bluuebunny/crossref_metadata_2025_split huggingface dataset:
Protein Structure Design and Engineering
Authors: Berry Steven M , Lu Yi 
Published: February 2011

AbstractProtein structure design and engineering is a research endeavour in which proteins with predicted structure and function are constructed in the laboratory through rational design, combinatorial selection or combination of both approaches. It is built upon our knowledge about the structure and function of proteins and can be accomplished either from scratch (de novodesign) or based on native scaffolds (redesign). The area of protein design is an exciting and rapidly growing field, advancing from the design of simple protein structures, to those that are more complicated and recently to the designs of functional proteins. Design of artificial proteins containing unnatural amino acids, backbone linkages or cofactors have also been reported, making it possible to prepare proteins with structural and functional properties beyond those of native proteins. These advances bring us closer to realising the dream of tailor‐made artificial enzymes with high catalytic efficiency and selectivity for biotechnological and pharmaceutical applications.Key Concepts:Protein structure design and engineering is a research endeavour in which proteins with predicted structure and function are constructed in the laboratory.The protein design field can be organised into two complimentary approaches: rational design and combinatorial selection of the desired protein.Rational protein design strategies can involve designing a protein from scratch (de novodesign) or redesigning a protein with native scaffolds to achieve new structures and functions.Combinatorial approaches can involve sampling a large population of proteins to select the desired one or it can involve numerous rounds of randomised mutation followed by selection to fine‐tune properties being selected for.Techniques are available that allow the incorporation of unnatural moieties, such as unnatural amino acids, backbone linkages or cofactors, into proteins.The field has made a big stride recently in designing functional proteins.
Mitochondrial Genome
Authors: Saccone Cecilia 
Published: February 2011

Abstract

Mitochondria are cytoplasmic organelles present in all eukaryotic cells that are able to respire. They possess their own genetic system whose size and organisation are quite variable in plants, protists and fungi. A certain degree of structural variability is observed also in Metazoa, whereas mitochondrial
deoxyribonucleic acid
(mtDNA) is rather small but constant in vertebrate cells (about 17

kb). The genetic content and organisation of human mtDNA is very similar in all vertebrates. It consists of two ribosomal
ribonucleic acid
(RNA) genes, 13 protein‐coding genes and 22 transfer RNAs. All other mitochondrial products are coded by nuclear DNA and transported into the mitochondrion. Mitochondrial genetic system has a uni‐directional way of inheritance, and it is present in cell in a multicopy state. mtDNA replication, transcription and translation are also very peculiar. Evolutionary origin of mitochondria has been described through the endosymbiotic theory.

Key Concepts:

Mitochondria are eukaryotic cell organelles with their own genome.

Mitochondrial genome shows great structural variability in most eukaryotic taxa but are quite stable for content and organisation in Metazoans.

mtDNA has a poor gene content; mitochondrial protein genes encode for respiratory chain subunits, fundamental for cell energy production.

The mitochondrial genome has bacterial origin, with several peculiar features about its replication and expression.

mtDNA is a multicopy genome that is uni‐parentally transmitted and does not undergo recombination.

Evolution of mtDNA (within Metazoa) is lineage‐specific.
Primate Evolution: Gene Loss and Inactivation
Authors: Satta Yoko 
Published: February 2011

Abstract

Pseudogenes are defined as genes that have lost function. However, despite the loss of function, pseudogenes can play an important role in evolution, especially when the environment changes. Owing to some environmental changes in diet or habitat, for example, pseudogenisation may be beneficial rather than deleterious for individual fitness. The human‐specific pseudogenes are of particular interest, and more than 100 have been identified. These include olfactory receptors, immune function‐related genes and metabolic pathway genes, which seem to be sensitive to environmental conditions. In this article, examples of environment‐driven pseudogenisation are presented and the role or significance of the loss‐of‐function genes in the evolution of primates and humans is discussed.

Key Concepts:

Functional constraints maintain function of a protein and therefore maintain the conserved way of molecular evolution.

