04319nam 22006975 450 991084507740332120241120175023.03-031-47104-010.1007/978-3-031-47104-9(CKB)31252887400041(MiAaPQ)EBC31281836(Au-PeEL)EBL31281836(DE-He213)978-3-031-47104-9(OCoLC)1428780971(EXLCZ)993125288740004120240329d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierBayesian Filter Design for Computational Medicine A State-Space Estimation Framework /by Dilranjan S. Wickramasuriya, Rose T. Faghih1st ed. 2024.Cham :Springer International Publishing :Imprint: Springer,2024.1 online resource (235 pages)3-031-47103-2 Introduction -- Some Useful Statistical Results -- State-space Model with One Binary Observation -- State-space Model with One Binary and One Continuous Observation -- State-space Model with One Binary and Two Continuous Observations -- State-space Model with One Binary, Two Continuous and a Spiking-type Observation -- State-space Model with One Marked Point Process (MPP) Observation -- Additional Models and Derivations -- MATLAB Code Examples -- List of Supplementary MATLAB Functions.This book serves as a tutorial that explains how different state estimators (Bayesian filters) can be built when all or part of the observations are binary. The book begins by briefly motivating the need for point process state estimation followed by an introduction to the overall approach, as well as some basic background material in statistics that are necessary for the equation derivations that are utilized in subsequent chapters. The subsequent chapters focus on different state-space models and provides step-by-step explanations on how to build the corresponding Bayesian filters. Each of the main chapters that describes a single state-space model also describes the corresponding MATLAB code examples at the end. Descriptions are also provided regarding the code. The code contains both simulated and experimental data examples. All the experimental data examples are taken from real-world experiments. The experiments involve the recording of skin conductance, heart rate and blood cortisol data. A MATLAB toolbox of code examples that cover the different filters covered in the book is included in a companion webpage. The book is primarily intended for graduate students in either electrical engineering or biomedical engineering who will be beginning research in state estimation related to point process data or mixed data (i.e., point processes and other types of observations). The book can also be used by practicing researchers who measure skin conductance and heart rate or pulsatile hormones in their own work (e.g. in psychology). This is an open access book.Computational neuroscienceNeurotechnology (Bioengineering)Biomedical engineeringSignal processingBiophysicsCell interactionComputational NeuroscienceNeuroengineeringBiomedical Engineering and BioengineeringDigital and Analog Signal ProcessingMechanobiological Cell SignalingComputational neuroscience.Neurotechnology (Bioengineering)Biomedical engineering.Signal processing.Biophysics.Cell interaction.Computational Neuroscience.Neuroengineering.Biomedical Engineering and Bioengineering.Digital and Analog Signal Processing.Mechanobiological Cell Signaling.612.8570.285Wickramasuriya Dilranjan S1735694Faghih Rose T1735695MiAaPQMiAaPQMiAaPQBOOK9910845077403321Bayesian Filter Design for Computational Medicine4155010UNINA03754nam 2200985z- 450 991055738040332120220111(CKB)5400000000042095(oapen)https://directory.doabooks.org/handle/20.500.12854/76432(oapen)doab76432(EXLCZ)99540000000004209520202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierImmunohistochemical ExpressionBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (168 p.)3-0365-0400-1 3-0365-0401-X Immunohistochemistry (IHC) is an ancillary method, widely used in pathologists' practice, that allows identifying diagnostic and prognostic/predictive of therapeutic response protein markers on tissue samples by the use of specific monoclonal antibodies and chromogenic substances that guarantee the visualization of an antibody-antigene binding complex under a light microscope [1]. Coon et al., in 1941 [2], first introduced the use of fluorochrome-conjugated antibodies in clinical practice. Since then, IHC has gone from being a useful tool for identifying the differentiation line of otherwise undifferentiated cells to a technique capable of providing not only diagnostic but also prognostic and predictive indications of responses to specific therapeutic options [1,3]. The abovementioned peculiarities have made IHC one of the most used ancillary methods in the histopathological approach to human neoplastic and non-neoplastic diseases [3-5]. This Special Issue contains 11 accepted papers that provide readers with a comprehensive update on current and future applications of IHC in medical practice.MedicinebicsscABCB5c-kitcarcinogenesischaperonopathiescytokeratinDENdifferential diagnosisEGCGenteric nervous systemexcretion fraction of uric acidfollicular adenomafollicular carcinomaheartHsp27Hsp60Hsp70Hsp90human embryonal/fetal tissueshypouricemiaimmunohistochemistryinflammationinitial lymphaticskidneylivermacroH2Amatrix metalloproteinasesmegacystismetastasismolecular chaperonen/aneoplastic tissueNGALoutcome and prognosisoxidative stressPDGFRpericardiumprognosisprognostic factorprostate cancerSLC22A12temporomandibular jointtemporomandibular joint disorderthyroidtraining exerciseultra-structural changesURAT1uric acid transportersurinary tract malformationsuveal melanomaVDRVitamin DWT1MedicineCaltabiano Rosarioedt1314843Loreto CarlaedtCaltabiano RosarioothLoreto CarlaothBOOK9910557380403321Immunohistochemical Expression3032037UNINA