04281nam 22007451 450 991045135840332120100317081752.01-4725-6298-41-280-80096-897866108009641-84731-208-X10.5040/9781472562982(CKB)1000000000338505(EBL)285371(OCoLC)476036473(SSID)ssj0000100013(PQKBManifestationID)11566155(PQKBTitleCode)TC0000100013(PQKBWorkID)10015800(PQKB)10542971(MiAaPQ)EBC285371(MiAaPQ)EBC1772372(OCoLC)1148125180(UtOrBLW)bpp09256518(Au-PeEL)EBL285371(EXLCZ)99100000000033850520140929d2004 uy 0engur|n|---|||||txtccrAfter national democracy rights, law and power in America and the new Europe /edited by Lars Trägardh1st ed.Oxford ;Portland :Hart Publishing,2004.1 online resource (180 p.)Onati international series in law and society"A series published for the Onati Institute for the Sociology of Law"--T.pages1-84113-328-0 Includes bibliographical references.1. Introduction /Lars Trägardh --2.Normative theory and the EU : legitimising the Euro-polity and its regime /Richard Bellamy, Dario Castiglione --3.The juridification of politics in the United States and Europe : historical roots, contemporary debates and future prospects /Lars Trägardh, Michael X. Delli Carpini --4.Rights and Regulations in (the) Europe(an Union) : after national democracy? /Daniel Wincott --5.Constitutional moments /Juliet Williams --6.Law and politics in a Madisonian republic : opportunities and challenges for judges and citizens in the new Europe /Lisa Hilbink --7.Democracy beyond nation and rule? Reflections on the democratic possibilities of proceduralism /Warren Breckman."The "imagined community" of the nation,which served as the affective basis for the post-French Revolution social contract, as well as its institutional counter-part, the welfare state, are currently under great stress as states lose control over what once was referred to as the "national economy" In this book a number of authors - historians, legal scholars, political theorists - consider the fate of national democracy in the age of globalization. In particular, the authors ask whether the order of European nation-states, with its emphasis on substantive democracy, is now, in the guise of the European Union, giving way to a more loosely constructed, often federalized system of procedural republics (partly constructed in the image of the United States). Is national parliamentary democracy being replaced by a politico-legal culture, where citizen action increasingly takes place in a transnational legal domain at the expense of traditional (and national) party politics? Is the notion of a nationally-bound citizen in the process of being superceded by a cosmopolitan legal subject?"--Bloomsbury Publishing.Onati international series in law and society.Constitutional historyUnited StatesDemocracyEuropean Union countriesLegitimacy of governmentsEuropean Union countriesNation-stateJurisprudence & philosophy of lawEuropean Union countriesPolitics and governmentEuropean Union countriesPolitics and governmentUnited StatesPolitics and governmentElectronic books.Constitutional historyDemocracyLegitimacy of governmentsNation-state.320.1Trägardh LarsOnati International Institute for the Sociology of Law.UtOrBLWUtOrBLWBOOK9910451358403321After national democracy2470647UNINA03453nam 2201045z- 450 991063999110332120231214132834.03-0365-5820-9(CKB)5470000001633443(oapen)https://directory.doabooks.org/handle/20.500.12854/95832(EXLCZ)99547000000163344320202301d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAntimicrobial Peptides Aka Host Defense Peptides – From Basic Research to TherapyBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (226 p.)3-0365-5819-5 This Special Issue reprint will address the most current and innovative developments in the field of HDP research across a range of topics, such as structure and function analysis, modes of action, anti-microbial effects, cell and animal model systems, the discovery of novel host-defense peptides, and drug development.Research & information: generalbicsscChemistrybicsscECPAMPsinfectionmurine