and transmission (CST), concurrent transmission and reception, improved sensing efficiency and secondary throughput, and the mitigation of the hidden terminal problem. In this direction, first, starting with a detailed overview of FD-enabled DSS, we provide a comprehensive survey of recent advances in this domain. We then highlight several potential techniques for enabling FD operation in DSS wireless systems. Subsequently, we propose a novel communication framework to enable CST in DSS systems by employing a power control-based SI mitigation scheme and carry out the throughput performance analysis of this proposed framework. Finally, we discuss some open research issues and future directions with the objective of stimulating future research efforts in the emerging FD-enabled DSS wireless systems.
TL;DR: It is shown that caesarean section delivery can affect the transmission of specific microbial strains and the immunomodulatory potential of the microbiota and linked functional repertoires and immune-stimulatory potential during a critical window for neonatal immune system priming.
Abstract: The rate of caesarean section delivery (CSD) is increasing worldwide. It remains unclear whether disruption of mother-to-neonate transmission of microbiota through CSD occurs and whether it affects human physiology. Here we perform metagenomic analysis of earliest gut microbial community structures and functions. We identify differences in encoded functions between microbiomes of vaginally delivered (VD) and CSD neonates. Several functional pathways are over-represented in VD neonates, including lipopolysaccharide (LPS) biosynthesis. We link these enriched functions to individual-specific strains, which are transmitted from mothers to neonates in case of VD. The stimulation of primary human immune cells with LPS isolated from early stool samples of VD neonates results in higher levels of tumour necrosis factor (TNF-α) and interleukin 18 (IL-18). Accordingly, the observed levels of TNF-α and IL-18 in neonatal blood plasma are higher after VD. Taken together, our results support that CSD disrupts mother-to-neonate transmission of specific microbial strains, linked functional repertoires and immune-stimulatory potential during a critical window for neonatal immune system priming. The effects of caesarean section delivery on mother-to-neonate transmission of microbiota are unclear. Here the authors show that caesarean section delivery can affect the transmission of specific microbial strains and the immunomodulatory potential of the microbiota.
TL;DR: It is shown that microglia isolated from LPS‐injected mice display a global downregulation of their homeostatic signature together with an upregulation of inflammatory genes, which greatly differ from neurodegenerative disease‐associated profiles.
Abstract: Microglia are specialized parenchymal-resident phagocytes of the central nervous system (CNS) that actively support, defend and modulate the neural environment. Dysfunctional microglial responses are thought to worsen CNS diseases; nevertheless, their impact during neuroinflammatory processes remains largely obscure. Here, using a combination of single-cell RNA sequencing and multicolour flow cytometry, we comprehensively profile microglia in the brain of lipopolysaccharide (LPS)-injected mice. By excluding the contribution of other immune CNS-resident and peripheral cells, we show that microglia isolated from LPS-injected mice display a global downregulation of their homeostatic signature together with an upregulation of inflammatory genes. Notably, we identify distinct microglial activated profiles under inflammatory conditions, which greatly differ from neurodegenerative disease-associated profiles. These results provide insights into microglial heterogeneity and establish a resource for the identification of specific phenotypes in CNS disorders, such as neuroinflammatory and neurodegenerative diseases.
TL;DR: In this article, a combination of physics-based potentials with machine learning (ML) is proposed to handle new molecules and conformations without explicit prior parametrization, which is transferable across small neutral organic and biologically relevant molecules.
Abstract: Classical intermolecular potentials typically require an extensive parametrization procedure for any new compound considered. To do away with prior parametrization, we propose a combination of physics-based potentials with machine learning (ML), coined IPML, which is transferable across small neutral organic and biologically relevant molecules. ML models provide on-the-fly predictions for environment-dependent local atomic properties: electrostatic multipole coefficients (significant error reduction compared to previously reported), the population and decay rate of valence atomic densities, and polarizabilities across conformations and chemical compositions of H, C, N, and O atoms. These parameters enable accurate calculations of intermolecular contributions—electrostatics, charge penetration, repulsion, induction/polarization, and many-body dispersion. Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All l...
TL;DR: For the first time, a highly accurate and fully automated method for electrode reconstruction that considers curved trajectories is presented that is made publicly available and constitutes an important step towards future integration of lead reconstruction into standard clinical care.
TL;DR: In this paper, the lattice constants, cohesive energies, and bulk moduli of 64 solids using six functionals, representing the local, semi-local, and hybrid DFAs on the first four rungs of Jacob's ladder.
