Time travelling animated program executions

Visualizations of program executions are often generated on the fly. This has many advantages relative to off-line generation of animated video files. Video files, however, trivially support flexible viewing via controls that include reverse and fast forward. Here we report on an implementation of time travel that combines the best of both techniques. In ToonTalk both the construction and execution of programs are animated. Time travel enables the user to move back in time and replay animated executions. The replay can be paused and the user can skip forward or further back in time. The implementation of time travel is based records of every input event and periodic snapshots of the state of the computation

Designing to see and share structure in number sequences

This paper reports on a design experiment in the domain of number sequences conducted in the course of the WebLabs project. We iteratively designed and tested a set of activities and tools in which 10-14 year old students used the ToonTalk programming environment to construct models of sequences and series, and then shared their models and their observations about them utilising a webbased collaboration system. We report on the evolution of a design pattern (programming method) called ‘Streams’ which enables students to engage in the process of summing and ‘hold the series in their hand’, and consequently make sophisticated arguments regarding the mathematical structures of the sequences without requiring the use of algebra. While the focus of this paper is mainly on the design of activities, and in particular their epistemological foundations, some illustrative examples of one group of students’ work indicate the potential of the activities and tools for expressing and reflecting on deep mathematical ideas

Scalable monitoring of student interaction indicators in exploratory learning environments

We present and evaluate a web-based architecture for monitoring student-system interaction indicators in Exploratory Learning Environments (ELEs), using as our case study a microworld for secondary school algebra. We discuss the challenging role of teachers in exploratory learning settings and motivate the need for visualisation and notification tools that can assist teachers in focusing their attention across the class and inform teachers' interventions. We present an architecture that can support such Teacher Assistance tools and demonstrate its scalability to allow concurrent usage by thousands of users (students and teachers)

Thinking in progress

Relates the story of 11-14 year-old students using a programming system to represent and discuss some mathematical ideas in Great Britain. Programming with the Toontalk language; Tools developed within the programming system; Use of programmed models as elements of students' discourse

Settlement Chronologies and Shifting Resource Exploitation in Ka‘ū District, Hawaiian Islands

Museum collections contribute valuable information for cultural heritage, biological conservation, and the application of innovative and new methodological approaches. Collections deriving from archaeological projects in Hawai‘i serve as a case in point. Here, we report on re-analysis of two Ka‘ū District collections from Hawai‘i Island (HA-B22-64 and -248) to demonstrate what can be learned when applying new research questions to old collections. Our research goals center on two main themes: re-dating the HA-B22-64 and -248 sites to place them within the newly refined Hawaiian archipelago settlement chronology; and using diverse data sources to look at changing resource use in pre-Contact Hawai‘i through time. Our new AMS dating results indicate that the lower levels of rockshelter HA-B22-64 date to the mid- to Late Prehistoric period during the fifteenth and seventeenth centuries, while upper levels calibrate to the ninteenth century. Both levels of HA-B22-248 calibrate to the late eighteenth to nineteenth centuries. In terms of resource use, Pu‘u Wa‘awa‘a volcanic glass is present at both sites in small amounts, which is consistent with other sites in the South Point area. However, the high percentage of Group 3 volcanic glass is unusual for the area, and represents the highest percentage for the Kona side of Hawai‘i Island. HA-B22-64 has a small number of basalt artifacts consistent with the Keahua I source on Kaua‘i, while both sites have evidence for artifacts produced from the Mauna Kea quarry. Technological data from our basalt assemblages do not support direct access to the Mauna Kea quarry nor the presence of adze specialists in Ka‘ū households; rather, we find rejuvenation and use of already finished adzes. Measurements on Scarine oral and pharyngeal jawbones illustrate a consistent and stable size structure of fish populations at both sites. This, along with the large overall fish size, is indicative of sustainable fishing practices

The role for simulations in theory construction for the social sciences:Case studies concerning Divergent Modes of Religiosity

Religion is, at the very least, a highly complex social phenomenon. The theories we use to understand religion – and sociocultural systems more generally – are often so complex that even experts in the field may not be able to see all their consequences. Social simulations can help us understand and communicate the consequences of a theory, provided we can describe the theory with sufficient precision and comprehensiveness in order to run it on a computer. In this article we demonstrate the benefits of simulating the predictions of a well-known theory in the Cognitive Science of Religion, the theory of Divergent Modes of Religiosity. Many of these predictions have already been tested against contemporary and longitudinal evidence, using the methods of both qualitative case study and large-scale survey, and some of the mechanisms responsible for the patterns observed have been investigated by means of controlled experiments. Nevertheless, in simulating the patterns of religious transmission and transformation predicted by the modes theory we discovered numerous aspects that were underspecified, generating new hypotheses for investigation in future empirical research. This back-and-forth between simulation and theory testing has the potential to accelerate progress in the scientific study of religion

Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice

Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b[superscript −/−]) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b[superscript −/−] mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b[superscript −/−] mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation–reduction in altered PUFA and triglyceride metabolism.Pfizer Inc. (grant)National Institutes of Health (U.S.) (grant 5R24DK090963)National Institutes of Health (U.S.) (grant U54-CA112967)National Institutes of Health (U.S.) (grant CA49152 R37)National Institutes of Health (U.S.) (grant R01-DK080756)National Mouse Metabolic Phenotyping Center at UMASS (Grant (U24-DK093000))National Science Foundation (U.S.) (Graduate Research Fellowship

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy