bluuebunny/crossref_metadata_2025_split huggingface dataset:
Memory
Authors: Dudai Yadin 
Published: November 2023

Abstract
The view taken here is that all memories, regardless of the species and the task, are biological *internal representations. Similarly to ‘learning’, the difference among types of memory is in the ecological function, computational theory, *algorithms, and neural implementation of the internal representation. The pursuit of internal representations and their modification by experience is thus tagged as the gist of memory research. Internal representations are assumed to be encoded in the spatiotemporal states of neural circuits (*cell assembly, *homunculus). In contemporary neurobiology, quite a few research programmes that claim to target memory concentrate solely on the molecular and the isolated single-cell level.
Model
Authors: Dudai Yadin 
Published: November 2023

Abstract
All models (modus, Latin for ‘a measure’, ‘standard of measurement’) are analogies (things similar but not identical, Greek for ‘proportionate’). Some are only *metaphors, others sets of quantitative relations among components of the modelled system, and yet others somewhere in between. In the context of the present discussion, it is useful to distinguish among (a) mathematical models, (b) diagrammatic models, and (c) the use of the term ‘model’ to describe a *simple system that could potentially illuminate phenomena of interest in what is considered a more complex system. Although different in type and complexity, all models share some elementary methodological aims, pros and cons.
Nutrients
Authors: Dudai Yadin 
Published: November 2023

Abstract
NutrientsThe possibility could be raised that the activity of the noradrenergic system, similarly to that of the cholinergic and the dopaminergic systems, is not essential for the operation of the ‘core’ molecular machinery that embodies lasting *synaptic changes in some systems of the mammalian brain. The distinction between a ‘core’ and an ‘accessory’ synaptic storage system is not self-evident. Generally speaking, it implies that there are molecular cascades that are essential for synaptic storage and there are others that are indispensable for encoding distinct cellular and circuit states but dispensable for the storage process per se.
Ligand binding to PPlases
Authors: Galat Andrzej , Riviere Sylvie 
Published: November 2023

Abstract
At least four distinct families of proteins have PPIase activity, namely cyclophilins, FKBPs, parvulins and the trigger factor. Although the sequences of the PPIase domains of these four families do not have any significant degree of similarity with each other, their binding clefts maintain at least one common enzymatic function. The four families of PPIases display moderate specificity to peptide substrates [632, 661], differ in their potential to accelerate in vitro protein refolding [439] and show unique spatial and temporal expression patterns in cells.
The biology of PPlases
Authors: Galat Andrzej , Riviere Sylvie 
Published: November 2023

Abstract
The wide phylogenetic distribution, ubiquity and diversity of the activities and structures of PPIases (immunophilins) has led to many roles being suggested for this group of proteins (see Table 1). The involvement of PPIases in the folding of proteins or co-regulation of molecular channels has already been discussed. Other phenomena summarized in Table 1 await confirmation by other laboratories. One possible problem that can be seen from Table 1 is that most of the tentative cellular or biological functions have been attributed to the two archetypal immunophilins, namely FKBP-12 and CyP-18. Could CyP-18 have nuclease [326, 869], putative protease [781] and PPIases activities? Do those active sites overlap or are they in different parts of the molecule? Or do these activities belong not to the PPIases but to other components contaminating the preparations tested? Perhaps more judicious verification of some of these observations is required.
Methods
Authors: Galat Andrzej , Riviere Sylvie 
Published: November 2023

Abstract
Several basic methodologies that have been applied in research on PPIases are summarized in Table 20. The majority of abundant small mammalian cydophilins and FKBPs arc moderately basic proteins which bind to cation exchangers. Proteins entrapped on chromatographic media are resolved either on chromatofocusing gels or by preparative IEF. Final purification can be accomplished using cation exchangers [129, 130, 136 138, 194, 199, 236, 237, 256, 259].
Change, Choice and Inference
Authors: Rott Hans 
Published: November 2023

Abstract
Change, Choice and Inference develops logical theories that are necessary both for the understanding of adaptable human reasoning and for the design of intelligent systems. The book shows that reasoning processes - the drawing on inferences and changing one's beliefs - can be viewed as belonging to the realm of practical reason by embedding logical theories into the broader context of the theory of rational choice. The book unifies lively and significant strands of research in logic, philosophy, economics and artificial intelligence. It elaborates on the relevant theories and provides a mathematically precise foundation for the thesis that large parts of theoretical reason can be subsumed under practical reason.
Post-Genome Informatics
Authors: Kanehisa Minoru 
Published: November 2023

