Dataset Information

The Use of Modular, Electronic Neuron Simulators for Neural Circuit Construction Produces Learning Gains in an Undergraduate Anatomy and Physiology Course.

ABSTRACT: During the spring of 2016 at the University of Wisconsin-Milwaukee, we implemented a novel educational technology designed to teach undergraduates about the nervous system while allowing them to physically construct their own neural circuits. Modular, electronic neuron simulators called NeuroBytes were used by the students in BIOSCI202 Anatomy and Physiology I, a four-credit course consisting of three hours per week each of lecture and laboratory time. 162 students participated in the laboratory sessions that covered reflexes; 83 in the experimental sections used the NeuroBytes to build a model of the patellar tendon reflex, while 79 in the control sections participated in alternate reflex curricula. To address the question of whether or not the NeuroBytes-based patellar tendon reflex simulation brought about learning gains, the control and experimental group students underwent pre/post testing before and after their laboratory sections. We found that for several of the neuroscience and physiology concepts assessed on the test, the experimental group students had significantly greater declarative learning gains between the pre- and post-test as compared to the control group students. While there are numerous virtual neuroscience education tools available to undergraduate educators, there are relatively few designed to engage students in the basics of electrophysiology and neural circuitry using physical manipulatives, and none to our knowledge that allow them to build circuits from functioning hand-held "neurons."

SUBMITTER: Petto A

PROVIDER: S-EPMC5480844 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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The Use of Modular, Electronic Neuron Simulators for Neural Circuit Construction Produces Learning Gains in an Undergraduate Anatomy and Physiology Course.

Petto Andrew A Fredin Zachary Z Burdo Joseph J

Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience 20170615 2

During the spring of 2016 at the University of Wisconsin-Milwaukee, we implemented a novel educational technology designed to teach undergraduates about the nervous system while allowing them to physically construct their own neural circuits. Modular, electronic neuron simulators called NeuroBytes were used by the students in BIOSCI202 Anatomy and Physiology I, a four-credit course consisting of three hours per week each of lecture and laboratory time. 162 students participated in the laboratory ...[more]

PMID: 28690437

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Project description:We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the “clustering behavior” of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is “data-driven” and does not involve manual selection of genes by the investigator. We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules.

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Dataset Information

The Use of Modular, Electronic Neuron Simulators for Neural Circuit Construction Produces Learning Gains in an Undergraduate Anatomy and Physiology Course.

Publications

The Use of Modular, Electronic Neuron Simulators for Neural Circuit Construction Produces Learning Gains in an Undergraduate Anatomy and Physiology Course.

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