The Wheat Peptidyl Prolyl cis-trans-Isomerase FKBP77 Is Heat Induced and Developmentally Regulated1

We isolated a cDNA encoding a 568-amino acid, heat-stress-induced peptidyl prolyl isomerase belonging to the FK506-binding-protein (FKBP) family. The open reading frame encodes for a peptidyl prolyl isomerase that possesses three FKBP-12-like domains, a putative tetratricopeptide motif, and a calmodulin-binding domain. Specific antibodies showed that the open reading frame encodes a heat-induced 77-kD protein, the wheat FKBP77 (wFKBP77), which exhibits 84% identity with the wFKBP73 and 42% identity with the human FKBP59. Because of the high similarity in sequence to wFKBP73, wFKBP77 was designated as the heat-induced isoform. The wFKBP77 mRNA steady-state level was 14-fold higher at 37°C than at 25°C. The wFKBP77 transcript abundance was the highest in mature embryos that had imbibed and 2-d-old green shoots exposed to 37°C, and decreased to 6% in 6-d-old green shoots. The transcript level returned to the level detected at 25°C after recovery of the embryos for 90 min at 25°C. We compared wFKBP73 and wFKBP77 with the heat-shock proteins having cognate and heat-stress-induced counterparts.

The role of left prefrontal cortex in language and memory

This article reviews attempts to characterize the mental operations mediated by left inferior prefrontal cortex, especially the anterior and inferior portion of the gyrus, with the functional neuroimaging techniques of positron emission tomography and functional magnetic resonance imaging. Activations in this region occur during semantic, relative to nonsemantic, tasks for the generation of words to semantic cues or the classification of words or pictures into semantic categories. This activation appears in the right prefrontal cortex of people known to be atypically right-hemisphere dominant for language. In this region, activations are associated with meaningful encoding that leads to superior explicit memory for stimuli and deactivations with implicit semantic memory (repetition priming) for words and pictures. New findings are reported showing that patients with global amnesia show deactivations in the same region associated with repetition priming, that activation in this region reflects selection of a response from among numerous relative to few alternatives, and that activations in a portion of this region are associated specifically with semantic relative to phonological processing. It is hypothesized that activations in left inferior prefrontal cortex reflect a domain-specific semantic working memory capacity that is invoked more for semantic than nonsemantic analyses regardless of stimulus modality, more for initial than for repeated semantic analysis of a word or picture, more when a response must be selected from among many than few legitimate alternatives, and that yields superior later explicit memory for experiences.

Differential synaptic localization of two major gamma-aminobutyric acid type A receptor alpha subunits on hippocampal pyramidal cells.

Hippocampal pyramidal cells, receiving domain specific GABAergic inputs, express up to 10 different subunits of the gamma-aminobutyric acid type A (GABAA) receptor, but only 3 different subunits are needed to form a functional pentameric channel. We have tested the hypothesis that some subunits are selectively located at subsets of GABAergic synapses. The alpha 1 subunit has been found in most GABAergic synapses on all postsynaptic domains of pyramidal cells. In contrast, the alpha 2 subunit was located only in a subset of synapses on the somata and dendrites, but in most synapses on axon initial segments innervated by axo-axonic cells. The results demonstrate that molecular specialization in the composition of postsynaptic GABAA receptor subunits parallels GABAergic cell specialization in targeting synapses to a specific domain of postsynaptic cortical neurons.

An Empirical Comparison of Reuse in Embedded and Nonembedded Systems

High-quality software, delivered on time and budget, constitutes a critical part of most products and services in modern society. Our government has invested billions of dollars to develop software assets, often to redevelop the same capability many times. Recognizing the waste involved in redeveloping these assets, in 1992 the Department of Defense issued the Software Reuse Initiative. The vision of the Software Reuse Initiative was "To drive the DoD software community from its current "re-invent the software" cycle to a process-driven, domain-specific, architecture-centric, library-based way of constructing software.'' Twenty years after issuing this initiative, there is evidence of this vision beginning to be realized in nonembedded systems. However, virtually every large embedded system undertaken has incurred large cost and schedule overruns. Investigations into the root cause of these overruns implicates reuse. Why are we seeing improvements in the outcomes of these large scale nonembedded systems and worse outcomes in embedded systems? This question is the foundation for this research. The experiences of the Aerospace industry have led to a number of questions about reuse and how the industry is employing reuse in embedded systems. For example, does reuse in embedded systems yield the same outcomes as in nonembedded systems? Are the outcomes positive? If the outcomes are different, it may indicate that embedded systems should not use data from nonembedded systems for estimation. Are embedded systems using the same development approaches as nonembedded systems? Does the development approach make a difference? If embedded systems develop software differently from nonembedded systems, it may mean that the same processes do not apply to both types of systems. What about the reuse of different artifacts? Perhaps there are certain artifacts that, when reused, contribute more or are more difficult to use in embedded systems. Finally, what are the success factors and obstacles to reuse? Are they the same in embedded systems as in nonembedded systems? The research in this dissertation is comprised of a series of empirical studies using professionals in the aerospace and defense industry as its subjects. The main focus has been to investigate the reuse practices of embedded systems professionals and nonembedded systems professionals and compare the methods and artifacts used against the outcomes. The research has followed a combined qualitative and quantitative design approach. The qualitative data were collected by surveying software and systems engineers, interviewing senior developers, and reading numerous documents and other studies. Quantitative data were derived from converting survey and interview respondents' answers into coding that could be counted and measured. From the search of existing empirical literature, we learned that reuse in embedded systems are in fact significantly different from nonembedded systems, particularly in effort in model based development approach and quality where the development approach was not specified. The questionnaire showed differences in the development approach used in embedded projects from nonembedded projects, in particular, embedded systems were significantly more likely to use a heritage/legacy development approach. There was also a difference in the artifacts used, with embedded systems more likely to reuse hardware, test products, and test clusters. Nearly all the projects reported using code, but the questionnaire showed that the reuse of code brought mixed results. One of the differences expressed by the respondents to the questionnaire was the difficulty in reuse of code for embedded systems when the platform changed. The semistructured interviews were performed to tell us why the phenomena in the review of literature and the questionnaire were observed. We asked respected industry professionals, such as senior fellows, fellows and distinguished members of technical staff, about their experiences with reuse. We learned that many embedded systems used heritage/legacy development approaches because their systems had been around for many years, before models and modeling tools became available. We learned that reuse of code is beneficial primarily when the code does not require modification, but, especially in embedded systems, once it has to be changed, reuse of code yields few benefits. Finally, while platform independence is a goal for many in nonembedded systems, it is certainly not a goal for the embedded systems professionals and in many cases it is a detriment. However, both embedded and nonembedded systems professionals endorsed the idea of platform standardization. Finally, we conclude that while reuse in embedded systems and nonembedded systems is different today, they are converging. As heritage embedded systems are phased out, models become more robust and platforms are standardized, reuse in embedded systems will become more like nonembedded systems.

Examining the Complex Relationships of Early Intervention on Language and Psychosocial Development in Families Facing Multiple Risks with a Focus on Children of Hispanic Immigrants

Abundant research has shown that poverty has negative influences on young child academic and psychosocial development, and unfortunately, disparities in school readiness between low and high income children can be seen as early the first year of life. The largest federal early care and education intervention for these vulnerable children is Early Head Start (EHS). To diminish these disparate child outcomes, EHS seeks to provide community based flexible programming for infants and toddlers and their families. Given how relatively recent these programs have been offered, little is known about the nuances of how EHS impacts infant and toddler language and psychosocial development. Using a framework of Community Based Participatory Research (CBPR) this paper had 5 goals: 1) to characterize the associations between domain specific and cumulative risk and child outcomes 2) to validate and explore these risk-outcome associations separately for Children of Hispanic immigrants (COHIs), 3) to explore relationships among family characteristics, multiple environmental factors, and dosage patterns in different EHS program types, 4) to examine the relationship between EHS dosage and child outcomes, and 5) to examine how EHS compliance impacts child internalizing and externalizing behaviors and emerging language abilities. Results of the current study showed that risks were differentially related to child outcomes. Poor maternal mental health was related to child internalizing and externalizing behaviors, but not related to emerging child language skills. Although child language skills were not related to maternal mental health, they were related to economic hardship. Additionally, parent level Spanish use and heritage orientation were associated with positive child outcomes. Results also showed that these relationships differed when COHIs and children with native-born parents were examined separately. Further, unique patterns emerged for EHS program use, for example families who participated in home-based care were less likely to comply with EHS attendance requirements. These findings provide tangible suggestions for EHS stakeholders: namely, the need to develop effective programming that targets engagement for diverse families enrolled in EHS programs.

How to make a natural language interface to query databases accessible to everyone: An example

Natural Language Interfaces to Query Databases (NLIDBs) have been an active research field since the 1960s. However, they have not been widely adopted. This article explores some of the biggest challenges and approaches for building NLIDBs and proposes techniques to reduce implementation and adoption costs. The article describes {AskMe*}, a new system that leverages some of these approaches and adds an innovative feature: query-authoring services, which lower the entry barrier for end users. Advantages of these approaches are proven with experimentation. Results confirm that, even when {AskMe*} is automatically reconfigurable against multiple domains, its accuracy is comparable to domain-specific NLIDBs.

Transfer of Pattern Recall Skills May Contribute to the Development of Sport Expertise

Superior recall of domain-specific patterns is well established as a defining attribute of expert performers. Recent studies on the developmental histories of expert team ball sport players (e.g. Baker, Côté, & Abernethy, 2003a) also suggest that experts characteristically receive exposure to a wide range of sports in their developing years and that this related sports experience may reduce the amount of sport-specific training needed to become an expert. This study examined whether the facilitation of expertise associated with other sport experience might arise from positive transfer of pattern recall skills from one sport to another. Expert netball, basketball and field hockey players and experienced non-experts performed a recall task for patterns of play derived from each of these sports. Experts from sports different to those shown in the presented pattern consistently outperformed non-experts in their recall of defensive player positions, suggesting some selective transfer of pattern recall skills may indeed be possible

The Moldable Inspector

Object inspectors are an essential category of tools that allow developers to comprehend the run-time of object-oriented systems. Traditional object inspectors favor a generic view that focuses on the low-level details of the state of single objects. Based on 16 interviews with software developers and a follow-up survey with 62 respondents we identified a need for object inspectors that support different high-level ways to visualize and explore objects, depending on both the object and the current developer need. We propose the Moldable Inspector, a novel inspector model that enables developers to adapt the inspection workflow to suit their immediate needs by making the inspection context explicit, providing multiple interchangeable domain-specific views for each object, and supporting a workflow that groups together multiple levels of connected objects. We show that the Moldable Inspector can address multiple kinds of development needs involving a wide range of objects.

Hodgkin's lymphoma cell lines express a fusion protein encoded by intergenically spliced mRNA for the multilectin receptor DEC-205 (CD205) and a novel C-type lectin receptor DCL-1