Abstract
The genome projects have now entered a rapid production phase with complete genome sequences and complete gene catalogues already available for a number of organisms and an increasing number expected shortly. In addition the new DNA and protein chip technologies can produce functional data about genes such as gene expression profiles at a rapid rate. There is therefore a large and ever increasing amount of data about genes and molecules. However there is still a huge gap between information at the molecular level and information at the level of integrated biological systems. It is this gap that is addressed in Post-genome Informatics. Post-genome informatics is the analysis of biological functions in terms of the network of interacting molecules and genes with the aim of understanding how a biological system is organized from its individual building blocks. As well as containing a comprehensive survey of the database and computational technologies relevant to molecular sequence analysis, Post-genome Informatics will provide the reader with a conceptual framework and practical methods for the representation and computation of molecular networks.
Prologue
Authors: Chandrasekhar S 
Published: November 2023

Abstract
The black holes of nature are the most perfect macroscopic objects there are in the universe: the only elements in their construction are our concepts of space and time. And since the general theory of relativity provides only a single unique family of solutions for their descriptions, they are the simplest objects as well. The unique two-parameter family of solutions which describes the space time around black holes is the Kerr family discovered by Roy Patrick Kerr in July, 1963. The two parameters are the mass of the black hole and the angular momentum of the black hole. The static solution, with zero angular momentum, was discovered by Karl Schwarzschild in December, 1915. A study of the black holes of nature is then a study of these solutions. It is to this study that this book is devoted.
Electromagnetic Waves in Kerr Geometry
Authors: Chandrasekhar S 
Published: November 2023

Abstract
As our studies in the contexts of the Schwarzschild and the Reissner-Nordstrom black-holes have shown, our understanding of the space times is deepened and enriched by analyzing the manner of their reaction to external perturbations. Since external perturbations can, in general, be represented by incident waves of different sorts, their study, in essence, reduces to one of the propagation of waves of the different sorts in the space-times of the black holes and of how, in particular, they are scattered and absorbed. While the problem, as thus formulated, has a physical base, its solution reveals an analytic richness of the space-times themselves for which one is unprepared. In the case of the Kerr space-time, this richness becomes manifest almost at the outset.
Dimensions of electric units.
Authors: Maxwell James Clerk 
Published: November 2023

Abstract
Every electromagnetic quantity may be defined with reference to the fundamental units of length, mass, and time. If we begin with the definition of the unit of electricity, as given in art. 65, we may obtain definitions of the units of every other electromagnetic quantity, in virtue of the equations into which they enter along with quantities of electricity. The system of units thus obtained is called the electrostatic system.
Discussion and comparison with the literature
Authors: Gabbay Dov M 
Published: November 2023

Abstract
The previous chapters presented a general methodology for combining logics. There was an emphasis on semantical methods, especially for the cases of intuitionistic and modal logics. To see where our work stands, we must compare it with existing published works in the area. Combined systems of modal and intuitionistic logics have been around for a long time, and the landscape is a jungle. As shown by the examples below, authors simply defined what they wanted for good or bad reasons and proceeded to investigate the systems they presented. There was no clear methodology, and no coordinate system of milestones for comparison. We aim in this chapter to show how the existing literature fits into our methodology. We proceed by summarising some typical existing papers and discussing their systems from our point of view.
How to make your logic fuzzy
Authors: Gabbay Dov M 
Published: November 2023

Abstract
The aim of this chapter is to provide a methodology for turning a known crisp (i.e. {0, 1} valued) logic into a fuzzy system. We require of the methodology that it be meaningful in general terms, using processes which are independent of the notion of fuzziness, and that it yield a considerable number of known fuzzy systems.
Kinetic Theory
Authors: Succi Sauro 
Published: November 2023

Abstract
Kinetic theory is the branch of statistical physics dealing with the dynamics of non-equilibrium processes and their relaxation to thermodynamic equilibrium. The Boltzmann equation, established by Ludwig Boltzmann in 1872, is its cornerstone [1]. Originally developed in the framework of dilute gas systems, the Boltzmann equation has spread its wings across many areas of modern statistical physics, including electron transport in semiconductors, neutron transport, quantum liquids, to cite but a few [2, 3]. The aim of this chapter is to provide a minimal, and yet hopefully self-contained introduction to the Boltzmann equation in the context of classical statistical mechanics.
Microsatellites in conservation genetics
Authors: Beaumont Mark A , Bruford Michael W 
Published: November 2023

Abstract
In this chapter we will examine the role that microsatellite markers have played, and could potentially fulfil in the future, in the emerging field of conservation genetics. We first briefly discuss the applications, advantages, and disadvantages that microsatellite analysis brings to conservation genetics. We then highlight some current applications through the use of case study examples, ranging from the detection of inter-specific hybridization, through inferring population history, to distinguishing among demographic factors affecting present day allele frequencies, and include studies of inbreeding and reproductive success. In the final section we concentrate on the actual and potential additional information that microsatellites can give when compared with other quasi-neutral genetic markers, and explore the possible theoretical advantages and limitations that microsatellites have in conservation studies.
Microsatellite analysis of human tumours
Authors: Shibata Darryl 
Published: November 2023

Abstract
Human tumours arise after the accumulation of a number of somatic mutations. Many aspects of tumour evolution or progression are unknown, since serial observations are rare. Since tumours consist of populations of tumour cells, phylogenetic approaches may help to unravel their evolution. The recent discovery of human tumours with high frequencies of mutations at microsatellite loci (up to 0.01 per division), due to losses in DNA mismatch repair, provide practical opportunities to follow tumour evolution, since detectable frameshifts accumulate after relatively few divisions. In addition, the availability of cell lines and transgenic mice lacking DNA mismatch repair provide practical experimental models of microsatellite mutational processes.
Integrable Systems
Authors: Hitchin N J , Segal G B , Ward R S 
Published: November 2023

Abstract
This textbook is designed to give graduate students an understanding of integrable systems via the study of Riemann surfaces, loop groups, and twistors. The book has its origins in a series of lecture courses given by the authors, all of whom are internationally known mathematicians and renowned expositors. It is written in an accessible and informal style, and fills a gap in the existing literature. The introduction by Nigel Hitchin addresses the meaning of integrability: how do we recognize an integrable system? His own contribution then develops connections with algebraic geometry, and includes an introduction to Riemann surfaces, sheaves, and line bundles. Graeme Segal takes the Kortewegde Vries and nonlinear Schrödinger equations as central examples, and explores the mathematical structures underlying the inverse scattering transform. He explains the roles of loop groups, the Grassmannian, and algebraic curves. In the final part of the book, Richard Ward explores the connection between integrability and the self-dual Yang-Mills equations, and describes the correspondence between solutions to integrable equations and holomorphic vector bundles over twistor space.
On the Dangers of Modelling Through Continuous Distributions: A Bayesian Perspective
Authors: Fernández Carmen , Steel Mark F J 
Published: November 2023

Abstract
We point out that Bayesian inference on the basis of a given sample is not always possible with continuous sampling models, even under a proper prior. The reason for this paradoxical situation is explained, and linked to the fact that any dataset consisting of point observations has zero probability under a continuous sampling distribution. A number of examples, both with proper and improper priors, highlight the issues involved. A solution is proposed through the use of set observations, which take into account the precision with which the data were recorded. Use of a Gibbs sampler makes the solution practically feasible. The case of independent sampling from (possibly skewed) scale mixtures of Normals is analysed in detail for a location-scale model with a commonly used noninformative prior. For Student-^ sampling with unrestricted degrees of freedom the usual inference, based on point observations, is shown to be precluded whenever the sample contains repeated observations. We show that Bayesian inference based on set observations, however, is possible and illustrate this by an application to a skewed dataset of stock returns.
Bayesian Methods in Signal and Image Processing
Authors: Fitzgerald W J , Godsill S J , Kokaram A C , Stark J A 
Published: November 2023

Abstract
In this paper, an overview of Bayesian methods and models in signal and image processing is given. The first part of the paper reviews some traditional classes of model employed for signal processing time series analysis. Marginal inference based upon analytic integration of hyperparameters is described for these models and illustrations are given for the problem of estimating sinusoidal frequency components in white Gaussian noise and for the general changepoint problem applied to digital communications. In the second part of the paper, state of the art applications are described which employ MCMC methods for the enhancement of noise-degraded audio signals, non-linear system identification and image sequence restoration. The complex modelling requirements and large datasets involved in these problems require sophisticated MCMC schemes employing efficient blocking schemes, model uncertainty strategies (both reversible jump and Gibbs variable selection) and non-linear/non-Gaussian models.
Spatial Regression for Marked Point Processes
Authors: Ickstadt Katja , Wolpert Robert L 
Published: November 2023

Abstract
In a wide range of applications, dependence on smoothly-varying covariates leads spatial point count intensities to feature positive correlation for nearby locations. In applications where the points are “marked” with individual attributes, the distributions for points with varying attributes may also differ. We introduce a class of hierarchical spatial regression models for relating marked point process intensities to location-specific covariates and to individual-specific attributes, and for modeling the remaining intensity variation that arises from dependence on unobserved or unreported covariates. The magnitude of residual intensity variation is a measure of how completely the covariates explain the observed variations in point intensities. The models, extending those recently introduced by Wolpert and Ickstadt, treat the point patterns as doubly-stochastic Poisson random fields with a random inhomogeneous Poisson intensity given by a spatial mixture of gamma or other infinitely-divisible independent-increment random fields. Bayesian prior distributions for a third level of hierarchy are elicited to reflect beliefs about the homogeneity, continuity, and similar features of the intensity. Inference is based on posterior and predictive distributions computed using Markov chain Monte Carlo methods featuring data augmentation and an efficient method for sampling from independent-increment random fields. The models are illustrated in an application to a four-dimensional spatial regression analysis of origin/destination trip data from the 1994/95 Metro survey of Portland, Oregon.
MCMC Convergence Diagnostics: A Reviewww
Authors: Mengersen Kerrie L , Robert Christian P , Guihenneuc-Jouyaux Chantal 
Published: November 2023

