Clinical and Molecular Characterization of Patients with Mucopolysaccharidosis Type I in an Algerian Series

Mucopolysaccharidoses (MPS’s) represent a subgroup of lysosomal storage diseases related to a deficiency of enzymes that catalyze glycosaminoglycans degradation. Mucopolysaccharidosis type I (MPS I) is a rare autosomal recessive disorder caused by a deficiency of α-l-iduronidase encoded by the IDUA gene. Partially degraded heparan sulfate and dermatan sulfate accumulate progressively and lead to multiorgan dysfunction and damage. The aim of this study is to describe the clinical, biochemical, and molecular characteristics of 13 Algerian patients from 11 distinct families. MPS I diagnosis was confirmed by molecular study of the patients’ IDUA gene. Clinical features at the diagnosis and during the follow-up are reported. Eighty-four percent of the studied patients presented with a mild clinical phenotype. Molecular study of the IDUA gene allowed the characterization of four pathological variations at the homozygous or compound heterozygote status: IDUA NM_00203.4:c.1598C>G-p.(Pro533Arg) in 21/26 alleles, IDUA NM_00203.4:c.532G>A-p.(Glu178Lys) in 2/26 alleles, IDUA NM_00203.4:c.501C>G-p.(Tyr167*) in 2/26 alleles, and IDUA NM_00203. 4: c.1743C>G-p.(Tyr581*) in 1/26 alleles. This molecular study unveils the predominance of p.(Pro533Arg) variation in our MPS I patients. In this series, the occurrence of some clinical features linked to the Scheie syndrome is consistent with the literature, such as systematic valvulopathies, corneal opacity, and umbilical hernia; however, storage signs, facial dysmorphic features, and hepatomegaly were more frequent in our series. Screening measures for these debilitating diseases in highly consanguineous at-risk populations must be considered a priority health problem.     

Development of Microsatellite Markers in the Branched Broomrape Phelipanche ramosa L. (Pomel) and Evidence for Host-Associated Genetic Divergence

Phelipanche ramosa is a parasitic plant that infects numerous crops worldwide. In Western Europe it recently expanded to a new host crop, oilseed rape, in which it can cause severe yield losses. We developed 13 microsatellite markers for P. ramosa using next-generation 454 sequencing data. The polymorphism at each locus was assessed in a sample of 96 individuals collected in France within 6 fields cultivated with tobacco, hemp or oilseed rape. Two loci were monomorphic. At the other 11 loci, the number of alleles and the expected heterozygosity ranged from 3 to 6 and from 0.31 to 0.60, respectively. Genetic diversity within each cultivated field was very low. The host crop from which individuals were collected was the key factor structuring genetic variation. Individuals collected on oilseed rape were strongly differentiated from individuals collected on hemp or tobacco, which suggests that P. ramosa infecting oilseed rape forms a genetically diverged race. The microsatellites we developed will be useful for population genetics studies and for elucidating host-associated genetic divergence in P. ramosa.     

A Non-local Topology-Preserving Segmentation-Guided Registration Model

In this paper, we address the issue of designing a theoretically well-motivated segmentation-guided registration method capable of handling large and smooth deformations. The shapes to be matched are viewed as hyperelastic materials and more precisely as Saint Venant–Kirchhoff ones and are implicitly modeled by level set functions. These are driven in order to minimize a functional containing both a nonlinear-elasticity-based regularizer prescribing the nature of the deformation, and a criterion that forces the evolving shape to match intermediate topology-preserving segmentation results. Theoretical results encompassing existence of minimizers, existence of a weak viscosity solution of the related evolution problem and asymptotic results are given. The study is then complemented by the derivation of the discrete counterparts of the asymptotic results provided in the continuous domain. Both a pure quadratic penalization method and an augmented Lagrangian technique (involving a related dual problem) are investigated with convergence results.     

Learning domain ontologies for semantic Web service descriptions

High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains.

Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques. Indeed, the novelty of our work is not in the used natural language processing methods but rather in the way they are put together in a generic framework specialized for the context of Web services.     

Temporal Data Mining for Educational Applications

Intelligent tutoring systems (ITSs) acquire rich data about students' behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. This paper surveys a variety of data mining techniques for analyzing how students interact with ITSs, including methods for handling hidden state variables, and for testing hypotheses. To illustrate these methods we draw on data from two ITSs for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.     

A formal semantics for the Taverna 2 workflow model

This paper presents a formal semantics for the Taverna 2 scientific workflow system. Taverna 2 is a successor to Taverna, an open-source workflow system broadly adopted within the e-science community worldwide. The new version improves upon the existing model in two main ways: (i) by adding support for data pipelining, which in turns enables input streams of indefinite length to be processed efficiently; and (ii) by providing new extensibility points that make it possible to add new operators to the workflow model. Consistent with previous work by some of the authors, we use trace semantics to describe the effect of workflow computations, and we show how they can be used to describe the new features in the Taverna 2 model.     

Toward More Comprehensive Chinese Internet Users’ Studies: Translation and Validation of the Chinese–Mandarin Version of the 8-Item Information Retrieval on the Web Self-Efficacy Scale (Ch-IROWSE)

In this article, we fill a dearth in the literature of Internet self-efficacy (ISS) measurement. We introduce the translation and validation of the Chinese-Mandarin version of the 8-item Information Retrieval On the Web Self-Efficacy scale (IROWSE scale), which has been originally developed in the French language (Rodon & Meyer, 2017). After back-forward translation procedure and content clarity, we ran a test of this new version. We checked the intrinsic psychometric properties including reliability, factor structure (CFA), and criterion-related validity regarding skills for retrieving information on the Web, general attitude toward the Web, experience, gender, and age. Assessment of construct validity indicated, as expected, a one-factor model for the eight items (with coefficient alpha equal to 0.90). Its sum-scores were not confounded with skill levels or general attitudes, and it founds its sources in direct experiences. Furthermore, research would take into account the Internet World population more broadly beyond the West with this new version.     

Cross-Campus Collaboration to Produce Camtasia-Enhanced Lessons to Improve Distance Learning

This pilot study examines the use of Camtasia-enhanced lessons to supplement distance education provided to medical laboratory science students. More programs of study are available as distance learning opportunities, creating challenges to find better ways to present material. Librarians provided technical expertise necessary to produce a Camtasia video. This helped instructors prepare engaging lectures for phlebotomy to supplement the PowerPoint presentations and self-directed lessons provided in previous courses. Medical laboratory science faculty collaborated with library staff to produce videos that demonstrate proper phlebotomy technique using training personnel and faculty, whom the students eventually interact with in the clinical environment.     

The relationship between Algebra Nation usage and high‐stakes test performance for struggling students

Although the use of technology in the K12 classroom has been shown to have a positive impact, research on the use of open education resources (OER) is relatively limited, especially research focusing on low‐achieving students. The present study examines the relationship between usage of Algebra Nation, a self‐guided system that provided instructional videos and practice problems, and the performance of students who had failed the state‐administered Algebra I end‐of‐course (EOC) assessment the previous year. Indicators of usage of Algebra Nation consisted of logins, video views, and practice questions answered. Path analyses and logistic regressions were used to evaluate relationships between usage indicators and algebra scores, controlling for number of absences, free/reduced lunch eligibility, Hispanic/Latino origin, race, and gender. The results indicate that higher levels of logins, video views, and practice questions answered were related to higher scores when the students re‐took the assessment. Logins and practice questions were also related to increases in odds of passing the Algebra I EOC assessment, but not video views. The results suggest that there may be benefits to technology use in the form of an OER adopted by students and teachers on an informal basis and link self‐regulated learning strategies to student achievement.