Preservice Teachers’ Algebraic Reasoning and Symbol Use on a Multistep Fraction Word Problem

Previous research on preservice teachers’ understanding of fractions and algebra has focused on one or the other. To extend this research, we examined 85 undergraduate elementary education majors and middle school mathematics education majors’ solutions and solution paths (i.e., the ways or methods in which preservice teachers solve word problems) when combining fractions with algebra on a multistep word problem. In this article, we identify and describe common strategy clusters and approaches present in the preservice teachers’ written work. Our results indicate that preservice teachers’ understanding of algebra include arithmetic methods, proportions, and is related to their understanding of a whole

Gaining the voices of young people using Person Centred Planning: Exploring ways to engage young people with Additional Learning Needs in making decisions about their future.

Educational legislation in the UK has promoted the use of Person Centred Planning to gain the views of young people and involve them in making decisions about their provision (DFE, 2014; Welsh Government, 2015). However, there is a gap between the legislation and current practice in education (Holtom & Lloyd-Jones, 2014; Norwich & Eaton, 2015). Previous research has found young people are not meaningfully engaged in making decisions, particularly young people with additional learning needs (Lundy, 2007; Norwich & Eaton, 2015). However, the extent to which Person Centred Planning facilitates the engagement of young people in decision making has not yet been explored and the evidence base is limited (Ratti et al, 2016). This research paper aimed to explore participant’s perceptions of Person Centred Planning as a tool to engage young people. Semi- Structured Interviews were used to explore the experiences of young people, parents, school staff and Educational Psychologists in relation to Person Centred Planning meetings, focusing specifically on the engagement of young people in the process. A thematic analysis of the whole data set found four common themes across the data, these were ‘power’, ‘familiarity’, ‘presence of young person’ and ‘creativity and adaptation’, however the experiences of each participant group varied. Young people’s engagement in decision-making processes was limited, due to a lack of familiarity with the approach and established power hierarchies. The findings also highlighted the difficulty of applying one approach to a heterogeneous group such as young people with additional learning needs

Preservice teachers’ pictorial strategies for a multistep multiplicative fraction problem

Previous research has documented that preservice teachers (PSTs) struggle with under- standing fraction concepts and operations, and misconceptions often stem from their understanding of the referent whole. This study expands research on PSTs’ understanding of wholes by investigating pictorial strategies that 85 PSTs constructed for a multistep fraction task in a multiplicative context. The results show that many PSTs were able to construct valid pictorial strategies, and the strategies were widely diverse with respect to how they made sense of an unknown referent whole of a fraction in multiple steps, how they represented the wholes in their drawings, in which order they did multiple steps, and which type of model they used (area or set). Based on their wide range of pictorial strategies, we discuss potential benefits of PSTs’ construction of their own representations for a word problem in developing problem solving skills

Biomarker Discovery in Animal Health and Disease: The Application of Post-Genomic Technologies

The causes of many important diseases in animals are complex and multifactorial, which present unique challenges. Biomarkers indicate the presence or extent of a biological process, which is directly linked to the clinical manifestations and outcome of a particular disease. Identifying biomarkers or biomarker profiles will be an important step towards disease characterization and management of disease in animals. The emergence of post-genomic technologies has led to the development of strategies aimed at identifying specific and sensitive biomarkers from the thousands of molecules present in a tissue or biological fluid. This review will summarize the current developments in biomarker discovery and will focus on the role of transcriptomics, proteomics and metabolomics in biomarker discovery for animal health and disease

A phylomedicine approach to understanding the evolution of auditory sensory perception and disease in mammals

Hereditary deafness affects 0.1% of individuals globally and is considered as one of the most debilitating diseases of man. Despite recent advances, the molecular basis of normal auditory function is not fully understood and little is known about the contribution of single-nucleotide variations to the disease. Using cross-species comparisons of 11 'deafness' genes (Myo15, Ush1g, Strc, Tecta, Tectb, Otog, Col11a2, Gjb2, Cldn14, Kcnq4, Pou3f4) across 69 evolutionary and ecologically divergent mammals, we elucidated whether there was evidence for: (i) adaptive evolution acting on these genes across mammals with similar hearing capabilities; and, (ii) regions of long-term evolutionary conservation within which we predict disease-associated mutations should occur. We find evidence of adaptive evolution acting on the eutherian mammals in Myo15, Otog and Tecta. Examination of selection pressures in Tecta and Pou3f4 across a taxonomic sample that included a wide representation of auditory specialists, the bats, did not uncover any evidence for a role in echolocation. We generated ‘conservation indices' based on selection estimates at nucleotide sites and found that known disease mutations fall within sites of high evolutionary conservation. We suggest that methods such as this, derived from estimates of evolutionary conservation using phylogenetically divergent taxa, will help to differentiate between deleterious and benign mutations

Direct infusion mass spectrometry metabolomics dataset : a benchmark for data processing and quality control

Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment

Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phos-pholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites

Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phos-pholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites