Macro assembler for mc68000 series

The objective of this thesis is to use UNIX\u27s utility Yacc (Yet Another Compiler-Compiler) as a language developing tool to design and implement a MC68000 macro assembler. The assembler can support four MC68000 machine languages MC68000, MC68008, MC68010, and MC68020

Towards identifying intervention arms in randomized controlled trials: Extracting coordinating constructions

AbstractBackground: Large numbers of reports of randomized controlled trials (RCTs) are published each year, and it is becoming increasingly difficult for clinicians practicing evidence-based medicine to find answers to clinical questions. The automatic machine extraction of RCT experimental details, including design methodology and outcomes, could help clinicians and reviewers locate relevant studies more rapidly and easily. Aim: This paper investigates how the comparison of interventions is documented in the abstracts of published RCTs. The ultimate goal is to use automated text mining to locate each intervention arm of a trial. This preliminary work aims to identify coordinating constructions, which are prevalent in the expression of intervention comparisons. Methods and results: An analysis of the types of constructs that describe the allocation of intervention arms is conducted, revealing that the compared interventions are predominantly embedded in coordinating constructions. A method is developed for identifying the descriptions of the assignment of treatment arms in clinical trials, using a full sentence parser to locate coordinating constructions and a statistical classifier for labeling positive examples. Predicate-argument structures are used along with other linguistic features with a maximum entropy classifier. An F-score of 0.78 is obtained for labeling relevant coordinating constructions in an independent test set. Conclusions: The intervention arms of a randomized controlled trials can be identified by machine extraction incorporating syntactic features derived from full sentence parsing

Towards bioinformatics assisted infectious disease control

BACKGROUND: This paper proposes a novel framework for bioinformatics assisted biosurveillance and early warning to address the inefficiencies in traditional surveillance as well as the need for more timely and comprehensive infection monitoring and control. It leverages on breakthroughs in rapid, high-throughput molecular profiling of microorganisms and text mining. RESULTS: This framework combines the genetic and geographic data of a pathogen to reconstruct its history and to identify the migration routes through which the strains spread regionally and internationally. A pilot study of Salmonella typhimurium genotype clustering and temporospatial outbreak analysis demonstrated better discrimination power than traditional phage typing. Half of the outbreaks were detected in the first half of their duration. CONCLUSION: The microbial profiling and biosurveillance focused text mining tools can enable integrated infectious disease outbreak detection and response environments based upon bioinformatics knowledge models and measured by outcomes including the accuracy and timeliness of outbreak detection.9 page(s

A promoção da reflexão sobre a aprendizagem nas crianças

Promover a reflexão sobre a aprendizagem das crianças é um objetivo para muitos Educadores e Professores. Será que estes agem de acordo com este objetivo? Na sequência de dar resposta a esta questão, origina-se a presente investigação que se refere à promoção da reflexão sobre a aprendizagem das crianças, enquadrada no Mestrado em Educação Pré-Escolar e Ensino do 1º Ciclo do Ensino Básico. Assim, esta investigação incidirá sobre a promoção da reflexão sobre a aprendizagem nas crianças. Com a finalidade de dar sentido a esta investigação, foram realizadas entrevistas a Educadoras de Infância e Professores do 1.º Ciclo do Ensino Básico do contexto de estágio e confrontação com a teoria. Esta investigação permitiu mostrar o que acontece na prática, ou seja, se realmente os profissionais da educação fornecem instrumentos às crianças para que estas possam refletir sobre as suas aprendizagens. Palavras-chave: Criança, educador, professor, avaliação, aprendizagem, reflexão.Promoting reflection on children's learning is a goal for many Educators and Teachers. Do they act according to this goal? In order to give response to this question, this research about promoting reflection on children’s learning, included in Master´s Degree in Preschool Education and Teaching of the Primary School Education, has been carried out. This research will focus on the reflection’s promotion about children’s learning. In order to make sense of this research, interviews were conducted with Educators and Teachers of the 1st Cycle of Basic Education of the context of the stage and confrontation with the theory. This research showed what happens in practice and helped us to realize if Education Professionals provide instruments to children so that they can reflect on their learning

Hierarchical duration modeling for a speech recognition system

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 102-105).by Grace Chung.M.S

Towards multi-domain speech understanding with flexible and dynamic vocabulary

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 201-208).In developing telephone-based conversational systems, we foresee future systems capable of supporting multiple domains and flexible vocabulary. Users can pursue several topics of interest within a single telephone call, and the system is able to switch transparently among domains within a single dialog. This system is able to detect the presence of any out-of-vocabulary (OOV) words, and automatically hypothesizes each of their pronunciation, spelling and meaning. These can be confirmed with the user and the new words are subsequently incorporated into the recognizer lexicon for future use. This thesis will describe our work towards realizing such a vision, using a multi-stage architecture. Our work is focused on organizing the application of linguistic constraints in order to accommodate multiple domain topics and dynamic vocabulary at the spoken input. The philosophy is to exclusively apply below word-level linguistic knowledge at the initial stage. Such knowledge is domain-independent and general to all of the English language. Hence, this is broad enough to support any unknown words that may appear at the input, as well as input from several topic domains. At the same time, the initial pass narrows the search space for the next stage, where domain-specific knowledge that resides at the word-level or above is applied. In the second stage, we envision several parallel recognizers, each with higher order language models tailored specifically to its domain. A final decision algorithm selects a final hypothesis from the set of parallel recognizers.(cont.) Part of our contribution is the development of a novel first stage which attempts to maximize linguistic constraints, using only below word-level information. The goals are to prevent sequences of unknown words from being pruned away prematurely while maintaining performance on in-vocabulary items, as well as reducing the search space for later stages. Our solution coordinates the application of various subword level knowledge sources. The recognizer lexicon is implemented with an inventory of linguistically motivated units called morphs, which are syllables augmented with spelling and word position. This first stage is designed to output a phonetic network so that we are not committed to the initial hypotheses. This adds robustness, as later stages can propose words directly from phones. To maximize performance on the first stage, much of our focus has centered on the integration of a set of hierarchical sublexical models into this first pass. To do this, we utilize the ANGIE framework which supports a trainable context-free grammar, and is designed to acquire subword-level and phonological information statistically. Its models can generalize knowledge about word structure, learned from in-vocabulary data, to previously unseen words. We explore methods for collapsing the ANGIE models into a finite-state transducer (FST) representation which enables these complex models to be efficiently integrated into recognition. The ANGIE-FST needs to encapsulate the hierarchical knowledge of ANGIE and replicate ANGIE's ability to support previously unobserved phonetic sequences ...by Grace Chung.Ph.D

Customized Order-Entry Sets Can Prevent Antiretroviral Prescribing Errors: A Novel Opportunity For Antimicrobial Stewardship

Background: Patients with human immunodeficiency virus (HIV) infection on antiretroviral (ARV) therapy are at increased risk for medication errors during transitions of care between the outpatient and inpatient settings. This can lead to treatment failure or toxicity. Previous studies have emphasized the prevalence of medication errors in such patients, but few have reported initiatives to prevent errors from occurring. Methods: The study was conducted in a 1,400-bed health care center with a state-designated Acquired Immunodeficiency Syndrome (AIDS) Center in the Bronx, New York. The antimicrobial stewardship team and HIV specialists developed customized order-entry sets (COES) to guide ARV prescribing and retrospectively reviewed their effect on error rates of initial ARV orders for inpatients before reconciliation. Patient records were reviewed in six-month periods before and after intervention. The student’s t-test or Mann–Whitney U test was used to compare continuous variables; chi-square or Fisher’s exact test was used for categorical variables. Results: A total of 723 and 661 admissions were included in the pre-intervention and post-intervention periods, respectively. Overall, error rates decreased by 35% (38.0% to 24.8%, P \u3c 0.01) with COES. Wrong doses and drug interactions decreased by more than 40% (P \u3c 0.005). Error reductions were observed in protease inhibitor (PI)-based (43.6% versus 28.7%, P \u3c 0.01) and non–PI-based (38.0% versus 24.4%, P = 0.02) regimens with COES. A shift in predominant drug-class errors was observed as there was a trend toward increased usage of non-PI regimens post-intervention. Admission in the pre-intervention period (adjusted odds ratio [AOR], 1.79; 95% confidence interval [CI], 1.39–2.31) and use of PI-based regimens (AOR, 2.03; 95% CI, 1.53–2.70) remained significantly associated with ARV prescribing errors after controlling for confounding factors. Conclusion: Detailed COES improved ARV prescribing habits, reduced the potential for prescribing incorrect regimens, and can prove useful and cost-effective where HIV-specific medication reconciliation is unavailable

A novel modified-indirect ELISA based on spherical body protein 4 for detecting antibody during acute and long-term infections with diverse Babesia bovis strains

Cattle sera positive by the RAP-1-based cELISA but negative by the SBP4-based MI-ELISA and IFA had negative results by Western blot analysis, suggesting possible false positive results in the cELISA. A. Molecular weight marker (48 to 180 Kd), B. K42-#21, C. W31-#Y-3, D. W31-#Y-11, E. W31-#0-3, F. W31-#Y-9, G. W31-#0-9, H. W31-#Y-10, I. W31-#Y-15, J. P21-#224, K. positive control serum with a band at 75kd representing B. bovis RAP-1 protein, J. negative control serum. Figure S2. Technical difference between the modified indirect ELISA and conventional indirect ELISA using rGST-SBP4 was illustrated in this figure. (DOCX 645 kb

Memory Management Using Tab Discard and Reload Prediction

Browsers and other multi-tab applications discard tabs when there is insufficient memory. When a tab has been discarded, the user is forced to reload the tab to continue interaction. Selection of tabs to discard can be based on simple heuristics; however, such selection can lead to discarding tabs that the user is likely to use. Incorrectly discarded tabs are disruptive to users. This disclosure describes the use of machine learning techniques to generate more accurate predictions to select the tab to be discarded. Selectively discarding tabs in this manner can improve memory management while also providing a better user experience

Inferring Genetic Interactions via a Data-Driven Second Order Model

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R3) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm