gebhart/patrepeval huggingface dataset:
<i>In vitro</i> behavior of osteoblastic cells cultured in the presence of pseudowollastonite ceramic (2004)

Abstract Pseudowollastonite ceramic (psW) is a bioactive ceramic that binds to bone when implanted in vivo and may be useful for the treatment of skeletal defects. However, there have been no studies that examined the interaction between psW and osteoblastic cells in vitro. This study investigated the suitability of psW as a substratum for cell attachment and the ability of the material to effect osteoblasts at a distance from the material surface. Fetal rat calvarial cells were plated onto the ceramic and examined by scanning electron microscopy. The findings reported show that cells attached and proliferated on the surface to the ceramic. Attachment by cells to the material can be enhanced by preincubation of psW in serum or media containing fibronectin. The adhesion of cells can be inhibited by addition of GRGDS peptides suggesting that adhesion to psW is mediated by integrin binding to adsorbed proteins. To study the effects of psW at a distance, cells were cultured in the presence but not in direct contact with the material. Subsequent changes in proliferation, alkaline phosphatase expression, and bone nodule formation were assessed. Cells grown in wells containing psW demonstrated an increase in both the rate and total numbers of bone nodules formed, although there were no differences in proliferation or alkaline phosphatase expression. Overall, these results suggest that psW is biocompatible and osteoconductive. © 2004 Wiley Periodicals, Inc. J Biomed Mater Res 69A: 351–358, 2004
Towards an End-to-End Architecture for Run-Time Data Protection in the Cloud (2018)

Protecting sensitive data is a key concern for the adoption of cloud solutions. Protecting data in the cloud is made particularly challenging by the dynamic changes that cloud systems may undergo at run-time, as well as the complex interactions among multiple software and hardware components, services, and stakeholders. Conformance to data protection requirements in such a dynamic environment cannot any longer be ensured during design time; e.g. due to the dynamic changes imposed by replication and migration of components. It requires run-time data protection mechanisms. This paper proposes combining multiple existing data protection approaches and extending them to run-time, ultimately delivering an end-to-end architecture for run-time data protection in the cloud. We validate the practical applicability of our approach by a commercial case study.
Immobilization of enzymes with use of photosensitive polymers having the stilbazolium group (1980)

Abstract Reaction of the homopolymer of 2‐chloroethyl vinyl ether and the copolymer of N ‐vinylpyrrolidone with stilbazole produced photosensitive polymers. The photocrosslinking mechanism is discussed. Irradiation of a film of the water‐soluble photosensitive polymer yielded insolubilized material in which the enzymes were entrapped. The activity of entrapped invertase, glucoamylase, and catalase was 30, 60, and 35%, respectively.
Amino Acid-Modified Calcitonin Immunization Induces Tumor Epitope-Specific Immunity in a Transgenic Mouse Model for Medullary Thyroid Carcinoma (2008)

Up to now, no relevant tumor antigen has been identified in medullary thyroid carcinoma (MTC). The aim of the present study was to prove the concept of an immunization with an amino acid-modified calcitonin (CT) for the treatment of MTC in a transgenic mouse model. Amino acid-modified (human) CT has been chosen for vaccination because of its higher binding affinity to the murine H2-Kb-MHC molecule. Mice were immunized over 6 months with monthly injections of amino acid-modified CT-pulsed dendritic cells. For enumeration of tumor epitope-specific CD8+ cytotoxic T cells, tetramer analyses were performed. CT peptide-treated mice revealed a mean 0.73 +/- 0.45 and 0.91 +/- 0.59% positive cells, depending on the two tetramers tested, whereas no increase was seen in control protein-immunized mice (0.08-0.12% tetramer-positive cells). Importantly, the subset of CT-specific CD8+ T cells also showed a high expression of interferon-gamma. In line with these results, CT-immunized mice also showed an intratumor infiltration with CD8+ T lymphocytes. Importantly, we also found a diminished tumor outgrowth of -57% and a decrease of the serum CT levels (2.0 +/- 0.1 pg/ml) compared with control protein-immunized Ret/Cal mice (3.0 +/- 0.4 pg/ml). In summary, we show that amino acid-modified CT is recognized from the immune system leading to a specific antitumor immune response and a diminished tumor outgrowth in transgenic MTC mice. The results are of potential importance because they might be applicable to patients with metastatic spread of a MTC.
Detection of overlapping acoustic events using a temporally-constrained probabilistic model (2016)

In this paper, a system for overlapping acoustic event detection is proposed, which models the temporal evolution of sound events. The system is based on probabilistic latent component analysis, supporting the use of a sound event dictionary where each exemplar consists of a succession of spectral templates. The temporal succession of the templates is controlled through event class-wise Hidden Markov Models (HMMs). As input time/frequency representation, the Equivalent Rectangular Bandwidth (ERB) spectrogram is used. Experiments are carried out on polyphonic datasets of office sounds generated using an acoustic scene simulator, as well as real and synthesized monophonic datasets for comparative purposes. Results show that the proposed system outperforms several state-of-the-art methods for overlapping acoustic event detection on the same task, using both frame-based and event-based metrics, and is robust to varying event density and noise levels.
Constraint satisfaction problem using modified branch and bound algorithm (2008)

A constraint satisfaction problem (CSP) involves assigning possible values to a set of variables without defying any constraints. There are various techniques available to solve or give partial solution to CSP. This paper presents a modification of branch and bound algorithm, which is used to solve a constraint satisfaction problem in map colouring problem. There are two constraints involved which are only three colours are allowed to be used and adjacent regions in the map must not be of the same colour. The modified branch and bound algorithm uses back jumping when it encounters a dead-end in the search. Static variable ordering was also applied to aid the searching process. The modified branch and bound algorithm shows a better result in terms of the number of nodes instantiated and the reduced number of backtracking at dead ends. The result illustrated that the modified branch and bound algorithm with the use of variable ordering technique is better if compared to backjumping. Thus, it is concluded that the modified branch and bound algorithm would improve constraint satisfaction problem.
Analysis and Significance of Anti-Latent Membrane Protein-1 Antibodies in the Sera of Patients with EBV-Associated Diseases (2000)

Anti-latent membrane protein-1 (LMP-1) is an EBV-encoded type III integral membrane protein with oncogenic potential that is expressed most consistently in various EBV-associated malignancies. Unlike many other EBV proteins, LMP-1 Abs have rarely been demonstrated in EBV-associated disease conditions. We established a high level LMP-1-expressing cell clone and used it for the detection, quantitation, and characterization of these Abs in various human sera in immunoblots and ELISA. Our results demonstrate that, in contrast to the commonly held notion, LMP-1 induces significant humoral immune responses in EBV-associated malignant conditions especially in nasopharyngeal carcinoma (NPC) patients in whom >70% sera are positive for these Abs, and their titers correlate with the clinical condition of the tumors. Interestingly, anti-LMP-1 Abs of IgA isotype were found only in NPC patients. These Abs were absent from the sera of infectious mononucleosis and chronic EBV infection patients, whereas a small fraction ( approximately 5%) of the healthy, EBV-seropositive individuals were positive for them; however, their OD values were much lower than those of NPC patients. These studies demonstrate, for the first time, the potential significance of LMP-1-specific Abs for the diagnosis and prognosis of EBV-associated malignancies, especially of NPC.
Diabetogenic T cells recognize insulin bound to IA <sup>g7</sup> in an unexpected, weakly binding register (2010)

A peptide derived from the insulin B chain contains a major epitope for diabetogenic CD4(+) T cells in the NOD mouse model of type 1 diabetes (T1D). This peptide can fill the binding groove of the NOD MHCII molecule, IA(g7), in a number of ways or "registers." We show here that a diverse set of NOD anti-insulin T cells all recognize this peptide bound in the same register. Surprisingly, this register results in the poorest binding of peptide to IA(g7). The poor binding is due to an incompatibility between the p9 amino acid of the peptide and the unique IA(g7) p9 pocket polymorphisms that are strongly associated with susceptibility to T1D. Our findings suggest that the association of autoimmunity with particular MHCII alleles may be do to poorer, rather than more favorable, binding of the critical self-epitopes, allowing T-cell escape from thymic deletion.
Automatic extraction of notions from course material (2008)

Formally defining the knowledge units taught in a course helps instructors ensure a sound coverage of topics and provides an objective basis for comparing the content of two courses. The main issue is to list and define the course concepts, down to basic knowledge units. Ontology learning techniques can help partially automate the process by extracting information from existing materials such as slides and textbooks. The TrucStudio course planning tool, discussed in this article, provides such support and relies on Text2Onto to extract concepts from course material. We conducted experiments on two different programming courses to assess the quality of the results.
Multichannel Boron Doped Nanocrystalline Diamond Ultramicroelectrode Arrays: Design, Fabrication and Characterization (2012)

