gebhart/patrepeval huggingface dataset:
Anionic N<i>‐</i>Heterocyclic Carbenes That Contain a Weakly Coordinating Borate Moiety (2012)

Piggyback ride: Anionic N-heterocyclic carbenes are presented that bear their negative charge in the form of a weakly coordinating anionic borate moiety in the backbone. This class of carbene can be used in silver-free routes for the preparation of neutral, single-source gold(I) catalysts, the performance of which is comparable with that of other cationic gold(I) catalysts in enyne rearrangements. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
A polymer based and template-directed approach towards functional multidimensional micro-structured organic/inorganic hybrid materials (2014)

The control over structure and function in hierarchically multidimensional materials based on hollow spheres, nanowires, nanorods, nanotubes, fibres, membranes or inverse opals with adjustable dimensions has gained considerable attention due to their tremendous potential for a wide variety of applications. Herein we describe convenient and efficient synthetic concepts for synthesis and processing of well-defined polymer-templated inorganic materials with 0D, 1D, 2D and 3D nano- and microstructures. In the first step, we describe universal methodologies for the controlled build-up of polymer-templated non-functional inorganic structures by taking advantage of lower dimensional structures such as core–shell particles or fibres. With this approach it is possible to obtain more sophisticated architectures such as 3D ordered macroporous (3DOM) materials after applying different procedures for organization, e.g. the powerful melt shear technique as well as novel double-templating strategies towards multidimensional carbon architectures. To prove the feasibility of our protocols established herein we have exemplarily applied these methods to the formation of functional inorganic high-temperature materials, such as silicon carbide (β-SiC) and yttria-stabilized zirconia (YSZ). The general pathways for the controlled build-up of nano- and micro-scaled structures based on polymer templates may thus provide a facile and versatile route to an even wider variety of organic/inorganic composite materials offering a wider range of future applications in the fields of catalysis, separation, sensors, optics, and biomedicine. Herein we show first examples of selected new material morphologies towards energy related issues, namely Li-ion battery applications and heterogeneous catalysis (selective ethanol oxidation).
Secure Event Dissemination in Publish-Subscribe Networks (2007)

Secure event dissemination in a pub-sub network refers to secure distribution of events to clients subscribing to those events without revealing the secret attributes in the event to the unauthorized subscribers and the routing nodes in a pub-sub network. A common solution to provide confidentiality guarantees for the secret attributes in an event is to encrypt so that only authorized subscribers can read them. The key challenge here is to build a secure and scalable content-based event dissemination infrastructure that can handle complex and flexible subscription models while preserving the efficiency and scalability of key management algorithms. In this paper, we describe the design and implementation of PSGuard, for secure event dissemination in pub-sub networks. PSGuard exploit hierarchical key derivation algorithms to encode publication-subscription matching semantics for scalable key management. An experimental evaluation of our prototype system shows that PSGuard meets the security requirements while maintaining the performance and scalability of a pub-sub network.
The Formation of a Connection between Carbon Nanotubes in an Electron Beam (2001)

Crossing multiwalled carbon nanotubes are connected by irradiating the junction in a scanning electron microscope. The aggregation of hydrocarbons and successive transformation into amorphous carbon under the electron beam is used to solder nanotubes and to make a mechanical junction. Transmission electron microscopy is carried out to study the graphitization of the junction under further irradiation or annealing.
Asymptotic approximations in the near‐integrated model with a non‐zero initial condition (2001)

Journal Article Asymptotic approximations in the near‐integrated model with a non‐zero initial condition Get access Pierre Perron, Pierre Perron Department of Economics, Boston University, 270 Bay State Rd., Boston, MA 02215, USA Search for other works by this author on: Oxford Academic Google Scholar Cosme Vodounou Cosme Vodounou Institut National de la Statistique et de l'Analyse Économique, Bénin Search for other works by this author on: Oxford Academic Google Scholar The Econometrics Journal, Volume 4, Issue 1, 1 June 2001, Pages 143–169, https://doi.org/10.1111/1368-423X.00060 Published: 09 October 2008 Article history Received: 01 October 2000 Published: 09 October 2008
Multi-Scale Dictionary Learning Using Wavelets (2011)

In this paper, we present a multi-scale dictionary learning paradigm for sparse and redundant signal representations. The appeal of such a dictionary is obvious-in many cases data naturally comes at different scales. A multi-scale dictionary should be able to combine the advantages of generic multi-scale representations (such as Wavelets), with the power of learned dictionaries, in capturing the intrinsic characteristics of a family of signals. Using such a dictionary would allow representing the data in a more efficient, i.e., sparse, manner, allowing applications to take a more global look at the signal. In this paper, we aim to achieve this goal without incurring the costs of an explicit dictionary with large atoms. The K-SVD using Wavelets approach presented here applies dictionary learning in the analysis domain of a fixed multi-scale operator. This way, sub-dictionaries at different data scales, consisting of small atoms, are trained. These dictionaries can then be efficiently used in sparse coding for various image processing applications, potentially outperforming both single-scale trained dictionaries and multi-scale analytic ones. In this paper, we demonstrate this construction and discuss its potential through several experiments performed on fingerprint and coastal scenery images.
Treatment of Hidradenitis Suppurativa with Tumour Necrosis Factor-alpha Inhibitors: An Update on Infliximab (2011)

