Dialogic literacy: Talking, reading and writing among primary school children

This study investigates the interplay between talk, reading and writing as 6th grade Mexican primary school children worked together, in small groups, on a psycholinguistic task that required them to read three related texts and then write an integrative summary. The study was conducted in the context of an educational program called ‘Learning Together’ (LT), which uses collaborative learning to enhance the development of children's oracy and literacy. Analyses of children's dialogues using the Ethnography of Communication in combination with a novel ‘Scheme for Educational Dialogue Analysis (SEDA)’ (Hennessy et al., 2016), revealed important improvements in effective oral communication - and specifically a shift towards the use of dialogic styles of interaction - between the children who participated in the LT program (as compared to those who did not). These improvements were accompanied by significantly higher quality integrative summaries, not only when children worked in small groups but also individually. The latter results indicate appropriation of sophisticated literacy abilities by the children. Further analyses of the relations among talk, reading and writing suggest that these processes are interwoven through subtle intertextual relations and support each other in a dynamic and iterative manner. We discuss the theoretical, methodological and practical relevance of the study.The work reported in this paper was supported by the Dirección General de Asuntos del Personal Académico of the National Autonomous University of Mexico (UNAM) (DGAPA-UNAM) (PAPIIT Project Number: IN303716). Professor Rojas-Drummond would like to thank the Faculty of Education at the University of Cambridge, UK for hosting her as Visiting Scholar while part of this manuscript was being prepared. Her visiting Scholarship was funded by the National Council of Science and Technology in Mexico (CONACYT Project Number: 160873). In addition, some of the methodological tools used in the study reported derived from a collaborative work carried out for a project entitled ‘A Tool for Analysing Dialogic Interactions in Classrooms’ (http://tinyurl.com/BAdialogue) funded through the British Academy International Partnership and Mobility Scheme (ref. RG66509), between January 2013–December 2015

Transcriptional responses to glucose in Saccharomyces cerevisiae strains lacking a functional protein kinase A

Background The pattern of gene transcripts in the yeast Saccharomyces cerevisiae is strongly affected by the presence of glucose. An increased activity of protein kinase A (PKA), triggered by a rise in the intracellular concentration of cAMP, can account for many of the effects of glucose on transcription. In S. cerevisiae three genes, TPK1, TPK2, and TPK3, encode catalytic subunits of PKA. The lack of viability of tpk1 tpk2 tpk3 triple mutants may be suppressed by mutations such as yak1 or msn2/msn4. To investigate the requirement for PKA in glucose control of gene expression, we have compared the effects of glucose on global transcription in a wild-type strain and in two strains devoid of PKA activity, tpk1 tpk2 tpk3 yak1 and tpk1 tpk2 tpk3 msn2 msn4. Results We have identified different classes of genes that can be induced -or repressed- by glucose in the absence of PKA. Representative examples are genes required for glucose utilization and genes involved in the metabolism of other carbon sources, respectively. Among the genes responding to glucose in strains devoid of PKA some are also controlled by a redundant signalling pathway involving PKA activation, while others are not affected when PKA is activated through an increase in cAMP concentration. On the other hand, among genes that do not respond to glucose in the absence of PKA, some give a full response to increased cAMP levels, even in the absence of glucose, while others appear to require the cooperation of different signalling pathways. We show also that, for a number of genes controlled by glucose through a PKA-dependent pathway, the changes in mRNA levels are transient. We found that, in cells grown in gluconeogenic conditions, expression of a small number of genes, mainly connected with the response to stress, is reduced in the strains lacking PKA. Conclusions In S. cerevisiae, the transcriptional responses to glucose are triggered by a variety of pathways, alone or in combination, in which PKA is often involved. Redundant signalling pathways confer a greater robustness to the response to glucose, while cooperative pathways provide a greater flexibility.BT/BiotechnologyApplied Science

Octopus vulgaris. Paralarval Culture

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Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico

BACKGROUND: The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods. METHODS: We analyzed electronic health records and laboratory databases from the year 2012 to 2016 of T2D patients from six family medicine clinics (FMCs) delivering the DIABETIMSS program, and five FMCs providing routine care. All FMCs belong to the Mexican Institute of Social Security and are in Mexico City and the State of Mexico. The primary outcome was glycemic control. The study covariates included: patient sex, age, anthropometric data, history of glycemic control, diabetic complications and comorbidity. We measured the effects of DIABETIMSS program through 1) simple unadjusted mean differences; 2) adjusted via standard logistic regression and 3) adjusted via targeted machine learning. We treated the data as a serial cross-sectional study, conducted a standard principal components analysis to explore the distribution of covariates among clinics, and performed regression tree on data transformed to use the prediction model to identify patient sub-groups in whom the program was most successful. To explore the robustness of the machine learning approaches, we conducted a set of simulations and the sensitivity analysis with process-of-care indicators as possible confounders. RESULTS: The study included 78,894 T2D patients, from which 37,767patients received care through DIABETIMSS. The impact of DIABETIMSS ranged, among clinics, from 2 to 8% improvement in glycemic control, with an overall (pooled) estimate of 5% improvement. T2D patients with fewer complications have more significant benefit from DIABETIMSS than those with more complications. At the FMCs delivering the conventional model the predicted impacts were like what was observed empirically in the DIABETIMSS clinics. The sensitivity analysis did not change the overall estimate average across clinics. CONCLUSIONS: DIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers