Showing papers by "Braunschweig University of Technology published in 2019"
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, University of Melbourne27, Australian Institute of Marine Science28, 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: An overview and practical recommendations for aspects of HTS studies ranging from sampling and laboratory practices to data processing and analysis are provided, and advice for leveraging next-generation technologies to explore mycobiome diversity in different habitats is provided.
Abstract: Fungi are major ecological players in both terrestrial and aquatic environments by cycling organic matter and channelling nutrients across trophic levels. High-throughput sequencing (HTS) studies of fungal communities are redrawing the map of the fungal kingdom by hinting at its enormous - and largely uncharted - taxonomic and functional diversity. However, HTS approaches come with a range of pitfalls and potential biases, cautioning against unwary application and interpretation of HTS technologies and results. In this Review, we provide an overview and practical recommendations for aspects of HTS studies ranging from sampling and laboratory practices to data processing and analysis. We also discuss upcoming trends and techniques in the field and summarize recent and noteworthy results from HTS studies targeting fungal communities and guilds. Our Review highlights the need for reproducibility and public data availability in the study of fungal communities. If the associated challenges and conceptual barriers are overcome, HTS offers immense possibilities in mycology and elsewhere.
461 citations
University of California, Los Angeles1, University of California, Berkeley2, Goddard Space Flight Center3, Nagoya University4, Kanazawa University5, Tohoku University6, Korea Astronomy and Space Science Institute7, The Aerospace Corporation8, University of Washington9, Dartmouth College10, Montana State University11, University of California, Santa Cruz12, National Cheng Kung University13, Academia Sinica Institute of Astronomy and Astrophysics14, University of Tokyo15, National Central University16, National Oceanic and Atmospheric Administration17, Cooperative Institute for Research in Environmental Sciences18, Johns Hopkins University Applied Physics Laboratory19, Kyushu University20, Kyoto University21, National Institute of Polar Research22, University of Colorado Boulder23, University of Iowa24, University of New Hampshire25, Southwest Research Institute26, National Center for Atmospheric Research27, Université Paris-Saclay28, Boston University29, Braunschweig University of Technology30, University of Calgary31, University of Graz32, University of Minnesota33
TL;DR: The SPEDAS development history, goals, and current implementation are reviewed, and its “modes of use” are explained with examples geared for users and its technical implementation and requirements with software developers in mind are outlined.
Abstract: With the advent of the Heliophysics/Geospace System Observatory (H/GSO), a complement of multi-spacecraft missions and ground-based observatories to study the space environment, data retrieval, analysis, and visualization of space physics data can be daunting. The Space Physics Environment Data Analysis System (SPEDAS), a grass-roots software development platform (
www.spedas.org
), is now officially supported by NASA Heliophysics as part of its data environment infrastructure. It serves more than a dozen space missions and ground observatories and can integrate the full complement of past and upcoming space physics missions with minimal resources, following clear, simple, and well-proven guidelines. Free, modular and configurable to the needs of individual missions, it works in both command-line (ideal for experienced users) and Graphical User Interface (GUI) mode (reducing the learning curve for first-time users). Both options have “crib-sheets,” user-command sequences in ASCII format that can facilitate record-and-repeat actions, especially for complex operations and plotting. Crib-sheets enhance scientific interactions, as users can move rapidly and accurately from exchanges of technical information on data processing to efficient discussions regarding data interpretation and science. SPEDAS can readily query and ingest all International Solar Terrestrial Physics (ISTP)-compatible products from the Space Physics Data Facility (SPDF), enabling access to a vast collection of historic and current mission data. The planned incorporation of Heliophysics Application Programmer’s Interface (HAPI) standards will facilitate data ingestion from distributed datasets that adhere to these standards. Although SPEDAS is currently Interactive Data Language (IDL)-based (and interfaces to Java-based tools such as Autoplot), efforts are under-way to expand it further to work with python (first as an interface tool and potentially even receiving an under-the-hood replacement). We review the SPEDAS development history, goals, and current implementation. We explain its “modes of use” with examples geared for users and outline its technical implementation and requirements with software developers in mind. We also describe SPEDAS personnel and software management, interfaces with other organizations, resources and support structure available to the community, and future development plans.
371 citations
TL;DR: The BRENDA enzyme database, recently appointed ELIXIR Core Data Resource, is the main enzyme and enzyme-ligand information system and the enzyme class EC 7 has been established for ‘translocases’, enzymes that catalyze a transport of ions or metabolites across cellular membranes.