Abstract
This review paper aims at presenting the various convergence diagnostics available in the literature in a cohesive and conceptually consecutive manner. To this effect, we attempt classification according to several filters, as well as a description of a Web site, which aims at keeping track of the on-going research on the topic.
Simulation-Based Optimal Design
Authors: Moller Peter 
Published: November 2023

Abstract
We review simulation based methods in optimal design. Expected utility maximization, i.e., optimal design, is concerned with maximizing an integral expression representing expected utility with respect to some design parameter. Except in special cases neither the maximization nor the integration can be solved analytically and approximations and/or simulation based methods are needed. On one hand the integration problem is easier to solve than the integration appearing in posterior inference problems. This is because the expectation is with respect to the joint distribution of parameters and data, which typically allows efficient random variate generation. On the other hand, the problem is difficult because the integration is embedded in the maximization and has to possibly be evaluated many times for different design parameters.
Uncertainty Analysis and other Inference Tools for Complex Computer Codes
Authors: O’hagan Anthony , Kennedy Marc C , Oakley Jeremy E 
Published: November 2023

Abstract
This paper builds on work by Haylock and O’Hagan which developed a Bayesian approach to uncertainty analysis. The generic problem is to make posterior inference about the output of a complex computer code, and the specific problem of uncertainty analysis is to make inference when the “true” values of the input parameters are unknown. Given the distribution of the input parameters (which is often a subjective distribution derived from expert opinion), we wish to make inference about the implied distribution of the output. The computer code is sufficiently complex that the time to compute the output for any input configuration is substantial. The Bayesian approach was shown to improve dramatically on the classical approach, which is based on drawing a sample of values of the input parameters and thereby obtaining a sample from the output distribution. We review the basic Bayesian approach to the generic problem of inference for complex computer codes, and present some recent advances—inference about the distribution of quantile functions of the uncertainty distribution, calibration of models, and the use of runs of the computer code at different levels of complexity to make efficient use of the quicker, cruder, versions of the code. The emphasis is on practical applications.
Spatial Dependence and Errors-in-Variables in Environmental Epidemiology
Authors: Wakefield Jon , Morris Sara 
Published: November 2023

Abstract
Ecological correlation studies, in which grouped data are used to investigate the relationship between outcome and explanatory variables, are widely used in epidemiology. We consider a spatial context in which the groups represent areal units. In particular we analyse data from an on-going study investigating the relationship between myocardial infarction and the water constituents magnesium, calcium and fluoride. Information on these constituents is available through repeated measurements over time within water-company defined ‘water zones’. The analysis is challenging due to the over-dispersion and spatial dependence within the data; the errorS-in-variables nature of the exposure; the presence of potential confounders such as socio-economic status; and the different geographic scales at which the health, exposure and confounder variables are available. Our modelling strategy is to begin with a very simple model and to then increase the complexity as inadequacies are revealed by examination of diagnostics, both frequentist and Bayesian. Our emphasis is on utilising models that are necessarily complex and in addressing the sensitivity of inference to modelling assumptions.
Life and information
Authors: Maynard Smith John , SzathmáRy EöRs 
Published: November 2023

Abstract
Organisms are incredibly complex. The more we know about them— their biochemistry, their anatomy, their behaviour—the more astonishing are the detailed adaptations that we discover. How could all this complexity have arisen? We can all understand that, if a farmer breeds from the cows that give most milk, the milk yield of the cows will in time increase. This depends on the fact that offspring resemble their parents—that is, on heredity—but this does not at first sight present any great difficulty: we are familiar in our daily lives with the fact that children resemble their parents. Darwin’s theory of evolution by natural selection amounts to no more than the idea that, in nature, those individuals best able to survive and reproduce will transmit the characteristics that enabled them to do so to their children, leading to the evolution of traits beneficial to the organism itself, rather than to the breeder. It is, perhaps, the one profound idea in science that we can all readily understand.
The major transitions
Authors: Maynard Smith John , SzathmáRy EöRs 
Published: November 2023

Abstract
The theory of evolution by natural selection does not predict that organisms will get more complex. It predicts only that they will get better at surviving and reproducing in the current environment, or at least that they will not get worse. Empirically, many and perhaps most lineages change little for many millions of years. As D. M. S. Watson, who taught one of us zoology, once remarked, crocodiles have done damn all since the Cretaceous. Lampshells and horsetails have done damn all for longer than that.