We report on the fabrication and characterization of an 8 × 8 multichannel Boron Doped Diamond (BDD) ultramicro-electrode array (UMEA). The device combines both the assets of microelectrodes, resulting from conditions in mass transport from the bulk solution toward the electrode, and of BDD's remarkable intrinsic electrochemical properties. The UMEAs were fabricated using an original approach relying on the selective growth of diamond over pre-processed 4 inches silicon substrates. The prepared UMEAs were characterized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The results demonstrated that the electrodes have exhibited a very fast electrode transfer rate (k(0)) up to 0.05 cm·s(-1) (in a fast redox couple) and on average, a steady state limiting current (in a 0.5 M potassium chloride aqueous solution containing 1 mM Fe(CN)(6)(4-) ion at 100 mV·s(-1)) of 1.8 nA. The UMEAs are targeted for electrophysiological as well as analytical applications.
Flight-Test Evaluation of an Adaptive Controller for Flying Qualities Specification and Protection (2015)

The human pilot is a crucial component of the pilot/vehicle system. This article focuses on the in-flight ability of an adaptive receding horizon optimal controller to specify favorable flying qualities. Moreover, in the case of a deteriorated vehicle, the controller serves to protect the desirable flying qualities by automatically compensating for the new aircraft characteristics. Although the receding horizon optimal controller is able to protect the handling qualities as long as adequate physical control authority exists, particularly severe failures can result in heavily limited authority where encounters with actuator rate limits may occur. The controller itself is robust to rate-limit encounters, however, the handling qualities suffer because the authority no longer exists to deliver the performance expected by the pilot. To protect handling qualities and mitigate the risk of pilot-induced oscillation past this exhaustion of control authority, the controller is integrated into the Smart Adaptive Flight Effective Cue system (detailed elsewhere). The piloted flight tests demonstrate the capability of the receding horizon optimal controller to 1) deliver specified flying qualities, 2) protect those flying qualities in failure cases where adequate authority exists, and 3) operate in tandem with the Smart Adaptive Flight Effective Cue system to protect the pilot/vehicle system in cases of severely limited authority.
Advanced network management needs for optical networks (1990)

The authors discuss current and future optical network architectures and effective strategies for efficient network management. It is noted that, to assure the best possible network performance, network management equipment must not only provide surveillance and diagnostic data but must also provide access to all channels embedded in the transport. The T3AS monitor, access, and test system has been developed to provide integrated transport access unit (ITAU) functionality. The T3AS monitors and tests all levels of multiplexed traffic, thereby enabling widespread application, both carrier to carrier and within a carrier's own network.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Text Classification Based on Hybrid CNN-LSTM Hybrid Model (2018)

Aiming at the traditional methods of text classification, the dimensions need to be reduced, the features are extracted manually, and the classification accuracy is poor, furthermore, convolutional neural network CNN can only extract local information, cannot better express context information, long short-term memory network LSTM can extract context dependencies, and the classification effect is good, but the training time is long, a text classification algorithm based on hybrid CNN-LSTM hybrid model is proposed. The algorithm uses the Skip-Gram (continuous skip-gram) model and the CBOW (continuous bag-of-words) model in word2vec to represent words as vector, using CNN to extract local features of text, LSTM saves historical information, extracts contextual dependencies of text, and uses the feature vector output by CNN as the input of LSTM, using Softmax classifier for classification. Tests on the Chinese news corpus of Sogou.com show that the algorithm can effectively improve the precision of text classification.
Segmentation Driven Object Detection with Fisher Vectors (2013)

We present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and storage efficiency, we use a recent segmentation-based method to generate class-independent object detection hypotheses, in combination with data compression techniques. Our main contribution is a method to produce tentative object segmentation masks to suppress background clutter in the features. Re-weighting the local image features based on these masks is shown to improve object detection significantly. We also exploit contextual features in the form of a full-image FV descriptor, and an inter-category rescoring mechanism. Our experiments on the VOC 2007 and 2010 datasets show that our detector improves over the current state-of-the-art detection results.
Boron Nitride as a Selective Gas Adsorbent (1994)

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTBoron Nitride as a Selective Gas AdsorbentJurik F. Janik, William C. Ackerman, Robert T. Paine, Duen-Wu Hua, Alok Maskara, and Douglas M. SmithCite this: Langmuir 1994, 10, 2, 514–518Publication Date (Print):February 1, 1994Publication History Published online1 May 2002Published inissue 1 February 1994https://pubs.acs.org/doi/10.1021/la00014a029https://doi.org/10.1021/la00014a029research-articleACS PublicationsRequest reuse permissionsArticle Views604Altmetric-Citations50LEARN 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
Long-term application of wearable artificial endocrine pancreas--closed-loop intravenous vs subcutaneous insulin infusion. (1985)

