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• Le Cid. Plus de tourments / Massenet (3:33) ; La Juive. Il va venir / Halévy (3:34) (Eileen Farrell)
• Lucia di Lammermoor. Cruda funesta smania / Donizetti (Richard Bonelli) (3:44)
• Lucia di Lammermoor. Soffriva nel pianto / Donizetti (Lily Pons and Richard Bonelli) (6:46)
• Lakmé. Sous le dôme épais / Delibes (Lily Pons and Irra Petina) (3:46)
• Cavalleria rusticana. Voi lo sapete / Mascagni (Licia Albanese) (3:37)
• Tosca. Perchè chiuso? / Puccini (Licia Albanese and Jan Peerce) (8:21)-- Die Walküre. Winterstürmme / Wagner (Lauritz Merchior) (2:36)
• Lohengrin. Einsam in trüben Tagen / Wagner (Astrid Varnay) (3:10)
• Samson et Dalila. Vois ma misère / Saint-Saëns (Renè Maison) (5:44)
• Il castello di Kenilworth. Fuggi l'imagine / Donizetti (2:55) ; Les Vêpres Siciliennes. Merci, jeunes amies / Verdi (2:59) (Jean Dickenson)
• Aïda. O patria mia / Verdi (Norina Greco) (4:20)
• Andrea Chenier. La mamma morta (5:10) ; Manon Lescaut. In quelle trine morbide (2:42) (Stella Roman)
• Resurrection. Dieu de grâce / Alfano (Gladys Swarthout) (4:05)
• Etienne Marcel. O beaux rêves évanouis / Saint Saëns (Rose Dirman) (4:26) --Rigoletto. Tutte le feste ... Si, vendetta / Verdi (Hilda Reggiani and Robert Weede) (7:05).

• Il barbiere di Siviglia. Veramente torto e vero / Paisiello (Salvatore Baccaloni) (2:34)
• Macbeth. Vienti! t'affretta ... Or tutti sorgete (4:40) ; Die Walküre. Du bist der Lenz (1:42) (Regina Resnik)
• Cavalleria rusticana. Voi lo sapete (Helen Traubel) (3:03)
• Iris. Apri la tua finestra / Mascagni (Richard Crooks) (2:09)
• L'elisir d'amore. Chiedi all'aura lusinghiera (Bidú Sayão and Bruni Landi) (6:17)
• L'elisir d'amore. Prendi per me sei libero (Bidú Sayão) (3:49)
• Otello. Già nella notte densa (Jean Tennyson and Mario Lanza) (9:30)
• Faust. Death of Valentin (Leonard Warren) (3:21)
• Otello. Si, pel ciel (Leonard Warren and Set Svanholm) (3:23)
• Aïda. Già i sacerdoti adunansi (Set Svanholm and Blanche Thebom) (7:09)
• Carmen. Habanera (3:39) ; Mignon. Connais-tu le pays (4:51) (Vivian Della Chiesa)
• Tosca. La povera mia cena through Vissi d'arte (Grace Moore and Lawrence Tibbett) (9:08)
• La forza del destino. Madre pietosa vergine (4:11) ; Appendix: Die tote Stadt. Marietta's song / Korngold (Eileen Farrell) (4:35).

• Nursing Process Guidelines
• Patient Safety and Medication Administration
• Pharmacologic Classes
• Alphabetical Listing of Drugs by Generic Name.

Self help health & nutrition, diet and illness awareness knowledge.

• 24 preludes op. 11
• Sonata no. 4, op. 30
• Sonata no. 10, op. 70
• Prelude op. 51, no. 2
• Prelude op. 49, no. 2
• Rêverie op. 49, no. 3
• 3 morceaux op. 45
• Poème languide op. 52, no. 3
• Danse languide op. 51, no. 4
• 2 morceaux op. 57.

• pt. 1. New antibiotics. Side effects of modern chemotherapy
• pt. 2. Antiviral chemotherapy
• pt. 3. Antineoplastic chemotherapy
• pt. 4. New penicillins. Chemo antibiotics. New antibiotics
• pt. 5. Antibiotics. Chemoprophylaxy and chemotherapy of lesions caused by ionising radiations. Sulfonamides
• pt. 6. Nitrofurans.

• Duerme negrito : arrullo (2:52)
• Los ejes de mi carreta : milonga (2:50)
• Preguntitas sobre Dios : canción (3:40)
• La del campo : chacarera (2:05)
• Nada mas : hommage à Ernesto Guevara (3:15)
• Trabajo, quiero trabajo : canción (2:55)
• Punay : canción india (3:12)
• El poeta (2:15)
• Hui, jo jo jo! (3:26)
• Campesino : canción (4:25)
• De aquellos cerros vengo (2:00)
• Basta ya (5:35)
• Soy libre! soy bueno! : baguala andina (3:52)
• El tulumbano : gato (1:42)
• Cancion para Pablo Neruda (4:00)
• Camino del Indio : canción (3:40)
• La olvidada : chacarera (2:20) --Danza de la paloma enamorada : danza (2:25)
• Pobrecito soy : canción (3:40)
• Guitarra de pobre : zamba (2:52).

• Volume 2: Section 1: Ligand-Protein Interactions. Progress in Force Field Calculations of Molecular Interaction Fields and Intermolecular Interactions
• T. Liljefors. Comparative Binding Energy Analysis
• R.C. Wade, et al. Receptor-Based Prediction of Binding Affinities
• T.I. Oprea, G.R. Marshall. A Priori Prediction of Ligand Affinity by Energy Minimization
• M.K. Holloway. Rapid Estimation of Relative Binding Affinities of Enzyme Inhibitors
• M.R. Reddy, et al. Binding Affinities and Non-Bonded Interaction Energies
• R.M.A. Knegtel, P.D.J. Grootenhuis. Molecular Mechanics Calculations on Protein-Ligand Complexes
• I.T. Weber, R.W. Harrison. Section 2: Quantum Chemical Models and Molecular Dynamics Simulations. Some Biological Applications of Semiempirical MO-Theory
• B. Beck, T. Clark. Density-Functional Theory and Molecular Dynamics: A New Perspective for Simulations of Biological Systems
• W. Andreoni. Density-Functional Theory Investigations of Enzyme-Substrate Interactions
• P. Carloni, F. Alber. Molecular Dynamics Simulations: A Tool for Drug Design
• D. Rognan. Section 3: Pharmacophore Modelling and Molecular Similarity. Bioisosterism and Molecular Diversity
• R.D. Clark, et al. Similarity and Dissimilarity - A Medicinal Chemist's View
• H. Kubinyi. Pharmacophore Modeling: Methods, Experimental Verification and Applications
• A.K. Ghose, J.J. Wendoloski. The Use of Self-Organizing Neural Networks in Drug Design
• S. Anzali, et al. Calculation of Structural Similarity by the Alignment of Molecular Electrostatic Potentials
• D.A. Thorner, et al. Explicit Calculation of 3D Molecular Similarity
• A.C. Good, W.G. Richards. Novel Software Tools for Chemical Similarity--R.S. Pearlman, K.M. Smith. New 3D Molecular Descriptors: The WHIM Theory and QSAR Applications
• R. Todeschini, P. Gramatica. EVA &endash
• A Novel Theoretical Descriptor for QSAR Studies
• T.W. Heritage, et al. Volume 3: Section 1: 3D QSAR Methodology. CoMFA and Related Approaches. 3D QSAR: Current State, Scope, and Limitations
• C. Martin. Recent Progress in CoMFA Methodology and Related Techniques
• U. Norinder. Improving the Predictive Quality of CoMFA Models
• T. Kroemer, et al. Cross-Validated R2 Guided Region Selection for CoMFA Studies
• A. Tropsha, S.J. Cho. GOLPE-Guided Region Selection
• G. Cruciani, et al. Comparative Molecular Similarity Indices Analysis - CoMSIA
• G. Klebe. Alternative Partial Least Squares (PLS) Algorithms
• F. Lindgren, S. Rannar. Section 2: Receptor Models and Other 3D QSAR Approaches. Receptor Surface Models
• M. Hahn, D. Rogers. Pseudoreceptor Modeling in Drug Design: Applications of Yak and PrGen
• M. Gurrath, et al. Genetically Evolved Receptor Models (GERM) as a 3D QSAR Tool
• D.E. Walters. 3D QSAR of Flexible Molecules Using Tensor Representation
• W.J. Dunn III, A.J. Hopfinger. Comparative Molecular Moment Analysis (CoMMA)
• B.D. Silverman, et al. Section 3: 3D QSAR Applications. The CoMFA Steroids as a Benchmark Data Set for Development of 3D QSAR Methods
• E.A. Coats. Molecular Similarity Characterization Using CoMFA
• T. Langer. Building a Bridge Between G-Protein-Coupled Receptor Modelling, Protein Crystallography, and 3D-QSAR Studies for Ligand Design
• K.H. Kim. A Critical Review on Recent CoMFA Applications
• K.H. Kim, et al. List of CoMFA References, 1993-1996
• K. H. Kim.
• (source: Nielsen Book Data)

Significant progress has been made in the study of three-dimensional quantitative structure-activity relationships (3D QSAR) since the first publication by Richard Cramer in 1988 and the first volume in the series. 3D QSAR in Drug Design. Theory, Methods and Applications, published in 1993. The aim of that early book was to contribute to the understanding and the further application of CoMFA and related approaches and to facilitate the appropriate use of these methods. Since then, hundreds of papers have appeared using the quickly developing techniques of both 3D QSAR and computational sciences to study a broad variety of biological problems. Again the editor(s) felt that the time had come to solicit reviews on published and new viewpoints to document the state of the art of 3D QSAR in its broadest definition and to provide visions of where new techniques will emerge or new appli- tions may be found. The intention is not only to highlight new ideas but also to show the shortcomings, inaccuracies, and abuses of the methods. We hope this book will enable others to separate trivial from visionary approaches and me-too methodology from in- vative techniques. These concerns guided our choice of contributors. To our delight, our call for papers elicited a great many manuscripts.
(source: Nielsen Book Data)

Progress in medicinal chemistry and in drug design depends on our ability to understand the interactions of drugs with their biological targets. Classical QSAR studies describe biological activity in terms of physicochemical properties of substituents in certain positions of the drug molecules. The purpose of this book is twofold: On the one hand, both the novice and the experienced user will be introduced to the theory and application of 3D QSAR analyses, and on the other, a comprehensive overview of the scope and limitations of these methods is given. The detailed discussion of the present state of the art should enable scientists to further develop and improve these powerful new tools. The greater part of the book is dedicated to the theoretical background of 3D QSAR and to a discussion of CoMFA applications.In addition, various other 3D QSAR approaches and some CoMFA-related methods are described in detail. Thus, the book should be valuable for medicinal, agricultural and theoretical chemists, biochemists and biologists, as well as for other scientists interested in drug design. Its content, starting at a very elementary level and proceeding to the latest methodological results, the strengths and limitations of 3D QSAR approaches, makes the book also appropriate as a text for teaching and for graduate student courses.
(source: Nielsen Book Data)