In the November issue of Acta Dermato-Venereologica, Haslund et al. (1) performed a systematic search using hidradenitis suppurativa (HS), acne inversa, infliximab, etanercept and adalimumab as key words. A total of 20 articles regarding the use of infliximab in HS (52 patients in total) were retrieved and evaluated for a systematic review. The effort put into reviewing all the literature is highly appreciated, but in addition herein we would like to integrate new information regarding another 42 patients, seven of these from our personal experience, evaluating other selected variables that will add new insights to the work of Haslund et al. (1-5, 11).
Mining topics in documents (2014)

Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (e.g., thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have so many documents. Given a small number of documents, the classic topic model LDA generates very poor topics. Even with a large volume of data, unsupervised learning of topic models can still produce unsatisfactory results. In recently years, knowledge-based topic models have been proposed, which ask human users to provide some prior domain knowledge to guide the model to produce better topics. Our research takes a radically different approach. We propose to learn as humans do, i.e., retaining the results learned in the past and using them to help future learning. When faced with a new task, we first mine some reliable (prior) knowledge from the past learning/modeling results and then use it to guide the model inference to generate more coherent topics. This approach is possible because of the big data readily available on the Web. The proposed algorithm mines two forms of knowledge: must-link (meaning that two words should be in the same topic) and cannot-link (meaning that two words should not be in the same topic). It also deals with two problems of the automatically mined knowledge, i.e., wrong knowledge and knowledge transitivity. Experimental results using review documents from 100 product domains show that the proposed approach makes dramatic improvements over state-of-the-art baselines.
Optimized Solvent-in-Salt Electrolytes for High-Energy Rechargeable Metallic Lithium Batteries (2014)

Lithium-based batteries as a highly efficient energy storage device has long been considered as promising power supply in various electric vehicles (EVs). However, present available lithium ion technology could not satisfy the increasing demand on energy density. Metallic lithium batteries still exhibit the highest energy density among the secondary batteries. For instance, Li-S and Li-O 2 batteries have high theoretical specific energy density of 2654 Wh/kg and 3500 Wh/kg, respectively. However, metallic lithium batteries inevitably suffer from the safety concern due to the formation of lithium dendrite during cycling. Generally, the employed electrolyte plays an important role in influencing the interfacial properties and morphology of the metallic lithium anode. Thus, in order to make the metallic lithium anode stable in long cycling process, here, we show a new class of “Solvent-in-Salt” (SIS) electrolyte with ultrahigh lithium salt concentration, in which salt holds a dominant position in whole lithium ion transport system (1,2). This new electrolyte remarkably enhances cyclic and safety performance of rechargeable metallic lithium batteries via effectively suppressing the formation of lithium dendrites and greatly reducing the degree of damage of metallic lithium anode. In this presentation we will take lithium-sulfur battery as an example to show the power of SIS. Compared with traditional non-aqueous electrolyte (usually 1-2 mol/L), the SIS electrolyte (5-7 mol/1 L) exhibits the unique physicochemical properties: high lithium transference number (0.73), high viscosity (72 cP) and acceptable ionic conductivity (0.814 mS/cm @ room-temperature) as well as low glass transition temperature of -77.3oC. When used in Li-S batteries, the Li storage performance is significantly improved. It shows an initial specific discharge capacity of 1041 mAh/g at a rate of 0.2C and maintains a reversible capacity of 770 mAh/g with capacity retention of 74% after 100 cycles. More importantly, the coulombic efficiency reaches nearly 100% after the first cycle (the first one is 93.7%). The improved performance can be ascribed to 1) the dissolution of polysulfides in electrolyte is fully inhibited and 2) the corrosion of lithium anode is greatly reduced and the formation of lithium dendrites is effectively suppressed in this ultrahigh salt concentration electrolyte. Furthermore, we optimized the composition of the SIS electrolyte and carbon supports and further improved the performance (3). We also applied this new SIS electrolyte into the soft package of Li-S battery whose performance will be shown and discussed. References [1] A Chinese patent (application number: 201210017535.0) was filed on Jan. 19, 2012 . [2] L. M. Suo, Y.-S. Hu, H. Li, M. Armand, L. Q. Chen, Nature Communications , 2013 , 4, 1481. [3] Z. Fang, L. M. Suo, Y.-S. Hu, H. Li, L. Q. Chen, 2013 , in preparation. Figure 1 A soft package of Li-S battery using our Solvent-in-Salt electrolyte
Interaction of von Willebrand Factor Domain A1 with Platelet Glycoprotein Ibα-(1–289) (2000)

We investigated the crucial hemostatic interaction between von Willebrand factor (VWF) and platelet glycoprotein (GP) Ibalpha. Recombinant VWF A1 domain (residues Glu(497)-Pro(705) of VWF) bound stoichiometrically to a GPIbalpha-calmodulin fusion protein (residues His(1)-Val(289) of GPIbalpha; GPIbalpha-CaM) immobilized on W-7-agarose with a K(d) of 3.3 microM. The variant VWF A1(R545A) bound to GPIbalpha-CaM 20-fold more tightly, mainly because the association rate constant k(on) increased from 1,100 to 8,800 M(-1) s(-1). The GPIbalpha mutations G233V and M239V cause platelet-type pseudo-von Willebrand disease, and VWF A1 bound to GPIbalpha(G233V)-CaM and GPIbalpha(M239V)-CaM with a K(d) of 1.0 and 0.63 microM, respectively. The increased affinity of VWF A1 for GPIbalpha(M239V)-CaM was explained by an increase in k(on) to 4,500 M(-1) s(-1). GPIbalpha-CaM bound with similar affinity to recombinant VWF A1, to multimeric plasma VWF, and to a fragment of dispase-digested plasma VWF (residues Leu(480)/Val(481)-Gly(718)). VWF A1 and A1(R545A) bound to platelets with affinities and rate constants similar to those for binding to GPIbalpha-CaM, and botrocetin had the expected positively cooperative effect on the binding of VWF A1 to GPIbalpha-CaM. Therefore, allosteric regulation by botrocetin of VWF A1 binding to GPIbalpha, and the increased binding affinity caused by mutations in VWF or GPIbalpha, are reproduced by isolated structural domains. The substantial increase in k(on) caused by mutations in either A1 or GPIbalpha suggests that productive interaction requires rate-limiting conformational changes in both binding sites. The exceptionally slow k(on) and k(off) provide important new constraints on models for rapid platelet tethering at high wall shear rates.
A distributed mutual exclusion algorithm (1985)

A distributed algorithm is presented that realizes mutual exclusion among N nodes in a computer network. The algorithm requires at most N message exchanges for one mutual exclusion invocation. Accordingly, the delay to invoke mutual exclusion is smaller than in an algorithm of Ricart and Agrawala, which requires 2*( N - 1) message exchanges per invocation. A drawback of the algorithm is that the sequence numbers contained in the messages are unbounded. It is shown that this problem can be overcome by slightly increasing the number of message exchanges.
Deep learning using electrocardiographs (2020)

An electrocardiograph (ECG) is a low-cost and widely available test used to measure the electrical activity of the heart to help diagnose cardiac conditions. Rich in prognostic data, millions of ECGs are recorded annually and they have fuelled artificial intelligence (AI) algorithms to identify heart conditions, including atrial fibrillation, and impaired left ventricular systolic dysfunction for which a clinical trial is in progress. The Lancet Digital Health reports two studies using ECG data, which demonstrate that AI models are powerful tools for patient screening for cardiovascular conditions and diseases beyond the heart.
Nitrogen-Doped Carbon Nanoparticle–Carbon Nanofiber Composite as an Efficient Metal-Free Cathode Catalyst for Oxygen Reduction Reaction (2016)

Metal-free nitrogen-doped carbon materials are currently considered at the forefront of potential alternative cathode catalysts for the oxygen reduction reaction (ORR) in fuel cell technology. Despite numerous efforts in this area over the past decade, rational design and development of a new catalyst system based on nitrogen-doped carbon materials via an innovative approach still present intriguing challenges in ORR catalysis research. Herein, a new kind of nitrogen-doped carbon nanoparticle-carbon nanofiber (NCNP-CNF) composite with highly efficient and stable ORR catalytic activity has been developed via a new approach assisted by a solution plasma process. The integration of NCNPs and CNFs by the solution plasma process can lead to a unique morphological feature and modify physicochemical properties. The NCNP-CNF composite exhibits a significantly enhanced ORR activity through a dominant four-electron pathway in an alkaline solution. The enhancement in ORR activity of NCNP-CNF composite can be attributed to the synergistic effects of good electron transport from highly graphitized CNFs as well as abundance of exposed catalytic sites and meso/macroporosity from NCNPs. More importantly, NCNP-CNF composite reveals excellent long-term durability and high tolerance to methanol crossover compared with those of a commercial 20 wt % supported on Vulcan XC-72. We expect that NCNP-CNF composite prepared by this synthetic approach can be a promising metal-free cathode catalyst candidate for ORR in fuel cells and metal-air batteries.
.DELTA.3-Piperideine alkaloids from the toxic plant Lobelia berlandieri (1987)

