Modeling sporadic amyotrophic lateral sclerosis (sALS) has been a challenge since there is no known single gene mutation that triggers disease pathogenesis. Although human induced pluripotent stem cells (hiPSCs) have created new opportunities in studying sALS, they do not retain important age associated phenotypic markers due to the rejuvenation stage that takes place during the reprogramming of somatic cells into hiPSCs. To overcome this obstacle, we performed an alternative method of direct neuronal conversion from patient fibroblasts that utilizes two transcription factors, Ngn2 and Ascl1. These transcription factors were sufficient to initiate direct neuronal conversion and produce induced neurons (iNeurons). Through the positive staining of neuronal markers Map2, Synapsin-1, and Human Nuclear Marker we found that induced neurons do display neuronal features that are seen in mature neurons.

Data and the use of data to make educational decisions have attained new-found prominence in K-12 education following the inception of high-stakes testing and subsequent linking of teacher evaluations and teacher-performance pay to students' outcomes on standardized assessments. Although the research literature suggested students' academic performance benefits were derived from employing data-informed decision making (DIDM), many educators have not felt efficacious about implementing and using DIDM practices. Additionally, the literature suggested a five-factor model of teachers' efficacy and anxiety with respect to using DIDM practices: (a) identification of relevant information, (b) interpretation of relevant information, (c) application of interpretations of data to their classroom practices, (d) requisite technological skills, and (e) comfort with data and statistics.

This action research study was designed to augment a program of support focused on DIDM, which was being offered at a K-8 charter school in Arizona. It sought to better understand the relation between participation in professional development (PD) modules and teachers' self-efficacy for using DIDM practices. It provided an online PD component, in which 19 kindergarten through 8th-grade teachers worked through three self-guided online learning modules, focused sequentially on (a) identification of relevant student data, (b) interpretation of relevant student data, and (c) application of interpretations of data to classroom practices. Each module concluded with an in-person reflection session, in which teachers shared artifacts they developed based on the modules, discussed challenges, shared solutions, and considered applications to their classrooms.

Results of quantitative data from pre- and post-intervention assessments, suggested the intervention positively influenced participants' self-efficacy for (a) identifying and (b) interpreting relevant student data. Qualitative results from eight semi-structured interviews conducted at the conclusion of the intervention indicated that teachers, regardless of previous experience using data, viewed DIDM favorably and were more able to find and draw conclusions from their data than they were prior to the intervention. The quantitative and qualitative data exhibited complementarity pointing to the same conclusions. The discussion focused on explaining how the intervention influenced participants' self-efficacy for using DIDM practices, anxiety around using DIDM practices, and use of DIDM practices.