Planning to Learn : Creating and Using a Personal Planner with Young People on the Autism Spectrum

Late intervention often means that young people on the autism spectrum appear to act on impulse, seem disorganized, or fail to learn from past experiences. In this practical, effective resource, the authors share tried and tested techniques for creating and using a personal planner to help individuals on the autism spectrum to develop independence. "Planning to Learn" is split into three parts. The first part guides adults in helping young people to make sense of the world and to develop and practise coping strategies for any given situation. The authors also explain how simple visual and verbal cues can help people to cope successfully in stressful situations. The second part provides worksheets for the young person to complete to learn how to use plans in different situations, for example staying calm when waiting for a doctor, or coping with a change in the school timetable. Each individual makes a unique planner with procedures to refer to, such as responding to pressure, calming down, being organised, and being around people. The third part includes useful cards, schedules and plans for photocopying and including in the planner. This illustrated photocopiable workbook is packed with guidance, support and helpful notes for those new to, or experienced in, working with children and young people with ASD. It can be used within educational and community settings or at home.

Efficiency of lexical access in children with autism spectrum disorders : does modality matter?

The provision of visual support to individuals with an autism spectrum disorder (ASD) is widely recommended. We explored one mechanism underlying the use of visual supports: efficiency of language processing. Two groups of children, one with and one without an ASD, participated. The groups had comparable oral and written language skills and nonverbal cognitive abilities. In two semantic priming experiments, prime modality and prime–target relatedness were manipulated. Response time and accuracy of lexical decisions on the spoken word targets were measured. In the first uni-modal experiment, both groups demonstrated significant priming effects. In the second experiment which was cross-modal, no effect for relatedness or group was found. This result is considered in the light of the attentional capacity required for access to the lexicon via written stimuli within the developing semantic system. These preliminary findings are also considered with respect to the use of visual support for children with ASD.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Nano-scale morphology and electron spectrum of defect states in low-k SiOCH films

Results of experimental investigations on the relationship between nanoscale morphology of carbon doped hydrogenated silicon-oxide (SiOCH) low-k films and their electron spectrum of defect states are presented. The SiOCH films have been deposited using trimethylsilane (3MS) - oxygen mixture in a 13.56 MHz plasma enhanced chemical vapor deposition (PECVD) system at variable RF power densities (from 1.3 to 2.6 W/cm2) and gas pressures of 3, 4, and 5 Torr. The atomic structure of the SiOCH films is a mixture of amorphous-nanocrystalline SiO2-like and SiC-like phases. Results of the FTIR spectroscopy and atomic force microscopy suggest that the volume fraction of the SiC-like phase increases from ∼0.2 to 0.4 with RF power. The average size of the nanoscale surface morphology elements of the SiO2-like matrix can be controlled by the RF power density and source gas flow rates. Electron density of the defect states N(E) of the SiOCH films has been investigated with the DLTS technique in the energy range up to 0.6 eV from the bottom of the conduction band. Distinct N(E) peaks at 0.25 - 0.35 eV and 0.42 - 0.52 eV below the conduction band bottom have been observed. The first N(E) peak is identified as originated from E1-like centers in the SiC-like phase. The volume density of the defects can vary from 1011 - 1017 cm-3 depending on specific conditions of the PECVD process.

Detection of dynamic primary user with cooperative spectrum sensing

In this paper we demonstrate that existing cooperative spectrum sensing formulated for static primary users cannot accurately detect dynamic primary users regardless of the information fusion method. Performance error occurs as the sensing parameters calculated by the conventional detector result in sensing performance that violates the sensing requirements. Furthermore, the error is accumulated and compounded by the number of cooperating nodes. To address this limitation, we design and implement the duty cycle detection model for the context of cooperative spectrum sensing to accurately calculate the sensing parameters that satisfy the sensing requirements. We show that longer sensing duration is required to compensate for dynamic primary user traffic.

