Biology subjects

Shim, S.

Publications and source records attributed to Shim, S..

5 recordsLinked to original sources

bioRxiv2023

Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation

We describe the following shared data from N=103 healthy adults who completed a broad set cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90- minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. Subjects also completed resting state scans. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard.

neuroscience↗

bioRxiv2023

Glycoprotein VI is Critical for the Detection and Progression of Abdominal Aortic Aneurysms

A common feature in patients with abdominal aortic aneurysms (AAA) is the formation of a nonocclusive intraluminal thrombus (ILT) in regions of aortic dilation. Platelets are known to maintain hemostasis and propagate thrombosis through several redundant activation mechanisms, yet the role of platelet activation in the pathogenesis of AAA associated ILT is still poorly understood. Thus, we sought to investigate how platelet activation impacts the pathogenesis of AAA. Using RNA-sequencing, we identify that the platelet-associated transcripts are significantly enriched in the ILT compared to the adjacent aneurysm wall and healthy control aortas. We found that the platelet specific receptor glycoprotein VI (GPVI) is among the top enriched genes in AAA ILT and is increased on the platelet surface of AAA patients. Examination of a specific indicator of platelet activity, soluble GPVI (sGPVI), in two independent AAA patient cohorts is highly predictive of a AAA diagnosis and associates more strongly with aneurysm growth rate when compared to D-dimer in humans. Finally, intervention with the anti-GPVI antibody (J) in mice with established aneurysms blunted the progression of AAA in two independent mouse models. In conclusion, we show that levels of sGPVI in humans can predict a diagnosis of AAA and AAA growth rate, which may be critical in the identification of high-risk patients. We also identify GPVI as a novel platelet-specific AAA therapeutic target, with minimal risk of adverse bleeding complications, where none currently exist. KEY POINTSO_LISoluble glycoprotein VI, which is a platelet-derived blood biomarker, predicts a diagnosis of AAA, with high sensitivity and specificity in distinguishing patients with fast from slow-growing AAA. C_LIO_LIBlockade of glycoprotein VI in mice with established aneurysms reduces AAA progression and mortality, indicating therapeutic potential. C_LI

pathology↗

bioRxiv2023

Uncovering the Environmental Conditions Required for Phyllachora maydis Infection and Tar Spot Development on Corn in the United States for Use as Predictive Models for Future Epidemics

Phyllachora maydis is a fungal pathogen causing tar spot of corn (Zea mays L.), a new and emerging, yield-limiting disease in the United States. Since being first reported in Illinois and Indiana in 2015, P. maydis can now be found across much of the corn growing of the United States. Knowledge of the epidemiology of P. maydis is limited but could be useful in developing tar spot prediction tools. The research presented here aims to elucidate the environmental conditions necessary for the development of tar spot in the field and the creation of predictive models to anticipate future tar spot epidemics. Extended periods (30-day windowpanes) of moderate ambient temperature were most significant for explaining the development of tar spot. Shorter periods (14- to 21-day windowpanes) of moisture (relative humidity, dew point, number of hours with predicted leaf wetness) were negatively correlated with tar spot development. These weather variables were used to develop multiple logistic regression models, an ensembled model, and two machine learning models for the prediction of tar spot development. This work has improved the understanding of P. maydis epidemiology and provided the foundation for the development of a predictive tool for anticipating future tar spot epidemics.

plant biology↗

bioRxiv2023

The response time paradox in functional magnetic resonance imaging analyses

The functional MRI (fMRI) signal is a proxy for an unobservable neuronal signal, and differences in fMRI signals on cognitive tasks are generally interpreted as reflecting differences in the intensity of local neuronal activity. However, changes in either intensity or duration of neuronal activity can yield identical differences in fMRI signals. When conditions differ in response times (RTs), it is thus impossible to determine whether condition differences in fMRI signals are due to differences in the intensity of neuronal activity or to potentially spurious differences in the duration of neuronal activity. The most common fMRI analysis approach ignores RTs, making it difficult to interpret condition differences that could be driven by RTs and/or intensity. Because differences in response time are one of the most important signals of interest for cognitive psychology, nearly every task of interest for fMRI exhibits RT differences across conditions of interest. This results in a paradox, wherein the signal of interest for the psychologist is a potential confound for the fMRI researcher. We review this longstanding problem, and demonstrate that the failure to address RTs in the fMRI time series model can also lead to spurious correlations at the group level related to RTs or other variables of interest, potentially impacting the interpretation of brain-behavior correlations. We propose a simple approach that remedies this problem by including RT in the fMRI time series model. This model separates condition differences from RT differences, retaining power for detection of unconfounded condition differences while also allowing the identification of RT-related activation. We conclude by highlighting the need for further theoretical development regarding the interpretation of fMRI signals and their relationship to response times.

neuroscience↗

bioRxiv2020

EAT-UpTF: Enrichment Analysis Tool for Upstream Transcription Factors of a gene group

EAT-UpTF (Enrichment Analysis Tool for Upstream Transcription Factors of a gene group) is an open-source Python script that analyzes the enrichment of upstream transcription factors (TFs) in a group of genes-of-interest (GOIs). EAT-UpTF utilizes genome-wide lists of TF-target genes generated by DNA affinity purification followed by sequencing (DAP-seq) or chromatin immunoprecipitation followed by sequencing (ChIP-seq). Unlike previous methods based on the two-step prediction of cis-motifs and DNA-element-binding TFs, our EAT-UpTF analysis enabled a one-step identification of enriched upstream TFs in a set of GOIs using lists of empirically determined TF-target genes. Contactsangreashim@gmail.com or pjseo1@snu.ac.kr Availabilityhttps://github.com/sangreashim/EAT-UpTF

bioinformatics↗