modelGram-negative bacteriaLPShost defense peptidesmonocytesneutrophilsneutrophil–monocyte interactionextracellular trapshost defense peptideinnate immunityNF-κBpoly I:Ctoll-like receptor 3mucus of Cornu aspersumpeptide fraction MW < 10 kDaEscherichia coli NBIMCC 8785SEMfluorescence and digital assaysantibacterial effectoral cavityhuman cathelicidinantimicrobial peptidesimmunomodulationoncolytic peptidescancermembrane integritybulky non-nature amino acidDAMPsdrug designantimicrobial peptidomimetichydrogel-based systemhyaluronic acidanti-infective activityskin infectionsantimicrobial peptideexpressioninteinself-cleavagececropin-likeapolipoprotein Ehost defenseaggregationbody fluidAMPinteraction networkchemical barriercationic antimicrobial peptideslength dependent activityantimicrobial activityhemolysisvesicle leakagesolid-state 31P-, 15N- and 19F-NMRβ-stranded peptidesβ-sheetsstructure and orientation of peptides in membranesResearch & information: generalChemistryPetrlova Jitkaedt1290749Petrlova JitkaothBOOK9910639991103321Antimicrobial Peptides Aka Host Defense Peptides – From Basic Research to Therapy3021581UNINA06289nam 2201789z- 450 991040407710332120231214133549.03-03928-924-1(CKB)4100000011302367(oapen)https://directory.doabooks.org/handle/20.500.12854/49630(EXLCZ)99410000001130236720202102d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierHuman Milk and LactationMDPI - Multidisciplinary Digital Publishing Institute20201 electronic resource (368 p.)3-03928-923-3 Human milk is uniquely tailored to meet infants’ specific nutritional requirements. However, it is more than just “milk”. This dynamic and bioactive fluid allows mother–infant signalling over lactation, guiding the infant in the developmental and physiological processes. It exerts protection and life-long biological effects, playing a crucial role in promoting healthy growth and optimal cognitive development. The latest scientific advances have provided insight into different components of human milk and their dynamic changes over time. However, the complexity of human milk composition and the synergistic mechanisms responsible for its beneficial health effects have not yet been unravelled. Filling this knowledge gap will shed light on the biology of the developing infant and will contribute to the optimization of infant feeding, particularly that of the most vulnerable infants. Greater understanding of human milk will also help in elucidating the best strategies for its storage and handling. The increasing knowledge on human milk’s bioactive compounds together with the rapidly-advancing technological achievements will greatly enhance their use as prophylactic or therapeutic agents. The current Special Issue aims to welcome original works and literature reviews further exploring the complexity of human milk composition, the mechanisms underlying the beneficial effects associated with breastfeeding, and the factors and determinants involved in lactation, including its promotion and support.high pressure processinglipidssupplementationprotective factorsinfantcarbohydratemothersantioxidant capacityproteinfatcytokinesbioactive factorslate pretermzincinfantsdocosahexaenoic acid (DHA)pregnancyeicosapentaenoic acid (EPA)Lipidomicsmagnesiumomega-3 fatty acidsvitamin D deficiencyflow injection analysishuman milk benefitsmultiple source method3?-sialyllactose (3?SL)milk bankingmilk grouppasteurizationvideo instructionMilk Fat Globule Membranebile salt stimulated lipasebreastfeeding difficultiesbreastfeeding supportprematuritycarotenoidshormonesphosphocholineamino acidstargeted metabolomicshigh-performance liquid chromatography (HPLC)cholineselenium?-linolenic acidarachidonic acid (ARA)docosahexaenoic acidhuman milk fortificationprotease inhibitorsceliac diseasecoppertermadipokinesiodinemammary glandnutritional statusfood frequency questionnaireneonateearly breastfeeding cessationprospective studybreastfeedingmothers' own milkdisialyllacto-N-tetraose (DSLNT)countrylactating womenundernourishmentproteasespretermexpressingdietary assessmentretinolbody compositionduration of lactationpassive immunization2?