Abstract: Accurate and careful benchmarking of different density-functional approximations (DFAs) represents an important source of information for understanding DFAs and how to improve them. In this work we have studied the lattice constants, cohesive energies, and bulk moduli of 64 solids using six functionals, representing the local, semi-local, and hybrid DFAs on the first four rungs of Jacob’s ladder. The set of solids considered consists of ionic crystals, semiconductors, metals, and transition-metal carbides and nitrides. To minimize numerical errors and to avoid making further approximations, the full-potential, all-electron FHI-aims code has been employed, and all the reported cohesive properties include contributions from zeropoint vibrations. Our assessment demonstrates that current DFAs can predict cohesive properties with mean absolute relative errors of 0.6% for the lattice constant and 6% for both the cohesive energy and the bulk modulus over the whole database of 64 solids. For semiconducting and insulating solids, the recently proposed SCAN meta-GGA functional represents a substantial improvement over the other functionals. However, when considering the different types of solids in the set, all of the employed functionals exhibit some variance in their performance. There are clear trends and relationships in the deviations of the cohesive properties, pointing to the need to consider, for example, long-range van der Waals (vdW) interactions. This point is also demonstrated by consistent improvements in predictions for cohesive properties of semiconductors when augmenting GGA and hybrid functionals with a screened Tkatchenko-Scheffler vdW energy term. Submitted to: New J. Phys.
VU University Amsterdam1, RWTH Aachen University2, University of Amsterdam3, University of Luxembourg4, United States Environmental Protection Agency5, Helmholtz Centre for Environmental Research - UFZ6, Agro ParisTech7, IFREMER8, Université de Namur9, University of Tübingen10, Örebro University11, Swiss Federal Institute of Aquatic Science and Technology12, University of Antwerp13, McGill University14, Michigan State University15, Environment Agency16
TL;DR: Cheminformatics and in vitro screening tests could be used as first approach to identify eco-neurotoxic pollutants and a small species test battery could be applied to assess the risks of ecosystems.
Abstract: The numbers of potential neurotoxicants in the environment are raising and pose a great risk for humans and the environment. Currently neurotoxicity assessment is mostly performed to predict and prevent harm to human populations. Despite all the efforts invested in the last years in developing novel in vitro or in silico test systems, in vivo tests with rodents are still the only accepted test for neurotoxicity risk assessment in Europe. Despite an increasing number of reports of species showing altered behaviour, neurotoxicity assessment for species in the environment is not required and therefore mostly not performed. Considering the increasing numbers of environmental contaminants with potential neurotoxic potential, eco-neurotoxicity should be also considered in risk assessment. In order to do so novel test systems are needed that can cope with species differences within ecosystems. In the field, online-biomonitoring systems using behavioural information could be used to detect neurotoxic effects and effect-directed analyses could be applied to identify the neurotoxicants causing the effect. Additionally, toxic pressure calculations in combination with mixture modelling could use environmental chemical monitoring data to predict adverse effects and prioritize pollutants for laboratory testing. Cheminformatics based on computational toxicological data from in vitro and in vivo studies could help to identify potential neurotoxicants. An array of in vitro assays covering different modes of action could be applied to screen compounds for neurotoxicity. The selection of in vitro assays could be guided by AOPs relevant for eco-neurotoxicity. In order to be able to perform risk assessment for eco-neurotoxicity, methods need to focus on the most sensitive species in an ecosystem. A test battery using species from different trophic levels might be the best approach. To implement eco-neurotoxicity assessment into European risk assessment, cheminformatics and in vitro screening tests could be used as first approach to identify eco-neurotoxic pollutants. In a second step, a small species test battery could be applied to assess the risks of ecosystems.
TL;DR: This article identifies the existing theoretical frameworks and empirical research that focus on CPS and provides examples of how recent technologies can automate analyses of CPS processes and assessments so that substantially larger data sets can be analyzed and so students can receive immediate feedback on their CPS performance.
Abstract: Collaborative problem solving (CPS) has been receiving increasing international attention because much of the complex work in the modern world is performed by teams. However, systematic education a...
30 Jul 2018
TL;DR: A modified version of the InterPlanetary Filesystem (IPFS) that leverages Ethereum smart contracts to provide access controlled file sharing and interacts with the smart contract whenever a file is uploaded, downloaded or transferred is presented.