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXT.DELTA.3-Piperideine alkaloids from the toxic plant Lobelia berlandieriHoward J. Williams, Allen C. Ray, and Hyeong L. KimCite this: J. Agric. Food Chem. 1987, 35, 1, 19–22Publication Date (Print):January 1, 1987Publication History Published online1 May 2002Published inissue 1 January 1987https://pubs.acs.org/doi/10.1021/jf00073a005https://doi.org/10.1021/jf00073a005research-articleACS PublicationsRequest reuse permissionsArticle Views130Altmetric-Citations10LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts
MUSE CSP: An Extension to the Constraint Satisfaction Problem (1996)

This paper describes an extension to the constraint satisfaction problem (CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem). This extension is especially useful for those problems which segment into multiple sets of partially shared variables. Such problems arise naturally in signal processing applications including computer vision, speech processing, and handwriting recognition. For these applications, it is often difficult to segment the data in only one way given the low-level information utilized by the segmentation algorithms. MUSE CSP can be used to compactly represent several similar instances of the constraint satisfaction problem. If multiple instances of a CSP have some common variables which have the same domains and constraints, then they can be combined into a single instance of a MUSE CSP, reducing the work required to apply the constraints. We introduce the concepts of MUSE node consistency, MUSE arc consistency, and MUSE path consistency. We then demonstrate how MUSE CSP can be used to compactly represent lexically ambiguous sentences and the multiple sentence hypotheses that are often generated by speech recognition algorithms so that grammar constraints can be used to provide parses for all syntactically correct sentences. Algorithms for MUSE arc and path consistency are provided. Finally, we discuss how to create a MUSE CSP from a set of CSPs which are labeled to indicate when the same variable is shared by more than a single CSP.
Glutamate excitotoxicity: A mechanism of neurologic injury associated with hypothermic circulatory arrest (1994)

Glutamate, the major central nervous system neurotransmitter, may have potent neurotoxic activity under conditions of metabolic stress. By receptor autoradiography, we have demonstrated that brain regions most vulnerable to injury during prolonged hypothermic circulatory arrest have the highest density of glutamate receptors. To test the hypothesis that such injury could be mediated by glutamate excitotoxicity, we used dizocilpine (MK-801), a selective N-methyl-D-aspartate-glutamate receptor antagonist in a canine survival model of hypothermic circulatory arrest. Eighteen male dogs (20 to 25 kg) were supported by closed-chest cardiopulmonary bypass, subjected to 2 hours of hypothermic circulatory arrest at 18 degrees C, and rewarmed on cardiopulmonary bypass. All were mechanically ventilated and monitored for 20 hours before extubation and survived for 3 days. Group A dogs (n = 9) received a prearrest intravenous bolus of dizocilpine (0.75 mg/kg) followed by continuous infusion (75 micrograms/kg per hour), resulting in electroencephalographic silence. Dizocilpine was weaned before extubation. Group B dogs received vehicle only. According to a species-specific behavior scale that yielded a neurologic deficit score ranging from 0 (normal) to 500 (brain dead), all animals were neurologically assessed every 12 hours. After the dogs were killed at 72 hours, brains were examined by receptor autoradiography and histologically for patterns of selective neuronal necrosis; they were scored blindly from 0 (normal) to 100 (severe injury). Group A dogs had better neurologic function than group B (neurologic deficit score 21 +/- 15 versus 192 +/- 40, p < 0.001) and had less neuronal injury (7.3 +/- 3 versus 48.3 +/- 9, p < 0.0001). Densitometric receptor autoradiography revealed preservation of neuronal N-methyl-D-aspartate-glutamate receptor expression in group A only. These results represent the first direct evidence of a role for glutamate excitotoxicity in the development of hypothermic circulatory arrest-induced brain injury and suggest that selective glutamate receptor antagonists may have a neuroprotective capacity in prolonged periods of hypothermic circulatory arrest.
Active Throughput Estimation Using RTT of Differing ICMP Packet Sizes (2008)

Network measurements always play a vital role for good network management purposes and are essential for quality of service (QoS) requirements. Packet delay derivations are critical measurement parameters in diagnosing simple network performance measures. The traditional ping tool is popular for target host connectivity testing and average delay measurements. However, ping is a poor indicator of round trip time (RTT) measurements at the network layer. In this paper, an active round trip time (RTT) packet delay measurements using Internet control message protocol (ICMP) data packets of varying sizes is proposed. It was observed that in the proposed RTT measurement technique, peak throughput values were obtained compared to the ping measurements. The proposed approach eliminates the need for special purpose cooperation and/or software applications at the destination in the measured network path when using the ICMP Echo Request/ Reply technique.