• Volume 2: Section 1: Ligand-Protein Interactions. Progress in Force Field Calculations of Molecular Interaction Fields and Intermolecular Interactions-- T. Liljefors. Comparative Binding Energy Analysis-- R.C. Wade, et al. Receptor-Based Prediction of Binding Affinities-- T.I. Oprea, G.R. Marshall. A Priori Prediction of Ligand Affinity by Energy Minimization-- M.K. Holloway. Rapid Estimation of Relative Binding Affinities of Enzyme Inhibitors-- M.R. Reddy, et al. Binding Affinities and Non-Bonded Interaction Energies-- R.M.A. Knegtel, P.D.J. Grootenhuis. Molecular Mechanics Calculations on Protein-Ligand Complexes-- I.T. Weber, R.W. Harrison. Section 2: Quantum Chemical Models and Molecular Dynamics Simulations. Some Biological Applications of Semiempirical MO-Theory-- B. Beck, T. Clark. Density-Functional Theory and Molecular Dynamics: A New Perspective for Simulations of Biological Systems-- W. Andreoni. Density-Functional Theory Investigations of Enzyme-Substrate Interactions-- P. Carloni, F. Alber. Molecular Dynamics Simulations: A Tool for Drug Design-- D. Rognan. Section 3: Pharmacophore Modelling and Molecular Similarity. Bioisosterism and Molecular Diversity-- R.D. Clark, et al. Similarity and Dissimilarity - A Medicinal Chemist's View-- H. Kubinyi. Pharmacophore Modeling: Methods, Experimental Verification and Applications-- A.K. Ghose, J.J. Wendoloski. The Use of Self-Organizing Neural Networks in Drug Design-- S. Anzali, et al. Calculation of Structural Similarity by the Alignment of Molecular Electrostatic Potentials-- D.A. Thorner, et al. Explicit Calculation of 3D Molecular Similarity-- A.C. Good, W.G. Richards. Novel Software Tools for Chemical Similarity--R.S. Pearlman, K.M. Smith. New 3D Molecular Descriptors: The WHIM Theory and QSAR Applications-- R. Todeschini, P. Gramatica. EVA
• A Novel Theoretical Descriptor for QSAR Studies-- T.W. Heritage, et al. Volume 3: Section 1: 3D QSAR Methodology. CoMFA and Related Approaches. 3D QSAR: Current State, Scope, and Limitations-- C. Martin. Recent Progress in CoMFA Methodology and Related Techniques-- U. Norinder. Improving the Predictive Quality of CoMFA Models-- T. Kroemer, et al. Cross-Validated R2 Guided Region Selection for CoMFA Studies-- A. Tropsha, S.J. Cho. GOLPE-Guided Region Selection-- G. Cruciani, et al. Comparative Molecular Similarity Indices Analysis - CoMSIA-- G. Klebe. Alternative Partial Least Squares (PLS) Algorithms-- F. Lindgren, S. Rannar. Section 2: Receptor Models and Other 3D QSAR Approaches. Receptor Surface Models-- M. Hahn, D. Rogers. Pseudoreceptor Modeling in Drug Design: Applications of Yak and PrGen-- M. Gurrath, et al. Genetically Evolved Receptor Models (GERM) as a 3D QSAR Tool-- D.E. Walters. 3D QSAR of Flexible Molecules Using Tensor Representation-- W.J. Dunn III, A.J. Hopfinger. Comparative Molecular Moment Analysis (CoMMA)-- B.D. Silverman, et al. Section 3: 3D QSAR Applications. The CoMFA Steroids as a Benchmark Data Set for Development of 3D QSAR Methods-- E.A. Coats. Molecular Similarity Characterization Using CoMFA-- T. Langer. Building a Bridge Between G-Protein-Coupled Receptor Modelling, Protein Crystallography, and 3D-QSAR Studies for Ligand Design-- K.H. Kim. A Critical Review on Recent CoMFA Applications-- K.H. Kim, et al. List of CoMFA References, 1993-1996-- K. H. Kim.
• (source: Nielsen Book Data)

Significant progress has been made in the study of three-dimensional quantitative structure-activity relationships (3D QSAR) since the first publication by Richard Cramer in 1988 and the first volume in the series. 3D QSAR in Drug Design. Theory, Methods and Applications, published in 1993. The aim of that early book was to contribute to the understanding and the further application of CoMFA and related approaches and to facilitate the appropriate use of these methods. Since then, hundreds of papers have appeared using the quickly developing techniques of both 3D QSAR and computational sciences to study a broad variety of biological problems. Again the editor(s) felt that the time had come to solicit reviews on published and new viewpoints to document the state of the art of 3D QSAR in its broadest definition and to provide visions of where new techniques will emerge or new appli- tions may be found. The intention is not only to highlight new ideas but also to show the shortcomings, inaccuracies, and abuses of the methods. We hope this book will enable others to separate trivial from visionary approaches and me-too methodology from in- vative techniques. These concerns guided our choice of contributors. To our delight, our call for papers elicited a great many manuscripts.
(source: Nielsen Book Data)
This volume, the second in the 3D QSAR in Drug Design series, aims to review the progress being made in CoMFA and other 3D QSAR approaches since the publication of the first volume.; It divides into three sections dealing with: ligand-protein interactions; quantum chemical models and molecular dynamics simulations; and pharmacophore modelling and molecular similarity, respectively.; Volume 3 Recent Advances is also divided into three sections, namely: 3D QSAR methodology - CoMFA and related approaches; receptor models and other 3D QSAR approaches; and 3D QSAR applications.; More than 70 scientists have contributed nearly 40 reviews of their work and related research to these two volumes, which present coverage of recent developments in all fields of 3D QSAR.
(source: Nielsen Book Data)

