A newly recognized syndrome of marfanoid habitus; Long face; Hypotelorism; Long, thin nose; Long, thin hands and feet; and a specific pattern of language and learning disabilities

Here, we report on a newly recognized syndrome in a Brazilian family with three affected women, who had a Marfanoid habitus; long face; hypotelorism; long, thin nose; long, thin hands and feet; and language and learning disabilities. The disorder is compatible with autosomal dominant inheritance. (C) 2007 Wiley-Liss, Inc.

Increased prevalence of sex chromosome aneuploidies in specific language impairment and dyslexia

Aim: Sex chromosome aneuploidies increase the risk of spoken or written language disorders but individuals with specific language impairment (SLI) or dyslexia do not routinely undergo cytogenetic analysis. We assess the frequency of sex chromosome aneuploidies in individuals with language impairment or dyslexia. Method: Genome-wide single nucleotide polymorphism genotyping was performed in three sample sets: a clinical cohort of individuals with speech and language deficits (87 probands: 61 males, 26 females; age range 4 to 23 years), a replication cohort of individuals with SLI, from both clinical and epidemiological samples (209 probands: 139 males, 70 females; age range 4 to 17 years), and a set of individuals with dyslexia (314 probands: 224 males, 90 females; age range 7 to 18 years). Results: In the clinical language-impaired cohort, three abnormal karyotypic results were identified in probands (proband yield 3.4%). In the SLI replication cohort, six abnormalities were identified providing a consistent proband yield (2.9%). In the sample of individuals with dyslexia, two sex chromosome aneuploidies were found giving a lower proband yield of 0.6%. In total, two XYY, four XXY (Klinefelter syndrome), three XXX, one XO (Turner syndrome), and one unresolved karyotype were identified. Interpretation: The frequency of sex chromosome aneuploidies within each of the three cohorts was increased over the expected population frequency (approximately 0.25%) suggesting that genetic testing may prove worthwhile for individuals with language and literacy problems and normal non-verbal IQ. Early detection of these aneuploidies can provide information and direct the appropriate management for individuals. © 2013 The Authors. Developmental Medicine & Child Neurology published by John Wiley & Sons Ltd on behalf of Mac Keith Press.

Writing Motivation of Students with Specific Language Impairments

This study was designed to compare the writing motivation of students with specific language impairments with their non-disabled peers. Due to the cognitive and linguistic demands of the writing process, students with language impairments face unique difficulties during the writing process. It was hypothesized that students with specific language impairments will be more likely to report lower levels of perceived writing competence and be less autonomously motivated to write. Students in grades 3-5 in 11 schools (33 with specific language impairments, 242 non-disabled peers) completed self-report measures, designed from a Self-Determination Theory perspective, which measured the degree that students are intrinsically motivated to write as well as their perceived writing competence. Statistical analyses showed that (1) students with specific language impairments reported lower levels of perceived writing competence and autonomous writing motivation; (2) SLI status was a significant predictor of perceived writing competence after spelling, grade, and gender were controlled; and (3) when spelling, grade, and gender were controlled, perceived writing competence was a significant predictor of autonomous writing motivation, but SLI status was not. The results of this study are expected to inform the current understanding of the relationship between language ability and writing motivation in students with specific language impairments, as well as the design of future writing interventions.

Nondeclarative learning in children with Specific Language Impairment: Predicting regularities in the visuomotor, phonological, and cognitive domains.

Ullman (2004) suggested that Specific Language Impairment (SLI) results from a general procedural learning deficit. In order to test this hypothesis, we investigated children with SLI via procedural learning tasks exploring the verbal, motor, and cognitive domains. Results showed that compared with a Control Group, the children with SLI (a) were unable to learn a phonotactic learning task, (b) were able but less efficiently to learn a motor learning task and (c) succeeded in a cognitive learning task. Regarding the motor learning task (Serial Reaction Time Task), reaction times were longer and learning slower than in controls. The learning effect was not significant in children with an associated Developmental Coordination Disorder (DCD), and future studies should consider comorbid motor impairment in order to clarify whether impairments are related to the motor rather than the language disorder. Our results indicate that a phonotactic learning but not a cognitive procedural deficit underlies SLI, thus challenging Ullmans' general procedural deficit hypothesis, like a few other recent studies.