Solid-state MAS NMR measurements of intact articular bovine cartilage

Articular cartilage (AC), an avascular connective tissue lining articulating surfaces of the long bones, comprises extracellular biopolymers. In functionally compromised states such as osteoarthritis, thinned or lost AC causes reduced mobility and increased health-care costs. Understanding of the characteristics responsible for the load bearing efficiency of AC and the factors leading to its degradation are incomplete. DTI shows the structural alignment of collagen in AC [1] and T2 relaxation measurements suggest that the average director of reorientational motion of water molecules depends on the degree of alignment of collagen in AC [2]. Information on the nature of the chemical interactions involved in functional AC is lacking. The need for AC structural integrity makes solid state NMR an ideal tool to study this tissue. We examined the contribution of water in different functional ‘compartments’ using 1H-MAS, 13C-MAS and 13C-CPMAS NMR of bovine patellar cartilage incubated in D2O. 1H-MAS spectra signal intensity was reduced due to H/D exchange without a measureable redistribution of relative signal intensity. Chemical shift anisotropy was estimated by lineshape analysis of multiple peaks in the 1H-MAS spinning sidebands. These asymmetrical sidebands suggested the presence of multiple water species in AC. Therefore, water was added in small aliquots to D2O saturated AC and the influence of H2O and D2O on organic components was studied with 13C-MAS-NMR and 13C-CPMAS-NMR. Signal intensity in 13C-MAS spectra showed no change in relative signal intensity throughout the spectrum. In 13C-CPMAS spectra, displacement of water by D2O resulted in a loss of signal in the aliphatic region due to a reduction in proton availability for cross-polarization. These results complement dehydration studies of cartilage using osmotic manipulation [3] and demonstrate components of cartilage that are in contact with mobile water.

Emotion recognition of static and dynamic faces in autism spectrum disorder

There is substantial evidence for facial emotion recognition (FER) deficits in autism spectrum disorder (ASD). The extent of this impairment, however, remains unclear, and there is some suggestion that clinical groups might benefit from the use of dynamic rather than static images. High-functioning individuals with ASD (n = 36) and typically developing controls (n = 36) completed a computerised FER task involving static and dynamic expressions of the six basic emotions. The ASD group showed poorer overall performance in identifying anger and disgust and were disadvantaged by dynamic (relative to static) stimuli when presented with sad expressions. Among both groups, however, dynamic stimuli appeared to improve recognition of anger. This research provides further evidence of specific impairment in the recognition of negative emotions in ASD, but argues against any broad advantages associated with the use of dynamic displays.

Analysis of ancient pottery and ceramic objects using X-ray fluorescence spectrometry

Archaeology has been called 'the science of the artefact' and nothing demonstrates this point better than the current interest displayed in provenance studies of archaeological objects. In theory, every vessel carries a chemical compositional pattern or 'fingerprint' identical with the clay from which it was made and this relationship is basic to provenance studies. The reasoning behind provenance or sourcing studies is to probe into this past and attempt to re-create prehistory by obtaining information on exchange and social interaction. This paper discusses the use of XRF spectrometry for the analysis of ancient pottery and ceramics to examine whether it is possible to predict prehictoric cultural exchanges.

Enhancing MRI of prostate cancer using PSMA-targeting iron oxide magnetic nanoparticles

Introduction Novel imaging techniques for prostate cancer (PCa) are required to improve staging and real-time assessment of therapeutic response. We performed preclinical evaluation of newly-developed, biocompatible magnetic nanoparticles (MNPs) conjugated with J591, an antibody specific for prostate specific membrane antigen (PSMA), to enhance magnetic resonance imaging (MRI) of PCa. PSMA is expressed on ∼90% of PCa, including those that are castrate-resistant, rendering it as a rational target for PCa imaging. Materials and Methods The specificity of J591 for PSMA was confirmed by flow cytometric analysis of several PCa cell lines of known PSMA status. MNPs were prepared, engineered to the appropriate size, labeled with DiR fluorophore, and their toxicity to a panel of PC cells was assessed by in vitro Alamar Blue assay. Immunohistochemistry, fluorescence microscopy and Prussian Blue staining (iron uptake) were used to evaluate PSMA specificity of J591-MNP conjugates. In vivo MRI studies (16.4T MRI system) were performed using live immunodeficient mice bearing orthotopic LNCaP xenografts and injected intravenously with J591-MNPs or MNPs alone. Results MNPs were non-toxic to PCa cells. J591-MNP conjugates showed no compromise in specificity of binding to PSMA+ cells and showed enhanced iron uptake compared with MNPs alone. In vivo, tumour targeting (significant MR image contrast) was evident in mice injected with J591-MNPs, but not MNPs alone. Resected tumours from targeted mice had an accumulation of MNPs, not seen in normal control prostate. Conclusions Application of PSMA-targeting MNPs into conventional MRI has potential to enhance PCa detection and localization in real-time, improving patient management.