Classic Hodgkin's lymphoma (HL) tissue contains a small population of morphologically distinct malignant cells called Hodgkin and Reed-Sternberg (HRS) cells, associated with the development of HL. Using 3'-rapid amplification of cDNA ends ( RACE) we identified an alternative mRNA for the DEC-205 multilectin receptor in the HRS cell line L428. Sequence analysis revealed that the mRNA encodes a fusion protein between DEC-205 and a novel C-type lectin DCL-1. Although the 7.5-kb DEC-205 and 4.2-kb DCL-1 mRNA were expressed independently in myeloid and B lymphoid cell lines, the DEC-205/DCL-1 fusion mRNA (9.5 kb) predominated in the HRS cell lines ( L428, KM-H2, and HDLM-2). The DEC-205 and DCL-1 genes comprising 35 and 6 exons, respectively, are juxtaposed on chromosome band 2q24 and separated by only 5.4 kb. We determined the DCL-1 transcription initiation site within the intervening sequence by 5'-RACE, confirming that DCL-1 is an independent gene. Two DEC-205/DCL-1 fusion mRNA variants may result from cotranscription of DEC-205 and DCL-1, followed by splicing DEC-205 exon 35 or 34-35 along with DCL-1 exon 1. The resulting reading frames encode the DEC-205 ectodomain plus the DCL-1 ectodomain, the transmembrane, and the cytoplasmic domain. Using DCL-1 cytoplasmic domain-specific polyclonal and DEC-205 monoclonal antibodies for immunoprecipitation/Western blot analysis, we showed that the fusion mRNA is translated into a DEC-205/DCL-1 fusion protein, expressed in the HRS cell lines. These results imply an unusual transcriptional control mechanism in HRS cells, which cotranscribe an mRNA containing DEC-205 and DCL-1 prior to generating the intergenically spliced mRNA to produce a DEC-205/DCL-1 fusion protein.

The application of cognitive orientation to daily occupational performance (CO-OP) with children 5-7 years with developmental coordination disorder

There has been an increase in the use of cognitive frameworks in occupational therapy with children with developmental coordination disorder (DCD). Investigations into the utility of one such cognitive approach, namely Cognitive Orientation to (daily) Occupational Performance (CO-OP), with children with DCD have shown the intervention to be effective with children over 7 years. However, there has been limited research into its utility with younger children. This paper presents two case studies to demonstrate the use of CO-OP with children aged 5-7 years. Two boys with DCD engaged in 10 sessions of CO-OP. These younger children were found to be able to use the global framework (Goal, Plan, Do, Check) to improve their task performance, to develop plans using domain-specific strategies and to engage in checking strategies. Issues relating to attention, motivation and goal setting are discussed in the context of the two case studies.

Fitness assessment of document model

Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, Resource selection for domain-specific cross-lingual IR, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification.

A reference architecture for instructional educational software

Our extensive research has indicated that high-school teachers are reluctant to make use of existing instructional educational software (Pollard, 2005). Even software developed in a partnership between a teacher and a software engineer is unlikely to be adopted by teachers outside the partnership (Pollard, 2005). In this paper we address these issues directly by adopting a reusable architectural design for instructional educational software which allows easy customisation of software to meet the specific needs of individual teachers. By doing this we will facilitate more teachers regularly using instructional technology within their classrooms. Our domain-specific software architecture, Interface-Activities-Model, was designed specifically to facilitate individual customisation by redefining and restructuring what constitutes an object so that they can be readily reused or extended as required. The key to this architecture is the way in which the software is broken into small generic encapsulated components with minimal domain specific behaviour. The domain specific behaviour is decoupled from the interface and encapsulated in objects which relate to the instructional material through tasks and activities. The domain model is also broken into two distinct models - Application State Model and Domainspecific Data Model. This decoupling and distribution of control gives the software designer enormous flexibility in modifying components without affecting other sections of the design. This paper sets the context of this architecture, describes it in detail, and applies it to an actual application developed to teach high-school mathematical concepts.

Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

Ontologies, taxonomies, thesauri:learning from texts

The use of ontologies as representations of knowledge is widespread but their construction, until recently, has been entirely manual. We argue in this paper for the use of text corpora and automated natural language processing methods for the construction of ontologies. We delineate the challenges and present criteria for the selection of appropriate methods. We distinguish three ma jor steps in ontology building: associating terms, constructing hierarchies and labelling relations. A number of methods are presented for these purposes but we conclude that the issue of data-sparsity still is a ma jor challenge. We argue for the use of resources external tot he domain specific corpus.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.