bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Study of the Effectiveness of Teachers Training in Sindh Province
Authors: Ali Rubina Usman , Afzal Asma , Fahd Madiha 
Published: May 2024

This research study take a look into a study of the effectiveness of teachers training program for private secondary schools in Sindh. Technology integration is far behind in Pakistani Schools as a consequence of ICT deficiency, absence of in-service teachers training. ICT has tremendous potential to prepare students for the workplaces globally and improve educational systems, if it’s integrated wisely into a curriculum. ICT Teacher Training is being sorted out at distinctive levels for the educators to outfit them with essential most recent information and abilities to make them great ICT educators by the Government of Pakistan and Education Department; in this study the relationships between in service ICT teachers training and changes in teaching behavior will be investigated. This research take a look into a study of the effectiveness of ICT teachers training program for private secondary schools in Sindh to promote Ubiquitous learning among students. The study's main goals were to investigate the impact of in-service teachers' ICT training on subject teachers' teaching methodology based on Ubiquitous Learning. Apricot Education System conducted this study in private schools in Karachi, and 150 teachers were chosen as a target sample using purposive random sampling following three months of teacher training. The investigation was descriptive in nature. A questionnaire with 60 items based on a 5 Likert scale was used to collect data using the survey method. The data was analyzed using the correlation, regression, and reliability tests. The results of the regression test revealed that teacher training had a substantial impact on subject teachers' teaching style based on Ubiquitous Learning. The t- value is 5.079.The data was analyzed using the correlation, regression, and reliability tests. The results of the regression test revealed that teacher training had a substantial impact on subject teachers' teaching style based on Ubiquitous Learning. The t-value is 5.079, and the p-value is.000 less than 0.05. That equals.000. It indicates that H1 is acceptable. Finding administrators should perform maximal ICT Teachers Training to improve teacher competencies, according to the recommendation.
Modeling human genetic history
Authors: Chikhi Lounès , Beaumont Mark A. 
Published: November 2009

AbstractRecent years have seen the development of new ways of handling genetic data and making inferences on specific types of demographic events such as population expansions, bottlenecks, or admixture events. Indeed, simulations have shown that ancient demographic events can leave specific signatures in genetic data extracted from present‐day populations. How specific these signatures are is the focus of ongoing research. In this article, we present some of the principles underlying demographic inference based on genetic data. We discuss some results from the coalescent theory, which has been the backbone of population genetic modeling for the last 15–20 years. We also describe some of the recent methodological developments including the introduction of Bayesian and the so‐called approximate Bayesian computational methods. Genetic data provide unique and very powerful means of inferring or understanding the patterns of sometimes very ancient demographic events. However, we also emphasize the huge uncertainty in our ability to reconstruct past events from genetic data in general, and from mtDNA or Y chromosome haplotypes in particular. We conclude by discussing a number of problems associated with the use of network‐based methods, and briefly highlight some promising avenues of research for the future.
Imprinting and epigenetic inheritance in human disease
Authors: Polychronakos Constantin 
Published: November 2009

Abstract
The silencing of either the paternal or the maternal copy of the few human genes that are subject to parental imprinting may contribute to genetic disease or modify its inheritance pattern. Disruption of the normal process of imprinting may result in double gene dosage if both copies are expressed or in a null phenotype if neither is. When normally imposed, imprinting can play a role in disease by leaving only one of the two copies to be inactivated by mutation. This introductory review explains the various mechanisms and gives examples of the best‐understood diseases where imprinting plays an important role.
Evolution of genomic imprinting in mammals
Authors: Spencer Hamish G. 
Published: November 2009

AbstractGenomic imprinting is an evolutionary paradox because it seems at first glance to be disadvantageous: the monoallelic expression it entails means that recessive deleterious mutations cannot be masked. Several hypotheses seek to resolve the paradox, and the strengths and weaknesses of the most plausible of these – ovarian time bomb, genomic conflict, differential selection on males and females – are discussed.
Bioinformatics and the identification of imprinted genes in mammals
Authors: Fazzari Melissa J. , Greally John M. 
Published: November 2009

AbstractWith the identification of DNA sequence features that are unusually distributed in regions undergoing genomic imprinting, the basis is created for the bioinformatic prediction of the remaining imprinted genes in mammalian genomes, believed to number several hundreds. It is technically challenging to prove that a gene is imprinted, so any technique that narrows down the candidates for analysis is of obvious value. Genome sequence annotations can be mined to create large datasets for analysis, introducing a number of statistical challenges. We discuss how these challenges can be addressed, allowing every gene in the genome to be assigned a relative likelihood of imprinting on the basis of their similarity to known imprinted genes in terms of their most discriminatory sequence characteristics.
Genetics of complex diseases: lessons from type 2 diabetes
Authors: Groop Leif , Almgren Peter 
Published: November 2009