Teaching and learning German language and culture in higher education in Britain:problems, challenges and didactical implications. A case study

The aim of this thesis is to explore key aspects and problems of the institutionalised teaching and learning of German language and culture in the context of German Studies in British Higher Education (HE). This investigation focuses on teaching and learning experiences in one department of German Studies in the UK, which is the micro-context of the present study, in order to provide an in-depth insight into real-life problems, strengths and weaknesses as they occur in the practice of teaching and learning German. Following Lamb (2004) and Holliday (1994), the present study acts on the assumption that each micro-context does not exist in vacuo but is always embedded in a wider socio-political and education environment, namely the macro-context, which largely determines how and what is taught. The macro-analysis of the present study surveys the socio-political developments that have recently affected the sector of modern languages and specifically the discipline of German Studies in the UK. It demonstrates the impact they have had on teaching and learning German at the undergraduate level in Britain. This context is interesting inasmuch as the situation in Britain is to a large extent a paradigmatic example of the developments in German Studies in English-speaking countries. Subsequently, the present study explores learning experiences of a group of thirty-five first year students. It focuses on their previous experiences in learning German, exposure to the target language, motivation, learning strategies and difficulties encountered, when learning German at the tertiary level. Then, on the basis of interviews with five lecturers of German, teaching experience in the context under study is explored, problems and successful teaching strategies discussed.

Argument structure and specific language impairment: retrospect and prospect

This article reflects a collaboration between the Universities of Groningen and Reading of which Frans Zwarts was the promoter. One of the outcomes was a close attention to the learning of various aspects of argument structure by children with specific language impairment (SLI) in Dutch and English. At that time and since, the focus on deficits in grammatical morphology in these children has left verb complementation as something of a syntactic Cinderella. Here we review the findings from our studies in the 1990s. We confirm that children with SLI in both languages have problems with verb specificity, with argument structure alternations and with resultative verb predicates. The very limited number of subsequent studies on verb syntax appear to support our findings. We conclude that this is an area which will repay further scrutiny – it is high time argument structure received an invitation to the ball.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

The use of explicit algorithms and episodic context to teach subtraction to students with learning problems in mathematics /

This research attempted to address the question of the role of explicit algorithms and episodic contexts in the acquisition of computational procedures for regrouping in subtraction. Three groups of students having difficulty learning to subtract with regrouping were taught procedures for doing so through either an explicit algorithm, an episodic content or an examples approach. It was hypothesized that the use of an explicit algorithm represented in a flow chart format would facilitate the acquisition and retention of specific procedural steps relative to the other two conditions. On the other hand, the use of paragraph stories to create episodic content was expected to facilitate the retrieval of algorithms, particularly in a mixed presentation format. The subjects were tested on similar, near, and far transfer questions over a four-day period. Near and far transfer algorithms were also introduced on Day Two. The results suggested that both explicit and episodic context facilitate performance on questions requiring subtraction with regrouping. However, the differential effects of these two approaches on near and far transfer questions were not as easy to identify. Explicit algorithms may facilitate the acquisition of specific procedural steps while at the same time inhibiting the application of such steps to transfer questions. Similarly, the value of episodic context in cuing the retrieval of an algorithm may be limited by the ability of a subject to identify and classify a new question as an exemplar of a particular episodically deflned problem type or category. The implications of these findings in relation to the procedures employed in the teaching of Mathematics to students with learning problems are discussed in detail.