Relaxation of constraints can be achieved in two ways: gene duplication and environmental changes.

Gene duplication provides an extra copy of a functional gene, resulting in the relaxation of a functional constraint to on the gene.

The ‘less‐is‐more hypothesis’ proposed by Maynard Olson in 1999 suggests loss of function could be an advantage to organisms and also affect phenotypic or physiological changes in evolution.
Gonadotropin Hormones: Disorders
Authors: Martinez‐Aguayo Alejandro , Dattani Mehul T , Achermann John C 
Published: February 2011

Abstract

Abnormalities in gonadotropin hormone release and function can arise from changes in a variety of genes expressed throughout the hypothalamic–pituitary (gonadotrope)–gonadal (HPG) axis. Alterations in these genes can disrupt proteins that are involved in many different biological processes such as neuronal migration, gonadotropin‐releasing hormone (GnRH) release and action, and pituitary development, as well as affecting synthesis and function of the gonadotropin hormones (follicle‐stimulating hormone and luteinising hormone) themselves. The identification and characterisation of these factors is providing important insight into the reproductive axis in humans and may result in improved treatment and counselling for patients with infertility problems. However, the underlying cause of GnRH deficiency or altered gonadotropin action is not currently known in most cases, and it is emerging that digenic or oligogenic inheritance patterns are important in some situations. Therefore, the molecular basis of disorders of gonadotropin hormones may be more complex than originally thought.

Key Concepts:

The gonadotropins, FSH and LH, are heterodimeric glycoprotein hormones released in pulses from the pituitary gonadotrope cells.

FSH and LH secretion is regulated by hypothalamic GnRH, which itself is pulsatile.

GnRH‐secreting neurons originate in the olfactory placode and migrate to the hypothalamus during development; defects affecting neuronal migration can be associated with a lack of smell (anosmia).

Gonadotropes develop within the anterior pituitary gland, which originates from the oral ectoderm; defects affecting gonadotrope function can be associated with other pituitary hormone deficiencies.

FSH and LH act through peripheral G protein‐coupled receptors to stimulate gonad development and function.

Some factors can influence the hypothalamic–pituitary–gonadal axis at multiple levels.

More than 30 single gene disorders affecting gonadotropin function have now been described, often affecting receptor‐ligand pairs.

Phenotypic penetrance can be variable, even within families and may reflect the influence of modifier genes, epigenetic factors or environmental stimuli.

In some situations, changes in two genes can result in a clinical phenotype (digenic inheritance).

In many cases the underlying molecular basis of hypogonadism is still unknown.
Protein Interaction and Genetic Disease
Authors: Gregersen Niels , Bross Peter , Olsen Rikke KJ , Palmfeldt Johan , Corydon Thomas J 
Published: February 2011

Abstract

The function of proteins is dependent on other proteins. Proteins function as oligomers, complexes, super‐complexes or higher order networks, in which they interact with each other, either temporarily when they exert their function or ‘permanently’ in functional units. Genetic defects in single proteins may therefore, in addition to disturbing the specific function of the defective protein, disturb other functions that are dependent on it. In this review we will discuss how the two main types of defects in genetic disease, truncating variations (stop‐codon introductions and small out‐of‐frame deletions/insertions) and in‐frame variations (missense variations and small in‐frame deletions/insertions), may disturb normal interactions. Depending on the importance (location) of the missing or aberrant protein, the effect on the cellular pathway or interacting network may be severe or mild. Protein interactions and disturbances therein may be determined by protein mass spectrometry after immuno‐precipitation or other fractionation and separation methods.

Key Concepts:

Cellular proteins interact in oligomer and complex structures as well as in higher order networks.

Protein interactions may be permanent in the lifetime of proteins or temporary during their function.

Genetic defects may disturb interactions between proteins depending on the nature of the defect and type of interaction.

Missing proteins due to truncation, comprising big deletions, small out‐of‐frame deletions/insertions and severe splice alteration, may abolish the function of oligomers, complexes, pathways and networks.