Abstract: The BRENDA enzyme database (www.brenda-enzymes.org), recently appointed ELIXIR Core Data Resource, is the main enzyme and enzyme-ligand information system. The core database provides a comprehensive overview on enzymes. A collection of 4.3 million data for ∼84 000 enzymes manually evaluated and extracted from ∼140 000 primary literature references is combined with information obtained by text and data mining, data integration and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, predicted enzyme locations and genome annotations. Major developments are a revised ligand summary page and the structure search now including a similarity and isomer search. BKMS-react, an integrated database containing known enzyme-catalyzed reactions, is supplemented with further reactions and improved access to pathway connections. In addition to existing enzyme word maps with graphical information of enzyme specific terms, plant word maps have been developed. They show a graphical overview of terms, e.g. enzyme or plant pathogen information, connected to specific plants. An organism summary page showing all relevant information, e.g. taxonomy and synonyms linked to enzyme data, was implemented. Based on a decision by the IUBMB enzyme task force the enzyme class EC 7 has been established for 'translocases', enzymes that catalyze a transport of ions or metabolites across cellular membranes.
331 citations
18 Apr 2019
TL;DR: This work presents a simple yet effective method to infer detailed full human body shape from only a single photograph, trained purely with synthetic data, and generalizes well to real-world photographs.
Abstract: We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method.
304 citations
TL;DR: It is shown that CSR is triggered prior to differentiation into GC B cells or plasmablasts and is greatly diminished in GCs, and the existence of IgM-dominated GCs is demonstrated, which are unlikely to occur under the assumption of ongoing switching.
296 citations
15 Jun 2019
TL;DR: Octopus, a learning-based model to infer the personalized 3D shape of people from a few frames of a monocular video in which the person is moving with a reconstruction accuracy of 4 to 5mm, while being orders of magnitude faster than previous methods.
Abstract: We present Octopus, a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving with a reconstruction accuracy of 4 to 5mm, while being orders of magnitude faster than previous methods. From semantic segmentation images, our Octopus model reconstructs a 3D shape, including the parameters of SMPL plus clothing and hair in 10 seconds or less. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, Octopus can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 5mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach.
289 citations
TL;DR: The availability of a large number of novel NHC adducts has not only produced new varieties of already existing ligand classes but has also allowed establishment of numerous complexes with unusual and often unprecedented element-metal bonds.
Abstract: N-Heterocyclic carbenes (NHC) are nowadays ubiquitous and indispensable in many research fields, and it is not possible to imagine modern transition metal and main group element chemistry without t...
288 citations
TL;DR: In this article, a survey of application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog-and cloud-based IoT systems is presented, including request-reply and publish-subscribe protocols.
Abstract: The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This article surveys e application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog- and cloud-based IoT systems. To this end, the article first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the article surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption, and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability, and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture.
256 citations
TL;DR: This tutorial will be especially useful for researchers who work on RT algorithms development and channel modeling to meet the evaluation requirements of 5G and beyond technologies.
Abstract: The application scenarios and requirements are more diverse in the fifth-generation (5G) era than before. In order to successfully support the system design and deployment, accurate channel modeling is important. Ray-tracing (RT) based deterministic modeling approach is accurate with detailed angular information and is a suitable candidate for predicting time-varying channel and multiple-input multiple-output (MIMO) channel for various frequency bands. However, the computational complexity and the utility of RT are the main concerns of users. Aiming at 5G and beyond wireless communications, this paper presents a comprehensive tutorial on the design of RT and the applications. The role of RT and the state-of-the-art RT techniques are reviewed. The features of academic and commercial RT based simulators are summarized and compared. The requirements, challenges, and developing trends of RT to enable the visions are discussed. The practices of the design of high-performance RT simulation platform for 5G and beyond communications are introduced, with the publicly available high-performance cloud-based RT simulation platform as the main reference. The hardware structure, networking, workflow, data flow and fundamental functions of a flexible high-performance RT platform are discussed. The applications of high-performance RT are presented based on two 5G scenarios, i.e., a 3.5 GHz Beijing vehicle-to-infrastructure scenario and a 28 GHz Manhattan outdoor scenario. The questions on how to calibrate and validate RT based on measurements, how to apply RT for mobile communications in moving scenarios, and how to evaluate MIMO beamforming technologies are answered. This tutorial will be especially useful for researchers who work on RT algorithms development and channel modeling to meet the evaluation requirements of 5G and beyond technologies.
