BE Seminar Series: November 15th

The BE Seminar Series continues this week with two lectures delivered by our current PhD Students. We hope to see you there!

Date: November 15, 2018
Location: Room 337, Towne Building

Claim Extraction for Biomedical Publications

Speaker: Titipat Achikulvisut, Ph.D. Student
Advisor: Konrad Kording, Ph.D.
Time: 12:05-12:25 pm

Abstract:

Scientific claims are a foundation of scientific discourse. Extracting claims from scientific articles is primarily done by researchers during literature review and discussions. The enormous growth in scientific articles makes this ever more challenging and time-consuming. Here, we develop a deep neural network architecture to solve the problem. Our model an F1 score of 0.704 on a large corpus of expertly annotated claims within abstracts. Our results suggest that we can use a small dataset of annotated resources to achieve high-accuracy claim detection. We release a tool for discourse and claim detection, and a novel dataset annotated by experts. We discuss further applications beyond Biomedical literature.

Multiple Sclerosis Lesion Segmentation with Joint Label Fusion Evaluated on OASIS and CNN

Speaker: Mengjin Dong, Ph.D. Student
Advisor: Paul Yushkevich, Ph.D.
Time: 12:30-12:50 pm

Abstract:

Scientific claims are a foundation of scientific discourse. Extracting claims from scientific articles is primarily done by researchers during literature review and discussions. The enormous growth in scientific articles makes this ever more challenging and time-consuming. Here, we develop a deep neural network architecture to solve the problem. Our model an F1 score of 0.704 on a large corpus of expertly annotated claims within abstracts. Our results suggest that we can use a small dataset of annotated resources to achieve high-accuracy claim detection. We release a tool for discourse and claim detection, and a novel dataset annotated by experts. We discuss further applications beyond Biomedical literature.

 

BE Seminar Series: September 20, 2018

The BE Seminar Series continues next week with three lectures delivered by our current PhD Students. We hope to see you there!

“Magnetic Susceptibility of Hemorrhagic Myocardial Infarction”

Brianna Moon, PhD Candidate

Speaker: Brianna Moon, PhD Candidate
Research Advisor: Walter Witschey, PhD

Date: Thursday, September 20, 2018
Time: 12:05pm-12:25pm
Location: Room 337 Towne Building

Abstract: 
Hemorrhagic myocardial infarction (MI) has been reported in 41% and 54% of ST-elevated MI patients after primary percutaneous coronary intervention. These patients are at high risk for adverse left ventricle (LV) remodeling, impaired LV function and increased risk of fatal arrhythmias.  Relaxation time MRI such as T2*-maps are sensitive to hemorrhagic infarct iron content, but are also affected by myocardial edema and fibrosis. Quantitative Susceptibility Mapping (QSM), which uses the MR signal phase to quantify tissue magnetic susceptibility, may be a more specific and sensitive marker of hemorrhagic MI. The objective of this study was to develop and validate cardiac QSM in a large animal model of myocardial infarction, investigate the association of magnetic susceptibility with iron content and infarct pathophysiology, and compare QSM to relaxation time mapping, susceptibility-weighted imaging (SWI), and late-gadolinium enhanced (LGE) MRI.

 

“Role of ACTG2 Mutations in Visceral Myopathy”

Sohaib Hashmi, MD/PhD Student

Speaker: Sohaib Hashmi, MD/PhD Student
Research Advisor: Robert O. Heuckeroth, MD, PhD

Date: Thursday, September 20, 2018
Time: 12:30-12:50pm
Location: Room 337 Towne Building

Abstract: 

Visceral myopathy is a debilitating chronic medical condition in which smooth muscle of the bowel, bladder, and uterus is weak or dysfunctional. When the bowel muscle is weak and unable to efficiently contract, the bowel becomes distended which causes pain, bilious vomiting, growth failure, and nutritional deficiencies. The abdominal distension can become life-threatening. Patients often become dependent on intravenous nutrition and undergo multiple rounds of abdominal surgery, which only partially alleviates symptoms. Recently, rare mutations in gamma smooth muscle actin (ACTG2) have been shown to be responsible for a large subset of visceral myopathies. ACTG2 is a critical protein in the smooth muscle contractile apparatus. However, we have only limited knowledge of how ACTG2 mutations may cause human disease. To improve our understanding of the pathophysiology of ACTG2 mutations, my work has the following specific aims:

1) Determine how pathogenic ACTG2 mutations affect actin structure and function in primary human intestinal smooth muscle cells (HISMCs).

2) Examine the effects of ACTG2 mutations on differentiation of human pluripotent stem cells into smooth muscle cells (SMCs).

