PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201

Heat transfer via dropwise condensation on hydrophobic microstructured surfaces

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 22).Dropwise condensation has the potential to greatly increase heat transfer rates. Heat transfer coefficients by dropwise condensation and film condensation on microstructured silicon chips were compared. Heat transfer coefficients are found to be seventy percent higher in the hydrophobic, dropwise condensation case relative to the hydrophilic, film condensation case. With this increased heat transfer coefficient, dropwise condensation using microstructures could improve many heat exchange applications, particularly electronics cooling.by Karlen E. Ruleman.S.B

Nitrogen Management of Winter Triticale

Triticale (trit-ah-kay-lee) is a close relative of wheat. When durum wheat is pollinated with rye pollen, the cross is used in a breeding program to produce stable, self-replicating varieties. Triticale yield, stress tolerance, and disease resistance are typically greater than similar traits found in wheat. Triticale does not currently possess the grain traits of bread wheat, so its greatest market potential is as animal feed

Soil Fertility Paradigms Evaluated through Collaboration On-farm and On-station

A “paradigm” is a way of interpreting and making sense of the world. As such, our views on soil fertility are coherent with our interpretation of the scientific process and science institutions, and perhaps our feeling about the place of agriculture in the larger scheme of things. In agriculture today, two contradictory approaches to soil fertility uneasily coexist – the cation ratio paradigm (CR) and that referred to as “sufficient level of available nutrients” (SLAN)

Soil quality, yield stability and economic attributes of alternative crop rotations

Three long-term rotational crop studies in Iowa and one in Wisconsin were examined for conclusive evidence of rotational effects on soil quality. Long-term yield data also were evaluated to determine if there was a quantifiable relationship between soil quality and yield or yield stability

Potential economic, environmental benefits of narrow strip intercropping

Since its establishment in 1989, the Cropping Systems interdisciplinary research issue team has worked to develop a cropping system that is more environmentally sustainable than cur­ rent cropping approaches but just as favorable economically. The team\u27s work to date has focused on the strip intercropping concept