237 citations
Leipzig University1, Martin Luther University of Halle-Wittenberg2, Universidade Positivo3, University of Vigo4, Empresa Brasileira de Pesquisa Agropecuária5, ETH Zurich6, Moscow State University7, University of Freiburg8, University of Jena9, University of Catania10, Wageningen University and Research Centre11, Free University of Berlin12, Senckenberg Museum13, Colorado State University14, National Agriculture and Food Research Organization15, University of Nairobi16, Commonwealth Scientific and Industrial Research Organisation17, National Scientific and Technical Research Council18, Brandenburg University of Technology19, Cornell University20, University College Dublin21, United States Forest Service22, University of Toronto23, Aberystwyth University24, State University of New York at Cortland25, National University of Luján26, University of Trier27, University of the Philippines Mindanao28, Razi University29, Josip Juraj Strossmayer University of Osijek30, Kyushu University31, Minnesota Pollution Control Agency32, Aarhus University33, Northern Kentucky University34, Lincoln University (Missouri)35, University of Agricultural Sciences, Dharwad36, Fukushima University37, Matej Bel University38, Lancaster University39, Université d'Abobo-Adjamé40, Tarbiat Modares University41, Pachhunga University College42, University of São Paulo43, University of Hawaii at Hilo44, College of Tropical Agriculture and Human Resources45, Oklahoma State University–Stillwater46, Forest Research Institute47, University of Extremadura48, Katholieke Universiteit Leuven49, Research Institute for Nature and Forest50, Natural Resources Institute Finland51, University of Alcalá52, King Abdulaziz University53, COMSATS Institute of Information Technology54, University of Minnesota55, Federal University of Maranhão56, Jagiellonian University57, Technical University of Berlin58, University of Wisconsin-Madison59, Leibniz Association60, Braunschweig University of Technology61, University of Innsbruck62, Russian Academy of Sciences63, Keldysh Institute of Applied Mathematics64, Khalsa College, Amritsar65, University of La Laguna66, Kōchi University67, Universidad Pública de Navarra68, McGill University69, The Nature Conservancy70, University of Giessen71, Henan University72, University of Saint Mary73
TL;DR: It was found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms, which suggest that climate change may have serious implications for earthworm communities and for the functions they provide.
Abstract: Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.
TL;DR: In this article, the authors propose a scalable secure learning paradigm that mitigates the impact of evasion attacks, while only slightly worsening the detection rate in the absence of an attack.
Abstract: To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been recently questioned, but it has been shown that machine learning exhibits inherent vulnerabilities that can be exploited to evade detection at test time. In other words, machine learning itself can be the weakest link in a security system. In this paper, we rely upon a previously-proposed attack framework to categorize potential attack scenarios against learning-based malware detection tools, by modeling attackers with different skills and capabilities. We then define and implement a set of corresponding evasion attacks to thoroughly assess the security of Drebin, an Android malware detector. The main contribution of this work is the proposal of a simple and scalable secure-learning paradigm that mitigates the impact of evasion attacks, while only slightly worsening the detection rate in the absence of attack. We finally argue that our secure-learning approach can also be readily applied to other malware detection tasks.
TL;DR: In this paper, the authors present a review of the state-of-the-art GaN micro-and nanodevices beyond lighting, including an up-to-date overview on the state of the art.
Abstract: Gallium nitride (GaN) light-emitting-diode (LED) technology has been the revolution in modern lighting. In the last decade, a huge global market of efficient, long-lasting, and ubiquitous white light sources has developed around the inception of the Nobel-prize-winning blue GaN LEDs. Today, GaN optoelectronics is developing beyond solid-state lighting, leading to new and innovative devices, e.g., for microdisplays, being the core technology for future augmented reality and visualization, as well as point light sources for optical excitation in communications, imaging, and sensing. This explosion of applications is driven by two main directions: the ability to produce very small GaN LEDs (micro-LEDs and nano-LEDs) with high efficiency and across large areas, in combination with the possibility to merge optoelectronic-grade GaN micro-LEDs with silicon microelectronics in a hybrid approach. GaN LED technology is now even spreading into the realm of display technology, which has been occupied by organic LEDs and liquid crystal displays for decades. In this review, the technological transition toward GaN micro- and nanodevices beyond lighting is discussed including an up-to-date overview on the state of the art.
