4,430 research outputs found
IMAGINE-ing interprofessional education: program evaluation of a novel inner city health educational experience
Background: Poverty is a key determinant of health that leads to poor health outcomes. Although most healthcare providers will work with patients experiencing poverty, surveys among healthcare students have reported a curriculum gap in this area. This study aims to introduce and evaluate a novel, student-run interprofessional inner city health educational program that combines both practical and didactic educational components.Methods: Students participating in the program answered pre- and post-program surveys. Wilcoxon signed-rank tests and descriptive thematic analysis were used for quantitative and qualitative data, respectively.Results: A total of 28 out of 35 participants responded (response rate: 80%). Student knowledge about issues facing underserved populations and resources for underserved populations significantly increased after program participation. Student comfort working with underserved populations also significantly increased after program participation. Valued program elements included workshops, shadowing, and a focus on marginalized populations.Conclusion: Interprofessional inner city health educational programs are beneficial for students to learn about poverty intervention and resources, and may represent a strategy to address a gap in the healthcare professional curriculum
Epstein-Barr Virus Infection of Mammary Epithelial Cells Promotes Malignant Transformation
Whether the human tumor virus, Epstein-Barr Virus (EBV), promotes breast cancer remains controversial and a potential mechanism has remained elusive. Here we show that EBV can infect primary mammary epithelial cells (MECs) that express the receptor CD21. EBV infection leads to the expansion of early MEC progenitor cells with a stem cell phenotype, activates MET signaling and enforces a differentiation block. When MECs were implanted as xenografts, EBV infection cooperated with activated Ras and accelerated the formation of breast cancer. Infection in EBV-related tumors was of a latency type II pattern, similar to nasopharyngeal carcinoma (NPC). A human gene expression signature for MECs infected with EBV, termed EBVness, was associated with high grade, estrogen-receptor-negative status, p53 mutation and poor survival. In 11/33 EBVness-positive tumors, EBV-DNA was detected by fluorescent in situ hybridization for the viral LMP1 and BXLF2 genes. In an analysis of the TCGA breast cancer data EBVness correlated with the presence of the APOBEC mutational signature. We conclude that a contribution of EBV to breast cancer etiology is plausible, through a mechanism in which EBV infection predisposes mammary epithelial cells to malignant transformation, but is no longer required once malignant transformation has occurred
In-Context Analogical Reasoning with Pre-Trained Language Models
Analogical reasoning is a fundamental capacity of human cognition that allows
us to reason abstractly about novel situations by relating them to past
experiences. While it is thought to be essential for robust reasoning in AI
systems, conventional approaches require significant training and/or
hard-coding of domain knowledge to be applied to benchmark tasks. Inspired by
cognitive science research that has found connections between human language
and analogy-making, we explore the use of intuitive language-based abstractions
to support analogy in AI systems. Specifically, we apply large pre-trained
language models (PLMs) to visual Raven's Progressive Matrices (RPM), a common
relational reasoning test. By simply encoding the perceptual features of the
problem into language form, we find that PLMs exhibit a striking capacity for
zero-shot relational reasoning, exceeding human performance and nearing
supervised vision-based methods. We explore different encodings that vary the
level of abstraction over task features, finding that higher-level abstractions
further strengthen PLMs' analogical reasoning. Our detailed analysis reveals
insights on the role of model complexity, in-context learning, and prior
knowledge in solving RPM tasks
Efficient In-Context Learning in Vision-Language Models for Egocentric Videos
Recent advancements in text-only large language models (LLMs) have
highlighted the benefit of in-context learning for adapting to new tasks with a
few demonstrations. However, extending in-context learning to large
vision-language models (VLMs) using a huge amount of naturalistic
vision-language data has shown limited success, particularly for egocentric
videos, due to high data collection costs. We propose a novel training method
fficient n-context earning on
gocentric ideos (), which elicits
in-context learning in VLMs for egocentric videos without requiring massive,
naturalistic egocentric video datasets. involves architectural
and training data adaptations to allow the model to process contexts
interleaved with video clips and narrations, sampling of in-context examples
with clusters of similar verbs and nouns, use of data with skewed marginal
distributions with a long tail of infrequent verbs and nouns, as well as
homonyms and synonyms. Our evaluations show that -trained
models outperform larger VLMs trained on a huge amount of naturalistic data in
in-context learning. Furthermore, they can generalize to not only
out-of-distribution, but also novel, rare egocentric videos and texts via
in-context learning, demonstrating potential for applications requiring
cost-effective training, and rapid post-deployment adaptability. Our code and
demo are available at \url{https://github.com/yukw777/EILEV}.Comment: 10 pages, LaTeX; added acknowledgment
Quantitative profiling of selective Sox/POU pairing on hundreds of sequences in parallel by Coop-seq
© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. Cooperative binding of transcription factors is known to be important in the regulation of gene expression programs conferring cellular identities. However, current methods to measure cooperativity parameters have been laborious and therefore limited to studying only a few sequence variants at a time. We developed Coop-seq (cooperativity by sequencing) that is capable of efficiently and accurately determining the cooperativity parameters for hundreds of different DNA sequences in a single experiment. We apply Coop-seq to 12 dimer pairs from the Sox and POU families of transcription factors using 324 unique sequences with changed half-site orientation, altered spacing and discrete randomization within the binding elements. The study reveals specific dimerization profiles of different Sox factors with Oct4. By contrast, Oct4 and the three neural class III POU factors Brn2, Brn4 and Oct6 assemble with Sox2 in a surprisingly indistinguishable manner. Two novel half-site configurations can support functional Sox/Oct dimerization in addition to known composite motifs. Moreover, Coop-seq uncovers a nucleotide switch within the POU half-site when spacing is altered, which is mirrored in genomic loci bound by Sox2/Oct4 complexes.Link_to_subscribed_fulltex
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