Abstract: Large files cannot be efficiently stored on blockchains. On one hand side, the blockchain becomes bloated with data that has to be propagated within the blockchain network. On the other hand, since the blockchain is replicated on many nodes, a lot of storage space is required without serving an immediate purpose, especially if the node operator does not need to view every file that is stored on the blockchain. It furthermore leads to an increase in the price of operating blockchain nodes because more data needs to be processed, transferred and stored. IPFS is a file sharing system that can be leveraged to more efficiently store and share large files. It relies on cryptographic hashes that can easily be stored on a blockchain. Nonetheless, IPFS does not permit users to share files with selected parties. This is necessary, if sensitive or personal data needs to be shared. Therefore, this paper presents a modified version of the InterPlanetary Filesystem (IPFS) that leverages Ethereum smart contracts to provide access controlled file sharing. The smart contract is used to maintain the access control list, while the modified IPFS software enforces it. For this, it interacts with the smart contract whenever a file is uploaded, downloaded or transferred. Using an experimental setup, the impact of the access controlled IPFS is analyzed and discussed.
TL;DR: The vagal tank theory, building on neurophysiological, cognitive and social psychology approaches, will introduce a physiological indicator for self-regulation that has mainly been ignored from cognitive andSocial psychology, cardiac vagal control.
Abstract: The aim of this paper is to set the stage for the vagal tank theory, showcasing a functional resource account for self-regulation. The vagal tank theory, building on neurophysiological, cognitive and social psychology approaches, will introduce a physiological indicator for self-regulation that has mainly been ignored from cognitive and social psychology, cardiac vagal control (also referred to as cardiac vagal activity). Cardiac vagal control reflects the contribution of the vagus nerve, the main nerve of the parasympathetic nervous system, to cardiac regulation. We propose cardiac vagal control to be an indicator of how efficiently self-regulatory resources are mobilized and used. Three systematic levels of cardiac vagal control analysis are suggested: resting, reactivity, and recovery. Based on this physiological indicator we derive the metaphor of the vagal tank, which can get depleted and replenished. Overall, the vagal tank theory will enable to integrate previous findings from different disciplines and to stimulate new research questions, predictions, and designs regarding self-regulation.
23 Apr 2018
TL;DR: A blockchain-based PKI management framework for issuing, validating and revoking X.509 certificates with more reliable and robust PKI systems with modest maintenance costs is designed and developed.
Abstract: Public-Key Infrastructure (PKI) is the cornerstone technology that facilitates secure information exchange over the Internet. However, PKI is exposed to risks due to potential failures of Certificate Authorities (CAs) that may be used to issue unauthorized certificates for end-users. Many recent breaches show that if a CA is compromised, the security of the corresponding end-users will be in risk. As an emerging solution, Blockchain technology potentially resolves the problems of traditional PKI systems - in particular, elimination of single point-of-failure and rapid reaction to CAs shortcomings. Blockchain has the ability to store and manage digital certificates within a public and immutable ledger, resulting in a fully traceable history log. In this paper we designed and developed a blockchain-based PKI management framework for issuing, validating and revoking X.509 certificates. Evaluation and experimental results confirm that the proposed framework provides more reliable and robust PKI systems with modest maintenance costs.
TL;DR: The graded risk concept of the most recently identified PARK loci and some susceptibility variants in GBA, LRRK2 and SNCA and the emerging concept of rare genetic variants in candidates genes for PD, such as HSPA9, TRAP1 and RHOT1 complete the picture of the complex genetic architecture of PD.
Abstract: Mitochondrial impairment is a well-established pathological pathway implicated in Parkinson’s disease (PD). Defects of the complex I of the mitochondrial respiratory chain have been found in post-mortem brains from sporadic PD patients. Furthermore, several disease-related genes are linked to mitochondrial pathways, such as PRKN, PINK1, DJ-1 and HTRA2 and are associated with mitochondrial impairment. This phenotype can be caused by the dysfunction of mitochondrial quality control machinery at different levels: molecular, organellar or cellular. Mitochondrial unfolded protein response represents the molecular level and implicates various chaperones and proteases. If the molecular level of quality control is not sufficient, the organellar level is required and involves mitophagy and mitochondrial-derived vesicles to sequester whole dysfunctional organelle or parts of it. Only when the impairment is too severe, does it lead to cell death via apoptosis, which defines the cellular level of quality control. Here, we review how currently known PD-linked genetic variants interfere with different levels of mitochondrial quality control. We discuss the graded risk concept of the most recently identified PARK loci (PARK 17–23) and some susceptibility variants in GBA, LRRK2 and SNCA. Finally, the emerging concept of rare genetic variants in candidates genes for PD, such as HSPA9, TRAP1 and RHOT1, complete the picture of the complex genetic architecture of PD that will direct future precision medicine approaches.