Aberrant proteins due to missense variations and in‐frame deletions/insertions may disturb interactions with protein partners in oligomers, complexes and networks.

Missing and aberrant proteins in branch‐points (nodes) have as a role severe consequences, resulting in genotype–phenotype association.

Missing and aberrant proteins in redundant pathways have variable consequences, resulting in poor genotype–phenotype association.

Missing and aberrant proteins in molecular machines, such as the chaperonin Hsp60, may elicit pleotropic effects due to defective processing of client proteins.

Protein interactions and disturbances therein may be determined experimentally by protein mass spectrometry (MS), preceded by immunoprecipitation of target proteins, either directly or after cross‐linking, or indicated after mild solubilisation followed by extensive separating procedures and MS.

The challenge is to design experiments that can determine qualitative and quantitative disturbances of proteins interactions in cells and tissue from patients, model animals and model cells compared to interactions in normal cells and tissue.
Gain‐ and Loss‐of‐function Mutations in Trypsinogen
Authors: Chen Jian‐Min , Férec Claude 
Published: February 2011

Abstract

Historically, trypsinogen has been among the most extensively studied models of protein structure and function. It has received renewed attention since the identification of a gain‐of‐function missense mutation in the cationic trypsinogen gene (

PRSS1

) as a cause of hereditary pancreatitis in 1996; a finding gave strong support to the then century‐old hypothesis that pancreatitis is an autodigestive disease in which prematurely activated trypsin within the pancreas was thought to play a pivotal role. Whereas gain‐of‐function

PRSS1

missense mutations cause chronic pancreatitis through a negative effect on trypsin lysis and a positive effect on trypsinogen autoactivation, duplication and triplication copy number mutations of the trypsinogen locus cause the disease through a gene‐dosage effect. By contrast, loss‐of‐function variations in the

PRSS1

and

PRSS2

(encoding anionic trypsinogen) genes protect against chronic pancreatitis. The study of pathogenic

PRSS1

mutations also shed lights on the evolution of trypsinogen genes.

Key Concepts:

More than 100 years ago, pancreatitis was hypothesised to be an autodigestive disease in which prematurely activated trypsin within the pancreas was thought to play a pivotal role.

The century‐old hypothesis was given strong support when a gain‐of‐function missense mutation (R122H) in the cationic trypsinogen gene (
PRSS1
) was identified as a cause of hereditary pancreatitis in 1996.

Gain‐of‐function
PRSS1
missense mutations are characterised by a negative effect on trypsin lysis and a positive effect on trypsinogen autoactivation.

Duplication and triplication copy number mutations of the trypsinogen locus cause a gain of trypsin through a gene‐dosage effect.

Gain‐of‐function
PRSS1
mutations can be generated from diverse mutational mechanisms such as CpG substitution, gene conversion, nonallelic homologous recombination (NAHR) and microhomology‐mediated replication‐dependent recombination (MMRDR).

Whereas gain‐of‐function
PRSS1
mutations predispose to chronic pancreatitis, loss‐of‐function variations in the
PRSS1
and
PRSS2
(encoding anionic trypsinogen) genes protect against chronic pancreatitis.

The study of pathogenic
PRSS1
mutations also shed lights on the evolution of trypsinogen genes.
Origin of the Tuscans
Authors: Barbujani Guido , Ghirotto Silvia 
Published: February 2011

Abstract

Tuscany was home to the Etruscan culture, in the first millennium
ad
and is one of the few areas of Europe for which the genealogical relationships between past and present inhabitants have been thoroughly investigated at the genetic level. Analyses of deoxyribonucleic acid show that the contemporary Tuscans have affinities with people from the Near East but share very few mitochondrial haplotypes with the local Iron‐age population, that is, the Etruscans. These results do not imply that the Etruscans got extinct, but certainly that they cannot be regarded as ancestral to most modern people of Tuscany. By contrast, there is evidence of genealogical continuity between Medieval and current times, which suggests that the main demographic change(s) occurred in previous centuries.

