KDBioSci

I tried to walk across a street, in a small town once. There was so much traffic, I waited so long some friends drove by and asked if my car broke down. I said “no, you’ve never seen anyone walk across the street before have you?” We don’t walk enough in America. Our high tech fitness trackers tell us 5,900 steps a day. 10,000 is recommended for health. I’m a medical data scientist, I work on high tech medicine. But a simple thing like walking is healthy. Walking regularly reduces sick days, boosts immunity, and reduces death. My grandmother walked to the grocery store several times a week and lived to 94. She walked because she had safe, pleasant, sidewalks and places to walk to, weaving walking into the fabric of life. Few neighborhoods have places to walk to because of single use zoning. By design our homes, offices, and shopping are all separated and connected by traffic clogged roads - weaving sitting in traffic into the fabric of our lives. We don’t walk more because it’s

Math like science is a kind of art. Parabola and sphere drawn with VTK.

Think of a beautiful city or town or part of a city you’d go to on vacation? Paris, Barcelona, Kyoto, Japan, Boston’s Faneuil Hall area. Smaller towns like Rockport, MA. What’s common about the places we’re thinking of? They’re almost all old. And they’re lively places you walk around and experience up close. Do you want to use an old cell phone? Old TV? old computer? old airplane? Why are we getting better at making just about everything except cities and towns? Some people say the world hasn’t built a beautiful city in 100 years. We even know how to build lively, beautiful places. Urban designers looked at and measured favorite cities and towns to figure out the rules for beautiful cities. They have local character, Barcelona, Kyoto and Boston’s Faneuil Hall area are distinctly different but all nice places. They’re compact enough to walk around and for using trains or buses. On the most lively streets the buildings are about two times taller than the width of the street.

Developing an Imaging Biomarker for Vascular Disease presentation

Machine learning can make medical image feature detection, annotation, segmentation, and classification much more robust to changes in the lab and clinic. Traditional deterministic image processing uses fixed pipelines of filters to denoise, simplify, and enhance. The deterministic filter pipelines are designed by experts in image processing who have spent time looking closely at the images, understanding the imaging system that took the images and understanding the goals of the imaging. Also any deterministic feature extraction process will always look at finite number of image features - even if that number is in the hundreds. What happens when looking at a new condition or disease that affects new never before examined features? What happens when the imaging process changed? When a dye is changed? When a MRI pulse sequence is changed? The deterministic pipeline fails in these cases and the pipeline itself has to be changed - meaning rewriting the program to some degree. Machine lea

CRISPR gene editing can increase understanding how diseases happen even before it can be used for treating diseases. CRISPR gene editing is new enough that testing and regulation will take so time before CRISPR gene editing is used in treatments at the doctors office. That won't stop CRISPR from making an impact on medicine soon. Gene editing is already building model organisms of disease - cell lines, lab animals such as mice, that mimic a disease helping researchers understand a disease. Gene editing also lets researchers change a gene to see what happens when something is changed. This transforms genetics and biology from observing genetic variation from the wild to experimenting with genes. Our understanding of the biology will increase rapidly and a deeper understanding of life and disease will let us develop new treatments whether the treatments are gene editing based or not.