• Volume 2: Section 1: Ligand-Protein Interactions. Progress in Force Field Calculations of Molecular Interaction Fields and Intermolecular Interactions-- T. Liljefors. Comparative Binding Energy Analysis-- R.C. Wade, et al. Receptor-Based Prediction of Binding Affinities-- T.I. Oprea, G.R. Marshall. A Priori Prediction of Ligand Affinity by Energy Minimization-- M.K. Holloway. Rapid Estimation of Relative Binding Affinities of Enzyme Inhibitors-- M.R. Reddy, et al. Binding Affinities and Non-Bonded Interaction Energies-- R.M.A. Knegtel, P.D.J. Grootenhuis. Molecular Mechanics Calculations on Protein-Ligand Complexes-- I.T. Weber, R.W. Harrison. Section 2: Quantum Chemical Models and Molecular Dynamics Simulations. Some Biological Applications of Semiempirical MO-Theory-- B. Beck, T. Clark. Density-Functional Theory and Molecular Dynamics: A New Perspective for Simulations of Biological Systems-- W. Andreoni. Density-Functional Theory Investigations of Enzyme-Substrate Interactions-- P. Carloni, F. Alber. Molecular Dynamics Simulations: A Tool for Drug Design-- D. Rognan. Section 3: Pharmacophore Modelling and Molecular Similarity. Bioisosterism and Molecular Diversity-- R.D. Clark, et al. Similarity and Dissimilarity - A Medicinal Chemist's View-- H. Kubinyi. Pharmacophore Modeling: Methods, Experimental Verification and Applications-- A.K. Ghose, J.J. Wendoloski. The Use of Self-Organizing Neural Networks in Drug Design-- S. Anzali, et al. Calculation of Structural Similarity by the Alignment of Molecular Electrostatic Potentials-- D.A. Thorner, et al. Explicit Calculation of 3D Molecular Similarity-- A.C. Good, W.G. Richards. Novel Software Tools for Chemical Similarity--R.S. Pearlman, K.M. Smith. New 3D Molecular Descriptors: The WHIM Theory and QSAR Applications-- R. Todeschini, P. Gramatica. EVA
• A Novel Theoretical Descriptor for QSAR Studies-- T.W. Heritage, et al. Volume 3: Section 1: 3D QSAR Methodology. CoMFA and Related Approaches. 3D QSAR: Current State, Scope, and Limitations-- C. Martin. Recent Progress in CoMFA Methodology and Related Techniques-- U. Norinder. Improving the Predictive Quality of CoMFA Models-- T. Kroemer, et al. Cross-Validated R2 Guided Region Selection for CoMFA Studies-- A. Tropsha, S.J. Cho. GOLPE-Guided Region Selection-- G. Cruciani, et al. Comparative Molecular Similarity Indices Analysis - CoMSIA-- G. Klebe. Alternative Partial Least Squares (PLS) Algorithms-- F. Lindgren, S. Rannar. Section 2: Receptor Models and Other 3D QSAR Approaches. Receptor Surface Models-- M. Hahn, D. Rogers. Pseudoreceptor Modeling in Drug Design: Applications of Yak and PrGen-- M. Gurrath, et al. Genetically Evolved Receptor Models (GERM) as a 3D QSAR Tool-- D.E. Walters. 3D QSAR of Flexible Molecules Using Tensor Representation-- W.J. Dunn III, A.J. Hopfinger. Comparative Molecular Moment Analysis (CoMMA)-- B.D. Silverman, et al. Section 3: 3D QSAR Applications. The CoMFA Steroids as a Benchmark Data Set for Development of 3D QSAR Methods-- E.A. Coats. Molecular Similarity Characterization Using CoMFA-- T. Langer. Building a Bridge Between G-Protein-Coupled Receptor Modelling, Protein Crystallography, and 3D-QSAR Studies for Ligand Design-- K.H. Kim. A Critical Review on Recent CoMFA Applications-- K.H. Kim, et al. List of CoMFA References, 1993-1996-- K. H. Kim.
• (source: Nielsen Book Data)