Key Concepts:

Comparison of DNAs in ancient and modern individuals provides evidence about their genealogical relationships.

Modern methods to infer genealogical relationships from DNA data are based on extensive computer simulations.

The available evidence demonstrates that current populations are not necessarily descended from pre‐historic ancestors who dwelt in the same places, although there are a few cases of genealogical continuity across the past 2500 years.

Genetic variation in modern and ancient Tuscany shows that the Etruscans cannot be regarded as the direct ancestors of the bulk of the contemporary population. It also suggests that one or more immigration processes, probably occurring in the first millennium
ad
, have reshaped the population structure.
Block Replacement Policies
Authors: Belzunce Félix , Shaked Moshe 
Published: February 2011

AbstractIn this article, we describe block replacement policies and we list several results that compare block replacement policies with some other policies. Mainly, we present results that compare the number of unplanned failures in a block replacement policy in the sense of several stochastic orders, with the number of unplanned failures under the usual replacement policy and under an age replacement policy. Some other results for the increasing convex order, Laplace order, and comparisons of expectations for the arrival times are considered. We first list univariate results, and later we list some extensions of the block replacement policy and comparisons of entire processes. We conclude with a basic description of the cost analysis that is associated with block replacement policies.
Calculating Real Option Values
Authors: Dias Marco Antonio Guimarães 
Published: February 2011

AbstractThis article presents an overview of real option methodologies for evaluation and decision on capital investments under uncertainty. These methodologies are based on one of the two fundamental ideas: the construction of a risk‐free portfolio and the risk‐neutral probability measure, both to calculate the present value of options using risk‐free discount rate. The article discusses these ideas in discrete‐ and continuous‐time approaches, with methods like binomial lattices, stochastic differential equations, integral method, and Monte Carlo simulation. The strengths and weakness of these methods are discussed in light of applications. The article discusses different types of real options as well as the combination of real options with other disciplines like game theory, decision analysis, and genetic algorithms.
Combinatorial Traveling Salesman Problem Algorithms
Authors: D'Ambrosio Claudia , Lodi Andrea , Martello Silvano 
Published: February 2011

Abstract

The
traveling salesman problem
(TSP) is a fundamental and well‐known problem in combinatorial optimization. We start by reviewing some of its ancestors, including the famous Hamiltonian cycle problem of which the TSP is the weighted version. We then introduce the most famous formulations of both the symmetric and the asymmetric TSP, and describe combinatorial approaches for both versions of the problem. We conclude with a brief discussion on the available TSP software.
Combining Exact Methods and Heuristics
Authors: Boschetti Marco A. , Maniezzo Vittorio 
Published: February 2011

AbstractThe combination of exact and heuristic methods is as old as mathematical programming (MP) itself, because usually exact methods cannot work properly without a good bound on the optimal cost, which helps pruning the search space. However, it is only in very recent years that the combination has also gone the other way round, permitting the use of methods originally conceived for exact problem solution also in heuristic frameworks. In this article, we revise the newly identified field of metaheuristics, which refers in particular to algorithms combining metaheuristics with MP techniques and show how current research aims at exploiting MP and metaheuristics synergies.
Dynamic Programming, Control, and Computation
Authors: Hanson Floyd B. 
Published: February 2011

Abstract
The presentation in this article is in the formal manner of classical applied mathematics and probability in order to focus on the methods and their implementation. In the section titled “Dynamic Programming in Continuous‐Time,” a fairly general model of stochastic dynamic programming in continuous‐time is outlined. In the section titled “Computational Reducing Canonical Forms,” canonical forms, such as a linear dynamics and quadratic cost model in control, that lead to a large reduction in computational effort are given or discussed. In the section titled “Finite Difference PDE Computational Methods,” finite difference partial differential equation (PDE) methods are given that are suitable for solving nonlinear Bellman dynamic programming equations, approximately. Alternatively, Markov chain approximation probabilistic methods which systematically justify the stability and weak convergence of the approximating Markov chain are summarized in the section titled “Probabilistic Markov Chain Approximation.” In the last section, there is a brief summary and directions to some other approaches.
Computational Methods for
DTMCs
Authors: Dayar Tuǧrul , Stewart William J. 
Published: February 2011

Abstract
We consider methods for computing transient and stationary distributions of ergodic discrete‐time Markov chains. We also briefly consider the computation of moments of first passage times. The computation of transient distributions seldom presents difficulties and hence the major part of this article is devoted to the computation of stationary distributions. We discuss the theory, application, and suitability of direct methods based on Gaussian elimination and two of its variants, LU factorization and the GTH algorithm. Iterative methods are the most commonly used methods for computing stationary distributions, and in this context we examine three different approaches: (i) Methods based on splittings, including the power method, (block) Jacobi, and (block) successive overrelaxation. (ii) Decompositional methods, primarily the iterative aggregation‐disaggregation method. (iii) Krylov subspace projection methods. In this context, we discuss both popular methods and the concept of preconditioning which is vital to the success of such methods.
Nonlinear Conjugate Gradient Methods
Authors: Dai Yu‐Hong 
Published: February 2011

AbstractConjugate gradient methods are a class of important methods for solving linear equations and for solving nonlinear optimization. In this article, a review on conjugate gradient methods for unconstrained optimization is given. They are divided into early conjugate gradient methods, descent conjugate gradient methods, and sufficient descent conjugate gradient methods. Two general convergence theorems are provided for the conjugate gradient method assuming the descent property of each search direction. Some research issues on conjugate gradient methods are mentioned.
Category and Inventory Management
Authors: Alan Yasin , Gaur Vishal 
Published: February 2011

Abstract
Category and inventory management in retailing involves many novel and challenging aspects, including shelf space allocation, effect of inventory on stimulating demand, retail competition, substitution, and transshipment. Suitably managing these aspects has required new modeling advancements in the operations research and management science literature with the potential for a large improvement in the profitability of retailers. In this introductory article, we describe the main characteristics of these models, focusing on the principles involved, the insights from optimization, and techniques or heuristics used in practice.
Cooperative Game Theory with Nontransferable Utility
Authors: Oh Jungsuk 
Published: February 2011

AbstractThe central assumption of the cooperative game theory, that agents can impose binding agreements among themselves, is a huge step from the settings of the noncooperative game for acknowledging cooperative nature and capacity of economic agents. Yet, it is also a source of criticism for being “naive” to underestimate the complex dynamics among agents. When the cooperative game theory is utilized on top of the convenience of the transferable utility (TU) assumption, such criticism for being oversimplified and unrealistic is amplified. On the other hand, when detailed information on individual payoff scheme for various coalition structures is available, nontransferable utility (NTU) assumption can be accommodated.This is an introductory article that surveys a few central advances in game theory on NTU cooperative game situation. First, common criticisms of TU assumptions are presented, providing a motivation for the development of the NTU theory. Next, the role of the Nash bargaining solution in the development of the NTU theory is presented. Then, a few attempts to provide effectivity criteria for NTU settings in specifying the coalitional function is presented, which allows one to specify a coalitional‐form NTU game. Subsequently, the definition of the NTU coalitional form game is presented. Then, the NTU version of the definition and the theory of the core are described. Next, a few value concepts of NTU games, primarily the Shapley NTU value, are described, utilizing a few numerical examples. Finally, the axiomatization approach that has been fruitful in characterizing and comparing various NTU solution concepts is briefly surveyed.
Cross‐Entropy Method
Authors: Kroese Dirk P. 
Published: February 2011

AbstractThe cross‐entropy method is a recent versatile Monte Carlo technique. This article provides a brief introduction to the cross‐entropy method and discusses how it can be used for rare‐event probability estimation and for solving combinatorial, continuous, constrained, and noisy optimization problems. A comprehensive list of references on cross‐entropy methods and applications is included.