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

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

Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks

With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for the continuous monitoring of patients at risk. We utilise an annotated dataset of 12,186 single-lead ECG recordings to build a diverse ensemble of recurrent neural networks (RNNs) that is able to distinguish between normal sinus rhythms, atrial fibrillation, other types of arrhythmia and signals that are too noisy to interpret. In order to ease learning over the temporal dimension, we introduce a novel task formulation that harnesses the natural segmentation of ECG signals into heartbeats to drastically reduce the number of time steps per sequence. Additionally, we extend our RNNs with an attention mechanism that enables us to reason about which heartbeats our RNNs focus on to make their decisions. Through the use of attention, our model maintains a high degree of interpretability, while also achieving state-of-the-art classification performance with an average F1 score of 0.79 on an unseen test set (n=3,658).Comment: Accepted at Computing in Cardiology (CinC) 201

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

Corn Stover Nutrient Removal Estimates for Central Iowa, USA

One of the most frequent producer-asked questions to those persons striving to secure sustainable corn (Zea mays L.) stover feedstock supplies for Iowa’s new bioenergy conversion or other bio-product facilities is “what quantity of nutrients will be removed if I harvest my stover?” Our objective is to summarize six years of field research from central Iowa, U.S.A. where more than 600, 1.5 m2 samples were collected by hand and divided into four plant fractions: vegetative material from the ear shank upward (top), vegetative material from approximately 10 cm above the soil surface to just below the ear (bottom), cobs, and grain. Another 400 stover samples, representing the vegetative material collected directly from a single-pass combine harvesting system or from stover bales were also collected and analyzed. All samples were dried, ground, and analyzed to determine C, N, P, K, Ca, Mg, S, Al, B, Cu, Fe, Mn, and Zn concentrations. Mean concentration and dry matter estimates for each sample were used to calculate nutrient removal and estimate fertilizer replacement costs which averaged $25.06,$20.04, $16.62,$19.40, and $27.41 Mg−1 for top, bottom, cob, stover, and grain fractions, respectively. We then used the plant fraction estimates to compare various stover harvest scenarios and provide an answer to the producer question posed above

Planting Date Effects on WinterTriticale Grain and Forage Yield

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 these stable, self-replicating varieties. Triticale yield, stress tolerance, and disease resistance are typically greater than similar traits found in wheat. Triticale doesn’t currently possess the grain traits of bread wheat, so its greatest market potential is as animal feed