Significant progress has been made in the study of three-dimensional quantitative structure-activity relationships (3D QSAR) since the first publication by Richard Cramer in 1988 and the first volume in the series, 3D QSAR in Drug Design. Theory, Methods and Applications, published in 1993. The aim of that early book was to contribute to the understanding and the further application of CoMFA and related approaches and to facilitate the appropriate use of these methods. Since then, hundreds of papers have appeared using the quickly developing techniques of both 3D QSAR and computational sciences to study a broad variety of biological problems. Again the editor(s) felt that the time had come to solicit reviews on published and new viewpoints to document the state of the art of 3D QSAR in its broadest definition and to provide visions of where new techniques will emerge or new appli- tions may be found. The intention is not only to highlight new ideas but also to show the shortcomings, inaccuracies, and abuses of the methods. We hope this book will enable others to separate trivial from visionary approaches and me-too methodology from in- vative techniques. These concerns guided our choice of contributors. To our delight, our call for papers elicited a great many manuscripts.
(source: Nielsen Book Data)

• No. 2 in F major, K. 280 (14:10)
• No. 3 in B-flat major, K. 281 (13:53)
• No. 4 in E-flat major, K. 282 (12:09)
• No. 5 in G major, K. 283 (13:53).

• 4D Image Acquisition. Helical 4D CT and Comparison with Cine 4D CT / Tinsu Pan
• Acquiring 4D Thoracic CT Scans Using Ciné CT Acquisition / Daniel Low
• Motion Estimation and Modeling. Biophysical Modeling of Respiratory Organ Motion / René Werner
• Feature-Based Registration Techniques / Cristian Lorenz, Tobias Klinder, Jens von Berg
• Intensity-Based Deformable Registration: Introduction and Overview / David Sarrut, Jef Vandemeulebroucke, Simon Rit
• Intensity-Based Registration for Lung Motion Estimation / Kunlin Cao [and others]
• Validation and Comparison of Approaches to Respiratory Motion Estimation / Sven Kabus [and others]
• Modeling of Motion Variability. Estimating Internal Respiratory Motion from Respiratory Surrogate Signals Using Correspondence Models / Jamie McClelland
• Computational Motion Phantoms and Statistical Models of Respiratory Motion / Jan Ehrhardt, Tobias Klinder, Cristian Lorenz
• Applications of Motion Estimation Algorithms. 4-Dimensional Imaging for Radiation Oncology: A Clinical Perspective / Max Dahele, Suresh Senan
• Respiratory Motion Prediction in Radiation Therapy / Sastry Vedam
• Estimation of Lung Ventilation / Kai Ding [and others]
• Respiratory Motion Correction in Cone-Beam CT for Image-Guided Radiotherapy / Simon Rit, David Sarrut, Jan-Jakob Sonke
• Introduction to 4D Motion Modeling and 4D Radiotherapy / Paul Keall, Tokihiro Yamamoto, Yelin Suh.

Respiratory motion causes an important uncertainty in radiotherapy planning of the thorax and upper abdomen. The main objective of radiation therapy is to eradicate or shrink tumor cells without damaging the surrounding tissue by delivering a high radiation dose to the tumor region and a dose as low as possible to healthy organ tissues. Meeting this demand remains a challenge especially in case of lung tumors due to breathing-induced tumor and organ motion where motion amplitudes can measure up to several centimeters. Therefore, modeling of respiratory motion has become increasingly important in radiation therapy. With 4D imaging techniques spatiotemporal image sequences can be acquired to investigate dynamic processes in the patient's body. Furthermore, image registration enables the estimation of the breathing-induced motion and the description of the temporal change in position and shape of the structures of interest by establishing the correspondence between images acquired at different phases of the breathing cycle. In radiation therapy these motion estimations are used to define accurate treatment margins, e.g. to calculate dose distributions and to develop prediction models for gated or robotic radiotherapy. In this book, the increasing role of image registration and motion estimation algorithms for the interpretation of complex 4D medical image sequences is illustrated. Different 4D CT image acquisition techniques and conceptually different motion estimation algorithms are presented. The clinical relevance is demonstrated by means of example applications which are related to the radiation therapy of thoracic and abdominal tumors. The state of the art and perspectives are shown by an insight into the current field of research. The book is addressed to biomedical engineers, medical physicists, researchers and physicians working in the fields of medical image analysis, radiology and radiation therapy.