These studies will allow us to elucidate the mechanisms through which ACTG2 mutations impair normal visceral smooth muscle development and function. I am examining the effects of ACTG2 mutations on actin filament organization in fixed cells and actin dynamics in live cells using fluorescent probes. I will investigate changes in contractile force generation using traction force microscopy. I am also developing a novel differentiation method to convert pluripotent stem cells into cells closely resembling visceral SMCs. We will use this method with CRISPR/Cas9 gene editing to study the effects of the mutations on SMC differentiation. We are currently generating pluripotent stem cell lines containing ACTG2 mutations and performing assays to investigate actin organization and dynamics. We are using these systems to identify robust phenotypes highlighting the mechanisms through which ACTG2 mutations cause disease. We hope to leverage this information in the selection of targets for high-throughput drug screening, which may eventually lead to novel treatment strategies for visceral myopathies.

 

“Distinct Patterns of Longitudinal Cortical Thinking and Perfusion in Pathological Subtypes of Behavioral Variant Frontotemporal Degeneration”

Christopher Olm, PhD Student

Speaker: Christopher Olm, PhD Student
Research Advisor: Murray Grossman, MD, EdD

Date: Thursday, September 20, 2018
Time: 12:55-1:15pm
Location: Room 337 Towne Building

Abstract: 
Two main sources of pathology have been identified in the behavioral variant of frontotemporal dementia (bvFTD): tau inclusions (FTLD-tau) and TDP-43 aggregates (FTLD-TDP). With therapies emerging that target these proteins, exploring distinct trajectories of degeneration can be extremely helpful for tracking progression in clinical trials and improve prognosis estimation. We hypothesized that longitudinal cortical thinning (CT) would identify areas of extant disease progression in bvFTD subgroups and longitudinal hypoperfusion would identify distinct regions of anticipated neurodegeneration. We included N=47 patients with probable or definite bvFTD and two MRI scanning sessions including T1-weighted and arterial spin labeling (ASL) scans, recruited through the Penn Frontotemporal Degeneration Center. Neuropathology, genetic mutations, or CSF protein markers (phosphorylated tau (p-tau)/Ab1-42<.09 for likely FTLD; p-tau<8.75 for FTLD-TDP) were used to identify bvFTD with likely FTLD-tau (n=28, mean age=63.1 years, mean disease duration=3.89 years) or likely FTLD-TDP (n=19, mean age=61.9, mean disease duration=3.06). Voxel-wise cortical thickness and cerebral blood flow estimates were generated for each T1 and ASL scan, respectively, using longitudinal pipelines in ANTs. We created annual change images by subtracting follow-up images from baseline and dividing by inter-scan interval. In whole brain voxel-wise comparisons, FTLD-tau showed significantly greater right orbitofrontal CT and longitudinal hypoperfusion in right middle temporal and angular cortex relative to FTLD-TDP. FTLD-TDP displayed greater progressive CT in left superior and middle frontal cortex, precentral gyrus, and right temporal cortex, and longitudinal hypoperfusion in medial prefrontal cortex relative to FTLD-tau. In conclusion, FTLD-tau and FTLD-TDP show distinct patterns of longitudinal CT and hypoperfusion. Structural and functional MRI contribute independent information potentially useful for characterizing disease progression in vivo for clinical trials.

BE Seminar Series starts tomorrow!

Rosalind Picard, ScD, FIEEE

Please join us for the first of our seminar lectures this year!

Rosalind Picard, ScD, FIEEE
Director of Affective Computing Research
Faculty Chair, MIT Mind+Hand+Heart
MIT Media Lab

“What Can We Discover About Emotions and the Brain from Noninvasive Measures?”

 

Date: Thursday, September 13, 2018
Time: 12:00PM-1:00 PM
Location: 337 Towne Building

Abstract:
Years ago, our team at MIT created wearable as well as non-contact imaging technology and machine learning algorithms to detect changes in human emotion.  As we shrunk the sensors and made them able to comfortably collect data 24/7, we started to discover several surprising findings, such as that autonomic activity measured through a sweat response was more specific than 100 years of studies had assumed.  While we originally thought this signal of “arousal” or “stress” was quite generally related to overall activation, we learned it could peak even when a patient’s EEG showed a lack of cortical brain activity. This talk will highlight some of the most surprising findings along the journey of measuring emotion “in the wild”with implications for anxiety, depression, sleep-memory consolidation, epilepsy, autism, pain studies, and more. What is the grand challenge we aim to solve next?

Bio:
Rosalind Picard, ScD, FIEEE, is founder and director of the Affective Computing Research Group at the MIT Media Laboratory, co-founder and Chief Scientist of Empatica, improving lives with clinical quality wearable sensors and analytics, and co-founder of Affectiva, providing tools for Emotion AI.  Picard is the author of over two hundred fifty peer-reviewed scientific articles and of the book, Affective Computing, which helped launch that field. Picard’s lab at MIT develops technologies to better measure, understand, forecast, and regulate emotion, including personalized machine-learning analytics that work with wearables and your smartphone.