Abstract
While the genetic causes of monogenic disorders have been successfully identified in the past, the success in dissecting the genetics of complex polygenic diseases has been limited. There are several reasons for these shortcomings. For many complex diseases, variants in several genes increase the susceptibility to the disease but the individual risk may be too low to be detected by current methods. In addition, the genetic variants almost always require environmental triggers, which need to be taken into account in the search for the underlying genetic causes. The rapid development of high‐throughput tools for genotyping and improved understanding of basic concepts of complex diseases may open up for some future success.
Hematopoietic stem cell gene therapy
Authors: Thrasher Adrian J. , Candotti Fabio 
Published: November 2009

AbstractHematopoiesis is sustained throughout fetal and adult life by hematopoietic stem cells (HSCs) that are defined by their self‐renewal capacity, pluripotentiality, and their ability to repopulate myeloablated recipients. HSCs have therefore become important targets for transplantation and somatic gene therapy. Models of HSC gene therapy in mice and large animals have reproducibly demonstrated that a significant proportion of cells participating in long‐term engraftment can be stably transduced by integrating vectors based on mammalian retroviruses. Several clinical studies have also shown that conventional gene transfer technology can produce major beneficial therapeutic effects in human patients.
Linkage disequilibrium and whole‐genome association studies
Authors: Novik Karen L. , Brooks‐Wilson Angela R. 
Published: November 2009

Abstract
Complex diseases affect a substantial proportion of the human population and are caused by multiple genetic and environmental effects. The association study is a means of identifying genetic variation that may be involved in complex disease etiology. The existence of linkage disequilibrium (nonrandom association of alleles) across the human genome can be used to reduce the number of variants needed to successfully correlate with phenotypic traits. We discuss the current status and problems inherent in performing whole genome association studies to analyze complex diseases.
Fingerprint mapping
Authors: Schein Jacqueline E. , Krzywinski Martin I. 
Published: November 2009

Abstract

Restriction fingerprint maps have played a significant role in genomics research over the past 35 years. Representing the size and order of restriction endonuclease fragments along the genome, these maps provided a physical framework on which to locate known features and functions in genomes of early scientific interest. Over the last 20 years, large‐insert, clone‐based fingerprint maps have been critical components in genome mapping and sequencing projects, including those for
Caenorhabditis elegans
,
Saccharomyces cervisiae
,
Mus musculus
, and
Homo sapiens
. In this review, we provide a summary of the evolution of fingerprint mapping, and an overview of the current methodologies used and technical considerations for the generation of ordered‐clone fingerprint maps.
Alternative splicing: conservation and function
Authors: Gelfand Mikhail S. , Kriventseva Evgenia V. 
Published: November 2009

AbstractA major role in the analysis of alternative splicing belongs to the large‐scale computational examination of available EST and genome data. At least half of human genes are alternatively spliced, and many of them have isoforms not conserved in mouse. Alternative splicing tends to shuffle protein domains and frequently affects signal peptides and functional sites within domains. Thus, alternative splicing is a major mechanism generating functional and evolutional diversity of proteins. On the other hand, unlike simple gene duplication, this mechanism allows for the production of absolutely identical, in some parts, proteins.
History of genetic mapping
Authors: Morton Newton E. 
Published: November 2009

AbstractUnlike physical maps, genetic maps are not directly derived from the DNA sequence. There are two types of genetic maps based on linkage and allelic association (linkage disequilibrium), respectively. Linkage maps are a century old and their use is stable, performing well in assigning a gene of unknown structure and function to a broad candidate region. Association maps were impossible until a trustworthy physical map became available and the block structure of linkage disequilibrium became known. Their use is in rapid evolution because (like functional tests) they are more efficient than linkage in identifying a causal locus within a candidate region. The questions raised by association mapping, although too new to be found in textbooks, are being pursued by many investigators.
Pharmacogenetics and the future of medicine
Authors: McCarthy Alun D. , Kennedy James L. , Middleton Lefkos T. 
Published: November 2009

Abstract
The efforts over the last 20 years to identify polymorphisms that influence human diseases are beginning to have a clear impact on medical care. The promise of the “new genetics” lies in its ability to provide information on an individual's susceptibility to disease, the exact nature of that disease, and the best therapeutic choice. During most of the history of pharmacogenetics (PGx – the use of genetic information to impact drug choice), it has been limited to simple phenotypes such as drug concentrations in plasma. Key developments in genetics technology have now made a wider range of efficacy and safety PGx studies possible, with a likely impact on a wide range of drug discovery and development activities. Existing PGx data show the ability of this approach to generate information relevant to dose selection, efficacy determination, and safety issues. This in turn will lead to significant impacts on both the design of clinical development programs and on the probability of success of those programs – the latter being a key issue for pharmaceutical companies today as well as for patients who will benefit from these novel medicines.
2‐DDifferenceGelElectrophoresis – an accurate quantitative method for protein analysis
Authors: Hawkins Edward , David Stephen O. 
Published: November 2009

Abstract2‐D Difference Gel Electrophoresis (DIGE) is a novel technique that enables multiple protein extracts to be labeled with different fluorescent dyes. The labeled samples are then separated on the same 2‐D gel. The dyes known as CyDyeTMDIGE Fluors are spectrally resolvable as well as size‐ and charge‐matched.The ability to add more than one sample to a 2‐D electrophoresis (2‐DE) gel enables the introduction of an internal standard that adds the quantitative advantages of a reference sample commonly seen in DNA microarray studies.Measurement of protein abundance as a “Standardized Ratio” instead of an “Absolute volume” greatly reduces the influence of experimental variation commonly associated with conventional 2‐DE.
Probing cellular function with bioluminescence imaging
Authors: Rutter Guy A. , da Silva Xavier Gabriela , Tsuboi Takashi , Mitchell Kathryn J. , Hanrahan John W. 
Published: November 2009

Abstract

Bioluminescence is widely used by lower organisms as a means for communication. In recent years, gene transfer technologies have allowed this potential to be harnessed by cell biologists interested in intracellular signaling and other cellular processes. Here, we focus on the use of bioluminescence to image ATP, Ca
2+
, and gene expression in single mammalian cells.
What use is a protein structure?
Authors: Phillips Simon E. V. 
Published: November 2009

Abstract
The revolution generated by the pioneering protein crystallographers in the second half of the twentieth century has created a huge database of known protein structures. These are increasingly being used to design new drugs and therapies, as well as provide novel engineered reagents for industrial processes. While the three‐dimensional structures of proteins are often beautiful in themselves, our ability to use and manipulate them is becoming increasingly valuable.
Modeling membrane protein structures
Authors: Arinaminpathy Yalini , Sansom Mark S.P. 
Published: November 2009

Abstract
Integral membrane proteins play a central role in many biological activities of cells. Only a small number of structures are known at high resolution, in contrast to the relatively large fraction (∼25%) of genes encoding membrane proteins. It is somewhat easier to model the structure of a transmembrane protein than that of a globular protein as a consequence of the constraints imposed by the transmembrane environment. Modeling of membrane proteins plays an important role as the majority of experimentally determined structures are bacterial homologs of human proteins. The first stage of membrane protein modeling is to predict the location and extent of the transmembrane α‐helices. A number of sequence‐based methods are available for this, and the consensus accuracy of prediction is ∼80%. If a three‐dimensional structure is available for a homolog of the target membrane protein, this structure may be used as a template for homology modeling of the target protein. Secondary structure prediction and experimentally derived restraints may be used to aid the process of homology modeling. Once generated, a model may be evaluated in terms of general principles of membrane protein structure, such as the presence of bands of tryptophan and tyrosine side chains on the membrane protein surface positioned so as to interact with lipid bilayer headgroups. The stability and dynamic behavior of the membrane protein model may be investigated via molecular dynamics simulations. As an example of the overall process, generation of a model of the transmembrane domain of a prokaryotic glutamate receptor (GluR0) using a bacterial potassium channel (KcsA) as a template is described.
Data collection and analysis in systems biology
Authors: Ideker Trey 
Published: November 2009

AbstractSystems biology studies biological systems by systematically perturbing them (biologically, genetically, or chemically); monitoring the global biological response at the gene, protein, and informational pathway level; integrating these data; and ultimately, formulating mathematical models that describe the structure of the system and its response to individual perturbations. The key technologies that enable systems biology are reviewed with a focus on high‐throughput measurement technologies and computational modeling approaches for integrating the resulting large‐scale data.
Contextual inference of protein function
Authors: Narain Seshasayee Aswin Sai , Babu M. Madan 
Published: November 2009

AbstractThe objective of this article is to provide an overview of various contextual, homology‐independent methods for inferring protein function. The article is divided into two parts; the first part discusses methods used to infer function from genome sequence data, while the second part deals with the application of large‐scale experimental data to infer function. Genome sequence–based methods fundamentally rely on identifying orthologs of the protein of interest across multiple genomes. On the other hand, the second class is closer to experimental studies, as the underlying data, namely, protein–protein or protein–DNA interaction or gene‐expression data, are essentially obtained from high‐throughput functional genomics experiments. This article tries to put experimental and computational methods in the right perspective, giving each of them the right emphasis, so that their usefulness is appreciated by the readers.
Large‐scale, classification‐driven, rule‐based functional annotation of proteins
Authors: Natale Darren A. , Vinayaka C. R. , Wu Cathy H. 
Published: November 2009

AbstractExperimentally verified information on protein function lags far behind the rapid accumulation of protein sequences. The simple approach to propagating information from characterized proteins to unknown proteins – namely, by sequence similarity search against databases of individual proteins – may fail to produce accurate results, and typically is used to transfer only protein name information. A more accurate, consistent, and comprehensive approach for large‐scale automated annotation makes use of protein family classification‐driven rules. Unannotated proteins that satisfy a set of conditions for a particular rule can be annotated with the information appropriate for that rule. The approach leads to facile, accurate prediction and functional inference for uncharacterized proteins, allows systematic detection of genome annotation errors, and provides sensible propagation and standardization of protein annotation, including position‐specific sequence features, protein names and synonyms, and Gene Ontology terms. Rule‐based annotation will be discussed in the context of the PIRSF protein classification system, PIRNR Name Rule system, and the PIRSR Site Rule system.
Signal peptides and protein localization prediction
Authors: Nielsen Henrik 
Published: November 2009

Abstract
Proteins have signals that direct their localization in the cell. The best known of these signals is the secretory signal peptide. Prediction of protein localization from the amino acid sequence is done by two approaches: either by recognizing the signals themselves or by using global properties, such as amino acid composition, of the protein. A number of machine‐learning methods using both these approaches are implemented as publicly available servers, and a brief presentation of these methods is given here.
Phylogenomics for studies of microbial evolution
Authors: Andersson Siv G. E. , Fuxelius Hans‐Henrik 
Published: November 2009

Abstract
The classification of microorganisms represents a major challenge, the basis of which has been an issue of debate for the past 100 years. Phylogenetic inference based on rRNA sequences is currently the gold standard for studies related to microbial evolution and classification. As more and more microbial genomes are added to the rapidly expanding pool of sequence data, automatic systems are being developed to meet the growing demand for large‐scale phylogenetic analyses. However, the development of such tools and their application to biological problems has raised more questions than have been solved, and the debate about the extent to which microorganisms are related by vertical descent versus horizontal transmission is more heated than ever before. Here, we describe the recent development of methods and tools for phylogenomic studies and their application in microbiological research.
Statistical methods for gene expression analysis
Authors: Deng Shibing , Chu Tzu‐Ming , Truong Young K. , Wolfinger Russ D. 
Published: November 2009

Abstract
Microarray technology makes it possible to simultaneously study the expression levels from thousands of genes and is widely used in functional genomics research. The analysis of microarray data provides a challenge to researchers because of its high dimensionality and complexity. Extensive research has been devoted recently to address the statistical issues raised from the analysis of the microarray data, and a comprehensive review is not possible here given space limitations and the proliferation of new papers. Instead, this chapter reviews some of the more basic statistical methods used for microarray gene expression data analysis. We divide the methods into three major areas: differential expression, classification, and clustering.
Complexity in biological structures and systems
Authors: Lesk Arthur M. 
Published: November 2009

AbstractComplexity and related concepts are important for understanding life processes as revealed in the new high‐throughput data streams. Formal analysis of complexity is less advanced in biology than in certain other fields, including mathematics, physics, and computer science. This article offers an introduction to complexity for biologists, with emphasis on explaining the concepts and results from other fields applicable to understanding living systems at the molecular level. Many of the questions raised are subjects of current research.
Perl in bioinformatics
Authors: Niedner R. Hannes , Nair T. Murlidharan , Gribskov Michael 
Published: November 2009

AbstractBioinformatics evolved as a discipline that applies techniques from math, computer science, and statistics to make sense of the vast amounts of data produced by high‐throughput experiments in biology. These data are frequently output as experiment‐specific text files, which need to be parsed from and to a variety of formats to be amenable to further computational analyses. This process often involves accessing of additional information from on‐line repositories, reading and writing to and from files and databases, and ultimately the presentation of results on the World Wide Web.Perl stands for Practical Extraction and Report Language, and was developed as a UNIX glue language by Larry Wall in the mid‐1980s. It has proven to be a powerful tool for the tasks outlined above. Since its inception, Perl has grown far beyond its original use as a text‐parsing tool into a fully featured high‐level programming language. In this article, we give an overview on how this versatile language can be used to tackle a wide variety of frequent problems in bioinformatics.