A normative study of students selected at random on the H.M.H. plus and minus binocular rock test

A normative study of students selected at random on the H.M.H. plus and minus binocular rock tes

Gene expression profiling of human myeloid leukemic MV4-11 cells treated with 5-Aza-2’-deoxycytidine

The pyrimidine analog, 5-aza-2’-deoxycytidine (5-aza-dC) is a DNA methyltransferase inhibitor that triggers DNA demethylation leading to the reactivation of epigenetically silenced tumor suppressor genes. To understand the shift in gene expression which mediates the beneficial 5-aza-dC effects in leukemia, we have treated human myeloid derived leukemic cells with 5-aza-dC. Target genes were identified first in MV4-11 cells using a genome-wide gene expression profiling assay to detect differences in treated and untreated cells. From this analysis six genes were identified (HOXA4, HOXD4, HOXA8, HOXD12, CD9 and RGS2) as being significantly different expressed after treatment. To validate microarray data, we performed quantitative PCR on these genes from multiple leukemic cells. The results suggest that these genes are epigenetically regulated indicating that dysregulation of HOXA4, HOXD4, HOXA8, HOXD12, CD9 and RGS2 expression may play an important role in establishing the malignant phenotype in AML

Mycobacterium tuberculosis Transmission between Cluster Members with Similar Fingerprint Patterns

Molecular epidemiologic studies provide evidence of transmission of Mycobacterium tuberculosis within clusters of patients whose isolates share identical IS6110-DNA fingerprint patterns. However, M. tuberculosis transmission among patients whose isolates have similar but not identical DNA fingerprint patterns (i.e., differing by a single band) has not been well documented. We used DNA fingerprinting, combined with conventional epidemiology, to show unsuspected patterns of tuberculosis transmission associated with three public bars in the same city. Among clustered TB cases, DNA fingerprinting analysis of isolates with similar and identical fingerprints helped us discover epidemiologic links missed during routine tuberculosis contact investigations

Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds

We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement learning to model the confidence and consistency of human feedback. This enables deep reinforcement learning algorithms to determine the most appropriate time to listen to the human feedback, exploit the current policy model, or explore the agent's environment. Managing the trade-off between these three strategies allows DRL agents to be robust to inconsistent or intermittent human feedback. Through experimentation using a synthetic oracle, we show that our technique improves the training speed and overall performance of deep reinforcement learning in navigating three-dimensional environments using Minecraft. We further show that our technique is robust to highly innacurate human feedback and can also operate when no human feedback is given