-fucosyllactose (2?FL)phosphorusclinical trialgrowth factorsinfant formuladigestive tracthuman milk oligosaccharides (HMO)sodiumnutritioneicosapentaenoic acidlipid metaboliteslactationnervonic acid?-tocopherolmacronutrientsglycoproteinterm infantterm infantsmaternal dietpromotion of breastfeedingpotassiumantioxidantsmaternal immunoglobulinsHuman Milkhuman milkPhospholipidsflu vaccinelactational stagelactosestoragedietary intakePreterm infantimmune-active proteinscolostrumhuman milk fatinadequate intakemilk therapyendogenous peptidecalciumfatty acidsbreast milkpumpingsecretorLC-MSn-9 fatty acidLewisdonor human milkantenatalonlineirongrowthdonor milkGianni Maria Lorellaauth1323396BOOK9910404077103321Human Milk and Lactation3035494UNINA13439nam 22008655 450 991048372520332120230329175309.03-319-46675-510.1007/978-3-319-46675-0(CKB)3710000000872958(DE-He213)978-3-319-46675-0(MiAaPQ)EBC6303727(MiAaPQ)EBC5592785(Au-PeEL)EBL5592785(OCoLC)960195096(PPN)195511336(EXLCZ)99371000000087295820160928d2016 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierNeural Information Processing 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III /edited by Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (XVIII, 651 p. 215 illus.) Theoretical Computer Science and General Issues,2512-2029 ;99493-319-46674-7 Intro -- Preface -- Organization -- Contents - Part III -- Time Series Analysis -- Chaotic Feature Selection and Reconstruction in Time Series Prediction -- 1 Introduction -- 2 Chaotic Feature Selection and Reconstruction -- 2.1 Cooperative Neuro-Evolution -- 3 Experiments and Results -- 3.1 Problem Description -- 3.2 Experimental Design -- 3.3 Results and Discussion -- 4 Conclusions and Future Work -- References -- L1/2 Norm Regularized Echo State Network for Chaotic Time Series Prediction -- Abstract -- 1 Introduction -- 2 Echo State Networks -- 3 L1/2 Regularized Echo State Network -- 4 Simulations -- 5 Conclusions -- Acknowledgement -- References -- SVD and Text Mining Integrated Approach to Measure Effects of Disasters on Japanese Economics -- Abstract -- 1 Introduction -- 2 SVD and Text Mining Integrated Approach -- 3 Time Series Stock Data Analysis -- 4 Topic Extraction Results -- 5 Conclusion -- Acknowledgement -- References -- Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting -- Abstract -- 1 Introduction -- 2 DBN with BP Learning (The Conventional Method) -- 2.1 DBN with RBM and MLP -- 3 DBN with SGA (The Proposed Method) -- 3.1 The Structure of ANNs with SGA -- 4 Prediction Experiments and Results -- 4.1 CATS Benchmark Time Series Data -- 4.2 Optimization of Meta Parameters -- 4.3 Experiments Result -- 5 Conclusion -- Acknowledgment -- References -- Neuron-Network Level Problem Decomposition Method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction -- 1 Introduction -- 2 Neuron-Network Level Problem Decomposition -- 3 Experimental Setup -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Data-Driven Approach for Extracting Latent Features from Multi-dimensional Data -- Yet Another Schatten Norm for Tensor Recovery -- 1 Introduction.2 Theoretical Results -- 2.1 New Norm -- 2.2 Properties -- 2.3 Tensor Recovery -- 3 Experimental Results -- 4 Conclusion -- References -- Memory of Reading Literature in a Hippocampal Network Model Based on Theta Phase Coding -- 1 Introduction -- 2 Computer Simulation -- 3 Results -- 4 Discussion -- References -- Combining Deep Learning and Preference Learning for Object Tracking -- 1 Introduction -- 2 Deep and Preference Learning Tracker -- 2.1 Deep Learning -- 2.2 Preference Learning -- 2.3 Model Update -- 3 Experiments -- 4 Conclusions -- References -- A Cost-Sensitive Learning Strategy for Feature Extraction from Imbalanced Data -- 1 Introduction -- 2 A Motivating Example -- 3 Theoretical Analysis -- 3.1 Imbalance Cost Ratio -- 3.2 Cost-Sensitive Principal Component Analysis (CSPCA) -- 3.3 Cost-Sensitive Non-negative Matrix Factorization (CSNMF) -- 3.4 Revisiting the Motivating Example -- 4 Experiments and Analysis -- 4.1 Experimental Framework -- 4.2 Analysis and Results -- 5 Conclusions and Future Work -- References -- Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering -- 1 Introduction -- 2 Nonnegative Tensor Decomposition Models -- 2.1 NTD Model -- 2.2 NTT Model -- 2.3 NTT-Tucker Model: A Hybrid of the NTD and NTT Models -- 3 NTT-HALS: Proposed Algorithm for NTT and NTT-Tucker -- 3.1 NTT-HALS for NTT -- 3.2 NTT-HALS for NTT-Tucker -- 4 Experiments -- 4.1 Multi-domain Feature Extraction from ERP Data -- 4.2 Feature Extraction and Clustering from ORL Database of Face Images -- 5 Discussion -- References -- Hyper-parameter Optimization of Sticky HDP-HMM Through an Enhanced Particle Swarm Optimization -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Method: Hyperparameter Optimization -- 4.1 Random Search -- 4.2 Particle Swarm Optimization -- 4.3 Ring-Based Particle Swarm Optimization.5 Experiment -- 5.1 Dataset -- 5.2 Synthetic Data Results -- 5.3 Tum Kitchen Dataset Results -- 6 Conclusion -- References -- Approximate Inference Method for Dynamic Interactions in Larger Neural Populations -- 1 Introduction -- 2 Methods -- 2.1 The State-Space Model of Neural Interactions -- 2.2 Approximation Methods for a Large-Scale Analysis -- 3 Results -- 4 Conclusion -- References -- Features Learning and Transformation Based on Deep Autoencoders -- 1 Introduction -- 2 Unsupervised Transformation of the Feature Space -- 2.1 Matrix Decomposition and Normalization -- 2.2 Diffusion Maps -- 2.3 Deep Autoencoders -- 3 Topological Clustering -- 4 Experimental Results -- 5 Conclusion -- References -- t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom -- 1 Introduction -- 2 Stochastic Neighbor Embedding -- 3 t-Distributed Stochastic Neighbor Embedding -- 4 Inhomogeneous t-SNE -- 4.1 Degrees of Freedom -- 4.2 Cost Function and Its Gradient -- 4.3 Optimization -- 5 Experiments -- 5.1 Experiment 1 -- 5.2 Experiment 2 -- 6 Summary and Discussion -- References -- Topological and Graph Based Clustering Methods -- Parcellating Whole Brain for Individuals by Simple Linear Iterative Clustering -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Subjects -- 2.2 Simple Linear Iterative Clustering (SLIC) -- 2.3 Evaluation Metrics -- 3 Experimental Results -- 4 Conclusion and Future Directions -- Acknowledgements -- References -- Overlapping Community Structure Detection of Brain Functional Network Using Non-negative Matrix Factorization -- 1 Introduction -- 2 Methods and Material -- 2.1 Association Matrix Construction with NASR -- 2.2 Community Detection with SNMF -- 2.3 Data Preparation -- 2.4 Experiment -- 3 Results and Discussion -- 3.1 Simulated Data Set -- 3.2 Real Resting-State fMRI Data Set.4 Conclusion and Limitation -- References -- Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite Images -- 1 Introduction -- 2 Multi-scale Communication Between Different Algorithms -- 3 Experimental Results -- 3.1 Description of the Data -- 3.2 Results -- 4 Conclusion -- References -- Towards Ontology Reasoning for Topological Cluster Labeling -- 1 Introduction and Motivations -- 2 Related Work -- 3 Preliminaries About Ontology and Reasoning -- 4 Hybrid Approach: SOM Ontology Based Labeling -- 4.1 Topological Unsupervised Learning Step -- 4.2 Ontology Based Map Labeling -- 5 Experiments -- 5.1 Satellite Images Classification -- 6 Conclusion and Future Work -- References -- Overlapping Community Detection Using Core Label Propagation and Belonging Function -- 1 Introduction -- 2 Label Propagation Algorithm -- 2.1 Standard Label Propagation -- 2.2 Label Propagation with Dams and Core Detection -- 3 Proposed Methods for Detection of Overlapping