TL;DR: This work investigates how macrophages adopt their metabolism early after activation to regulate TLR-inducible gene induction and postulates that metabolic licensing of histone acetylation provides another layer of control that serves to fine-tune transcriptional responses downstream of TLR activation.
TL;DR: It is shown that the fractional versions of the model based upon the Caputo and ABC operators estimate the real data comparatively better than the original integer order model, whereas the CF yields the results equivalent to the results obtained from the integer-order model.
Abstract: In this study, a physical system called the blood ethanol concentration model has been investigated in its fractional (non-integer) order version. The three most commonly used fractional operators with singular (Caputo) and non-singular (Atangana-Baleanu fractional derivative in the Caputo sense—ABC and the Caputo-Fabrizio—CF) kernels have been used to fractionalize the model, whereas during the process of fractionalization, the dimensional consistency for each of the equations in the model has been maintained. The Laplace transform technique is used to determine the exact solution of the model in all three cases, whereas its parameters are fitted through the least-squares error minimization technique. It is shown that the fractional versions of the model based upon the Caputo and ABC operators estimate the real data comparatively better than the original integer order model, whereas the CF yields the results equivalent to the results obtained from the integer-order model. The computation of the sum of squared residuals is carried out to show the performance of the models along with some graphical illustrations.In this study, a physical system called the blood ethanol concentration model has been investigated in its fractional (non-integer) order version. The three most commonly used fractional operators with singular (Caputo) and non-singular (Atangana-Baleanu fractional derivative in the Caputo sense—ABC and the Caputo-Fabrizio—CF) kernels have been used to fractionalize the model, whereas during the process of fractionalization, the dimensional consistency for each of the equations in the model has been maintained. The Laplace transform technique is used to determine the exact solution of the model in all three cases, whereas its parameters are fitted through the least-squares error minimization technique. It is shown that the fractional versions of the model based upon the Caputo and ABC operators estimate the real data comparatively better than the original integer order model, whereas the CF yields the results equivalent to the results obtained from the integer-order model. The computation of the sum of sq...
06 Aug 2019
TL;DR: In this paper, the authors investigated the specific role of PVP and Ag+ ions in the formation of platinum nanocubes (NCs) in polyol synthesis and proposed extended formation and growth mechanisms based on PVP as the main structure-directing agent.
Abstract: In this work, we have investigated the specific role of PVP and Ag+ ions in the formation of platinum nanocubes (NCs) in polyol synthesis. Various characterization techniques such as transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS) were employed to unravel the effects of PVP and Ag+ ion concentrations on the monodispersity and particle size of the obtained Pt NCs. Very interestingly, we have already fabricated Pt NCs with similar monodispersity and particle size using only 0.4 M PVP (absence of Ag+ ions). Furthermore, the dispersity of the Pt NCs strongly depends on the initial PVP concentration. This observation underscores the important role of PVP during the NC formation processes by controlling the relative growth rates along the direction with respect to those of the . Time-resolved experiments show that the formation and growth of Pt NCs are much faster in the absence of Ag+ ions than with Ag+ ions, which can be explained by the enhanced growth rate along the direction or/and the suppression of the growth rate along the . Electronic interactions between the chemisorbed pyrrolidone ring of the PVP and Pt surface are revealed from the XPS and FTIR data, showing a negative shift of the binding energy of N 1s and a red shift of the Pt–CO vibration band. From our experimental results, we propose extended formation and growth mechanisms based on PVP as the main structure-directing agent. Our model indicates that the aliphatic chains of PVP forming a multi-layer shell influence the mass transport of precursor ions to the initial Pt seed to control the growth rate of Pt NCs with exposed {100} planes. Altogether, we provide a simple, efficient and resource-friendly synthetic guideline for the preparation of nano-sized Pt NCs with high monodispersity and high purity.
TL;DR: Differences between laboratory-scale cultures and larger-scale processes are reviewed to provide a perspective on those strain characteristics that are especially important during scaling.
TL;DR: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time and post-takeover control and models that capture these effects through evidence accumulation were identified as promising directions for future work.
Abstract: Objective:This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and sugges...
TL;DR: The first cesium lead bromide thin-film distributed feedback and vertical cavity surface emitting lasers with ultralow threshold at RT that do not rely on the use of NPs are demonstrated and will advise a revision of the paradigm that efficient light emission from CsPbX3 perovskites can only be achieved with NPs.
Abstract: Cesium lead halide perovskites are of interest for light-emitting diodes and lasers. So far, thin-films of CsPbX3 have typically afforded very low photoluminescence quantum yields (PL-QY < 20%) and amplified spontaneous emission (ASE) only at cryogenic temperatures, as defect related nonradiative recombination dominated at room temperature (RT). There is a current belief that, for efficient light emission from lead halide perovskites at RT, the charge carriers/excitons need to be confined on the nanometer scale, like in CsPbX3 nanoparticles (NPs). Here, thin films of cesium lead bromide, which show a high PL-QY of 68% and low-threshold ASE at RT, are presented. As-deposited layers are recrystallized by thermal imprint, which results in continuous films (100% coverage of the substrate), composed of large crystals with micrometer lateral extension. Using these layers, the first cesium lead bromide thin-film distributed feedback and vertical cavity surface emitting lasers with ultralow threshold at RT that do not rely on the use of NPs are demonstrated. It is foreseen that these results will have a broader impact beyond perovskite lasers and will advise a revision of the paradigm that efficient light emission from CsPbX3 perovskites can only be achieved with NPs.
TL;DR: The aim of this review is to document the progress in the research onAmyloid formation from a physicochemical perspective with a special focus on the physiological factors influencing the aggregation of the amyloid-β peptide, the islet amyloids polypeptide, α-synuclein, and the hungingtin protein.
Abstract: One of the grand challenges of biophysical chemistry is to understand the principles that govern protein misfolding and aggregation, which is a highly complex process that is sensitive to initial conditions, operates on a huge range of length- and timescales, and has products that range from protein dimers to macroscopic amyloid fibrils. Aberrant aggregation is associated with more than 25 diseases, which include Alzheimer's, Parkinson's, Huntington's, and type II diabetes. Amyloid aggregation has been extensively studied in the test tube, therefore under conditions that are far from physiological relevance. Hence, there is dire need to extend these investigations to in vivo conditions where amyloid formation is affected by a myriad of biochemical interactions. As a hallmark of neurodegenerative diseases, these interactions need to be understood in detail to develop novel therapeutic interventions, as millions of people globally suffer from neurodegenerative disorders and type II diabetes. The aim of this review is to document the progress in the research on amyloid formation from a physicochemical perspective with a special focus on the physiological factors influencing the aggregation of the amyloid-β peptide, the islet amyloid polypeptide, α-synuclein, and the hungingtin protein.
TL;DR: How the assembly of actin filament networks can feed back onto their regulators, as exemplified for the lamellipodial factor WAVE regulatory complex, tightly controlling accumulation of this complex at specific subcellular locations as well as its turnover is discussed.
Abstract: Cell migration is an essential process, both in unicellular organisms such as amoeba and as individual or collective motility in highly developed multicellular organisms like mammals. It is controlled by a variety of activities combining protrusive and contractile forces, normally generated by actin filaments. Here, we summarize actin filament assembly and turnover processes, and how respective biochemical activities translate into different protrusion types engaged in migration. These actin-based plasma membrane protrusions include actin-related protein 2/3 complex-dependent structures such as lamellipodia and membrane ruffles, filopodia as well as plasma membrane blebs. We also address observed antagonisms between these protrusion types, and propose a model - also inspired by previous literature - in which a complex balance between specific Rho GTPase signaling pathways dictates the protrusion mechanism employed by cells. Furthermore, we revisit published work regarding the fascinating antagonism between Rac and Rho GTPases, and how this intricate signaling network can define cell behavior and modes of migration. Finally, we discuss how the assembly of actin filament networks can feed back onto their regulators, as exemplified for the lamellipodial factor WAVE regulatory complex, tightly controlling accumulation of this complex at specific subcellular locations as well as its turnover.
TL;DR: All metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography and Exactive HF mass spectrometry, are annotated using the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3.