For closed-loop glycemic control on IDDM, closed-loop sc insulin infusion algorithm has developed by analysing the pharmacokinetics of sc insulin absorption. With the algorithm, daily glycemic excursion in IDDMs was controlled to near-normal levels. These data suggested that the closed-loop sc insulin infusion algorithm might be feasible to regulate glycemia in diabetic patients for a long-term period by wearable artificial endocrine pancreas.
Comparison of different algorithms for simultaneous estimation of multiple parameters in kinetic metabolic models (2010)

Summary Computational models in systems biology are usually characterized by a lack of reliable parameter values. This is especially true for kinetic metabolic models. Experimental data can be used to estimate these missing parameters. Different optimization techniques have been explored to solve this challenging task but none has proved to be superior to the other. In this paper we review the problem of parameter estimation in kinetic models. We focus on the suitability of four commonly used optimization techniques of parameter estimation in biochemical pathways and make a comparison between those methods. The suitability of each technique is evaluated based on the ability of converging to a solution within a reasonable amount of time. As most local optimization methods fail to arrive at a satisfactory solution we only considered the global optimization techniques. A case study of the upper part of Glycolysis consisting 15 parameters is taken as the benchmark model for evaluating these methods.
Pin-Printed Chemical Sensor Arrays for Simultaneous Multianalyte Quantification (2002)

A new approach to rapidly produce micrometer-scale sensor elements into reusable multianalyte chemical sensor arrays is demonstrated. By using pin printing technology in concert with sol−gel processing methods, we form discrete xerogel-based microsensors on a planar substrate. We illustrate the new approach by forming discrete O2- and pH-responsive sensing elements into arrays that allow one to simultaneously determine O2 and pH in aqueous samples. The pin printing method allows one to prepare sensor elements that are on the order of 100 μm in diameter, 1−2 μm thick, at a rate of approximately one sensor element per second with a single pin. Within a given calibrated array, the sensor element-to-sensor element response is reproducible to within 5%, the sensor element short- and long-term reproducibilities are 3 and 6%, respectively, and the array-to-array response reproducibility is 11%. These results demonstrate the potential of this methodology for rapidly forming ensembles of reusable sensor arrays for simultaneous multianalyte detection.
Java Deep Learning Projects (2018)

Build and deploy powerful neural network models using the latest Java deep learning librariesAbout This BookUnderstand DL with Java by implementing real-world projectsMaster implementations of various ANN models and build your own DL systemsDevelop applications using NLP, image classification, RL, and GPU processingWho This Book Is ForIf you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.What You Will LearnMaster deep learning and neural network architecturesBuild real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIsTrain ML agents to learn from data using deep reinforcement learningUse factorization machines for advanced movie recommendationsTrain DL models on distributed GPUs for faster deep learning with Spark and DL4JEase your learning experience through 69 FAQsIn DetailJava is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts.Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines.You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you'll be able to use their features to build and deploy projects on distributed computing environments.You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks.By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems.Style and approachA unique, learn-as-you-do approach, as the reader builds on his understanding of deep learning with Java progressively with each project. This book is designed in such a way that implementing each project will empower you with a unique skill set, and enable you to implement the next project more confidently.
Role of Polypeptide GalNAc-transferase T3 in Familial Tumoral Calcinosis: The Importance of a Single GalNAc-transferase Isoform (2007)

UDP-GalNAc : ポリペプチドN-アセチルガラクトサミン転移酵素 (GalNAc-転移酵素、GalNAc-T) はタンパク質のムチン型O-グリコシレーション (GalNAcα1-O-Ser/Thr) を触媒する酵素群である。GalNAc-T3の遺伝子変異が常染色体劣性代謝性疾患である家族性腫瘍状石灰化症 (familial tumoral calcinosis、FTC) (OMIM 211900) の原因となることが近年明らかにされた。FTCは高リン血症および異所性石灰化を伴う。我々はリン酸代謝調節因子であるFGF23の分泌にO-グリコシレーションが必要であること、およびGalNAc-T3がスブチリシン様プロプロテインコンベルターゼにより認識される配列を特異的にO-グリコシレーションし、その修飾がfurinプロテアーゼによるFGF23のプロセシングを阻害することを明らかにした。これらの結果は完全長のFGF23を産生する際のO-グリコシレーションとプロテアーゼによるプロセシングとの競合、というFGF23の新規翻訳後修飾制御モデルを示唆している。