Communities -- 3.1 Function 1: Membership Function Based on the Density -- 3.2 Function 2 Membership Function Based on the Local Clustering Coefficient -- 3.3 Proposed Community Detection Algorithms -- 4 Evaluation Measures of Community Detection Algorithm, Benchmarks, Experiments and Discussion -- 4.1 Experiments -- 4.2 Comparative Analysis -- 5 Perspectives and Conclusion -- References -- A New Clustering Algorithm for Dynamic Data -- 1 Introduction -- 2 Growing Neural Gas -- 3 A New Two-Level Clustering Algorithm for GNG -- 4 Experimental Results -- 5 Conclusions and Perspectives -- References -- Reinforcement Learning -- Decentralized Stabilization for Nonlinear Systems with Unknown Mismatched Interconnections -- 1 Introduction -- 2 Problem Statement -- 3 Decentralized Controller Design and Stability Analysis -- 3.1 Optimal Control -- 3.2 Neural Network Implementation.3.3 Stability Analysis -- 4 Simulation Study -- 5 Conclusion -- References -- Optimal Constrained Neuro-Dynamic Programming Based Self-learning Battery Management in Microgrids -- 1 Introduction -- 2 Problem Formulation -- 3 Iterative ADP Algorithm for Battery Management System -- 3.1 Derivations of the Iterative ADP Algorithm -- 4 Simulation Analysis -- 5 Conclusion -- References -- Risk Sensitive Reinforcement Learning Scheme Is Suitable for Learning on a Budget -- 1 Introduction -- 2 Incremental Learning on a Budget -- 2.1 Kernel Regression Based Learning on a Budget -- 2.2 Kernel Replacement Algorithm -- 3 Risk Sensitive Reinforcement Learning -- 4 Experimental Results -- 4.1 Results -- 5 Conclusion -- References -- A Kernel-Based Sarsa() Algorithm with Clustering-Based Sample Sparsification -- 1 Introduction -- 2 RL and Kernel Method -- 3 Clustering-Based Selective Kernel Sarsa() -- 3.1 Clustering-Based Novelty Criterion -- 3.2 Framework of CSKS() -- 4 Experiment and Results -- 4.1 Settings of Acrobot -- 4.2 Results -- 5 Conclusion -- References -- Sparse Kernel-Based Least Squares Temporal Difference with Prioritized Sweeping -- 1 Introduction -- 2 Background -- 3 Sparse Kernel-Based Least Squares Temporal Difference with Prioritized Sweeping (PS-SKLSTD) -- 3.1 Sparse Kernel-Based Least Squares Temporal Difference -- 3.2 Kernel-Based Prioritized Sweeping -- 3.3 PS-SKLSTD Algorithm -- 4 Experimental Results -- 4.1 Puddle World -- 4.2 Cart-Pole -- 5 Conclusions -- References -- Computational Intelligence -- Vietnamese POS Tagging for Social Media Text -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Tagging Method -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Models to Compare -- 5.3 Results -- 6 Conclusion -- References -- Scaled Conjugate Gradient Learning for Quaternion-Valued Neural Networks -- 1 Introduction -- 2 The HR Calculus.3 Conjugate Gradient Algorithms.The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitues the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms.Theoretical Computer Science and General Issues,2512-2029 ;9949Pattern recognition systemsComputer visionArtificial intelligenceComputer scienceData miningApplication softwareAutomated Pattern RecognitionComputer VisionArtificial IntelligenceTheory of ComputationData Mining and Knowledge DiscoveryComputer and Information Systems ApplicationsPattern recognition systems.Computer vision.Artificial intelligence.Computer science.Data mining.Application software.Automated Pattern Recognition.Computer Vision.Artificial Intelligence.Theory of Computation.Data Mining and Knowledge Discovery.Computer and Information Systems Applications.006.3Hirose Akiraedthttp://id.loc.gov/vocabulary/relators/edtOzawa Seiichiedthttp://id.loc.gov/vocabulary/relators/edtDoya Kenjiedthttp://id.loc.gov/vocabulary/relators/edtIkeda Kazushiedthttp://id.loc.gov/vocabulary/relators/edtLee Minhoedthttp://id.loc.gov/vocabulary/relators/edtLiu Derongedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910483725203321Neural Information Processing2554499UNINA