Abstract: Urine metabolites are used in many clinical and biomedical studies but usually only for a few classic compounds. Metabolomics detects vastly more metabolic signals that may be used to precisely define the health status of individuals. However, many compounds remain unidentified, hampering biochemical conclusions. Here, we annotate all metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography (HILIC)-Q Exactive HF mass spectrometry and charged surface hybrid (CSH)-Q Exactive HF mass spectrometry. Over 9,000 unique metabolite signals were detected, of which 42% triggered MS/MS fragmentations in data-dependent mode. On the highest Metabolomics Standards Initiative (MSI) confidence level 1, we identified 175 compounds using authentic standards with precursor mass, retention time, and MS/MS matching. An additional 578 compounds were annotated by precursor accurate mass and MS/MS matching alone, MSI level 2, including a novel library specifically geared at acylcarnitines (CarniBlast). The rest of the metabolome is usually left unannotated. To fill this gap, we used the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3). Testing the top-ranked metabolites in CSI:Finger ID annotations yielded 40% accuracy when applied to the MSI level 1 identified compounds. We classified all MSI level 3 annotations by the NIST hybrid search using the ClassyFire ontology into 21 superclasses that were further distinguished into 184 chemical classes. ClassyFire annotations showed that the previously unannotated urine metabolome consists of 28% derivatives of organic acids, 16% heterocyclics, and 16% lipids as major classes.
TL;DR: Fatigued drivers could pose a serious hazard in complex take-over situations where situation awareness is required to prepare for threats and driver fatigue monitoring or controllable distraction through non-driving tasks could be necessary to ensure alertness and availability during highly automated driving.
TL;DR: This work incorporates temporal and spatial anticipation of service requests into approximate dynamic programming (ADP) procedures to yield dynamic routing policies for the single-vehicle routing problem with stochastic service requests, an important problem in city-based logistics.
Abstract: Although increasing amounts of transaction data make it possible to characterize uncertainties surrounding customer service requests, few methods integrate predictive tools with prescriptive optimi...
TL;DR: A systematic multi-perspective overview of distinct strategies for optimizing MFC performance in terms of electric power output and decreasing power losses provides a valuable guideline for optimizing the electrical energy generation of MFCs.
TL;DR: In a rat model of multiple sclerosis, β-synuclein-specific T cells induce inflammation and pathological changes in the grey matter of the central nervous system; these cells were also found in higher numbers in patients with several sclerosis, particularly those with a chronic progressive course.
Abstract: The grey matter is a central target of pathological processes in neurodegenerative disorders such as Parkinson’s and Alzheimer’s diseases. The grey matter is often also affected in multiple sclerosis, an autoimmune disease of the central nervous system. The mechanisms that underlie grey matter inflammation and degeneration in multiple sclerosis are not well understood. Here we show that, in Lewis rats, T cells directed against the neuronal protein β-synuclein specifically invade the grey matter and that this is accompanied by the presentation of multifaceted clinical disease. The expression pattern of β-synuclein induces the local activation of these T cells and, therefore, determined inflammatory priming of the tissue and targeted recruitment of immune cells. The resulting inflammation led to significant changes in the grey matter, which ranged from gliosis and neuronal destruction to brain atrophy. In humans, β-synuclein-specific T cells were enriched in patients with chronic-progressive multiple sclerosis. These findings reveal a previously unrecognized role of β-synuclein in provoking T-cell-mediated pathology of the central nervous system. In a rat model of multiple sclerosis, β-synuclein-specific T cells induce inflammation and pathological changes in the grey matter of the central nervous system; these cells were also found in higher numbers in patients with multiple sclerosis, particularly those with a chronic progressive course.
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TL;DR: In this article, a learning-based model was proposed to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, with a reconstruction accuracy of 5mm.
Abstract: We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach.
TL;DR: In this paper, pectin extraction from pomelo peel was performed using water, acid and alkaline solutions with a microwave power of 1100 W for 2min, and yield and structural/chemical characteristics of the extracted pectins were investigated.
TL;DR: In this paper, Li-S batteries have attracted significant attention as a candidate for next-generation batteries, while employing solid elastic materials. But, their theoretical energy density is low compared to the lithium-sulfur (LiS) battery.
Abstract: Because of a remarkably high theoretical energy density, the lithium–sulfur (Li–S) battery has attracted significant attention as a candidate for next-generation batteries. While employing solid el...
TL;DR: The data demonstrate that the converted, insulin-specific CAR Tregs (cTregs) were functional stable, suppressive and long-lived in vivo, and a proof of concept for both redirection of T cell specificity and conversion of Teffs to cT Regs.