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  1. The 2nd Annual WPI-UMASS-UPENN EDM Data Min- ing Challenge required contestants to predict efficient test taking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized Partial Credit Model for estimating latent person ability. Additionally, we adopted an n-gram feature approach for event sequences. For training a multi-label classifier, we distinguished inefficent test takers who were going too fast and those who were going too slow, instead of using the provided binary target label. Our best-performing ensemble classify er comprised three sets of low-dimensional classi ers, dominated by test-taker speed. While our classi- er reached moderate performance, relative to competition leaderboard, our approach makes two important contributions. First, we show how explainable classi ers could provide meaningful predictions if results can be contextualized to test administrators who wish to intervene or take action. Second, our re-engineering of test scores enabled us to incorporate person ability into the estimation. However, ability was hardly predictive of efficient behavior, leading to the conclusion that the target label's validity needs to be questioned. The paper concludes with tools that are helpfulmore »for substantively meaningful log data mining.« less
  2. Transplantation of human embryonic stem cell (hESC)-derived neural progenitors is a potential treatment for neurological disorders, but relatively little is known about the time course for human neuron maturation after transplantation and the emergence of morphological and electrophysiological properties. To address this gap, we transplanted hESC-derived human GABAergic interneuron progenitors into the mouse hippocampus, and then characterized their electrophysiological properties and dendritic arborizations after transplantation by means of ex vivo whole-cell patch clamp recording, followed by biocytin staining, confocal imaging and neuron reconstruction software. We asked whether particular electrophysiological and morphological properties showed maturation-dependent changes after transplantation. We also investigated whether the emergence of particular electrophysiological properties were linked to increased complexity of the dendritic arbors. Human neurons were classified into five distinct neuronal types (Type I-V), ranging from immature to mature fastspiking interneurons. Hierarchical clustering of the dendritic morphology and Sholl analyses suggested four morphologically distinct classes (Class A-D), ranging from simple/immature to highly complex. Incorporating all of our data regardless of neuronal classification, we investigated whether any electrophysiological and morphological features correlated with time post-transplantation. This analysis demonstrated that both dendritic arbors and electrophysiological properties matured after transplantation.
  3. GABAergic interneuron dysfunction has been implicated in temporal lobe epilepsy (TLE), autism, and schizophrenia. Inhibitory interneuron progenitors transplanted into the hippocampus of rodents with TLE provide varying degrees of seizure suppression. We investigated whether human embryonic stem cell (hESC)-derived interneuron progenitors (hESNPs) could differentiate, correct hippocampal-dependent spatial memory deficits, and suppress seizures in a pilocarpine-induced TLE mouse model. We found that transplanted ventralized hESNPs differentiated into mature GABAergic interneurons and became electrophysiologically active with mature firing patterns. Some mice developed hESNP-derived tumor-like NSC clusters. Mice with transplants showed significant improvement in the Morris water maze test, but transplants did not suppress seizures. The limited effects of the human GABAergic interneuron progenitor grafts may be due to cell type heterogeneity within the transplants.