Non-word repetition in adolescents with Specific Language Impairment and Autism plus Language Impairments: a qualitative analysis

Non-word repetition (NWR) was investigated in adolescents with typical development, Specific Language Impairment (SLI) and Autism Plus language Impairment (ALI) (n = 17, 13, 16, and mean age 14;4, 15;4, 14;8 respectively). The study evaluated the hypothesis that poor NWR performance in both groups indicates an overlapping language phenotype (Kjelgaard & Tager-Flusberg, 2001). Performance was investigated both quantitatively, e.g. overall error rates, and qualitatively, e.g. effect of length on repetition, proportion of errors affecting phonological structure, and proportion of consonant substitutions involving manner changes. Findings were consistent with previous research (Whitehouse, Barry, & Bishop, 2008) demonstrating a greater effect of length in the SLI group than the ALI group, which may be due to greater short-term memory limitations. In addition, an automated count of phoneme errors identified poorer performance in the SLI group than the ALI group. These findings indicate differences in the language profiles of individuals with SLI and ALI, but do not rule out a partial overlap. Errors affecting phonological structure were relatively frequent, accounting for around 40% of phonemic errors, but less frequent than straight Consonant-for-Consonant or vowel-for-vowel substitutions. It is proposed that these two different types of errors may reflect separate contributory mechanisms. Around 50% of consonant substitutions in the clinical groups involved manner changes, suggesting poor auditory-perceptual encoding. From a clinical perspective algorithms which automatically count phoneme errors may enhance sensitivity of NWR as a diagnostic marker of language impairment. Learning outcomes: Readers will be able to (1) describe and evaluate the hypothesis that there is a phenotypic overlap between SLI and Autism Spectrum Disorders (2) describe differences in the NWR performance of adolescents with SLI and ALI, and discuss whether these differences support or refute the phenotypic overlap hypothesis, and (3) understand how computational algorithms such as the Levenshtein Distance may be used to analyse NWR data.

A sociocultural analysis of language learning: New forms of literacy practices in a language and culture awareness programme

This paper reports on a sociocultural study conducted in a Catholic primary school in the Australian outback and provides insights into how policy related to Languages Other Than English (LOTE) programmes is implemented in a specific location and interwoven within the literacy practices of children, parents and teachers. A case study that tracked a Year Four student's learning and development during a Language and Culture Awareness Programme is discussed within a discourse of cultural and linguistic practices. Significant aspects of the student's learning related to a phenomenon called multi-tiered scaffolding temporarily disrupted the established literacy practices in the school community. Implications of the research for second-language teaching and learning in Australian primary schools are elaborated.

Perception of Filtered Speech by Children with Developmental Dyslexia and Children with Specific Language Impairments

Here we use two filtered speech tasks to investigate children’s processing of slow (<4 Hz) versus faster (∼33 Hz) temporal modulations in speech. We compare groups of children with either developmental dyslexia (Experiment 1) or speech and language impairments (SLIs, Experiment 2) to groups of typically-developing (TD) children age-matched to each disorder group. Ten nursery rhymes were filtered so that their modulation frequencies were either low-pass filtered (<4 Hz) or band-pass filtered (22 – 40 Hz). Recognition of the filtered nursery rhymes was tested in a picture recognition multiple choice paradigm. Children with dyslexia aged 10 years showed equivalent recognition overall to TD controls for both the low-pass and band-pass filtered stimuli, but showed significantly impaired acoustic learning during the experiment from low-pass filtered targets. Children with oral SLIs aged 9 years showed significantly poorer recognition of band pass filtered targets compared to their TD controls, and showed comparable acoustic learning effects to TD children during the experiment. The SLI samples were also divided into children with and without phonological difficulties. The children with both SLI and phonological difficulties were impaired in recognizing both kinds of filtered speech. These data are suggestive of impaired temporal sampling of the speech signal at different modulation rates by children with different kinds of developmental language disorder. Both SLI and dyslexic samples showed impaired discrimination of amplitude rise times. Implications of these findings for a temporal sampling framework for understanding developmental language disorders are discussed.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.