Simple and Low-Cost Electroactive Membranes for Ammonia Recovery.

Ammonia is considered a contaminant to be removed from wastewater. However, ammonia is a valuable commodity chemical used as the primary feedstock for fertilizer manufacturing. Here we describe a simple and low-cost ammonia gas stripping membrane capable of recovering ammonia from wastewater. The material is composed of an electrically conducting porous carbon cloth coupled to a porous hydrophobic polypropylene support, that together form an electrically conductive membrane (ECM). When a cathodic potential is applied to the ECM surface, hydroxide ions are produced at the water-ECM interface, which transforms ammonium ions into higher-volatility ammonia that is stripped across the hydrophobic membrane material using an acid-stripping solution. The simple structure, low cost, and easy fabrication process make the ECM an attractive material for ammonia recovery from dilute aqueous streams, such as wastewater. When paired with an anode and immersed into a reactor containing synthetic wastewater (with an acid-stripping solution providing the driving force for ammonia transport), the ECM achieved an ammonia flux of 141.3 ± 14.0 g.cm-2.day-1 at a current density of 6.25 mA.cm-2 (69.2 ± 5.3 kg(NH3-N)/kWh). It was found that the ammonia flux was sensitive to the current density and acid circulation rate.

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG while at the same time preserving interpretability for medical decision-making. METHODS AND RESULTS: We identified 36 186 ECGs from the University of California, San Francisco database that would enable training of models for estimation of cardiac structure or function or detection of disease. We segmented the ECG into standard component waveforms and intervals using a novel combination of convolutional neural networks and hidden Markov models and evaluated this segmentation by comparing resulting electrical intervals against 141 864 measurements produced during the clinical workflow. We then built a patient-level ECG profile, a 725-element feature vector and used this profile to train and interpret machine learning models for examples of cardiac structure (left ventricular mass, left atrial volume, and mitral annulus e-prime) and disease (pulmonary arterial hypertension, hypertrophic cardiomyopathy, cardiac amyloid, and mitral valve prolapse). ECG measurements derived from the convolutional neural network-hidden Markov model segmentation agreed with clinical estimates, with median absolute deviations as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Models trained using patient-level ECG profiles enabled surprising quantitative estimates of left ventricular mass and mitral annulus e' velocity (median absolute deviation of 16% and 19%, respectively) with good discrimination for left ventricular hypertrophy and diastolic dysfunction as binary traits. Model performance using our approach for disease detection demonstrated areas under the receiver operating characteristic curve of 0.94 for pulmonary arterial hypertension, 0.91 for hypertrophic cardiomyopathy, 0.86 for cardiac amyloid, and 0.77 for mitral valve prolapse. CONCLUSIONS: Modern machine learning methods can extend the 12-lead ECG to quantitative applications well beyond its current uses while preserving the transparency that is so fundamental to clinical care.

Decision trees for identifying predictors of treatment effectiveness in clinical trials and its application to ovulation in a study of women with polycystic ovary syndrome.

BACKGROUND: Double-blind, randomized clinical trials are the preferred approach to demonstrating the effectiveness of one treatment against another. The comparison is, however, made on the average group effects. While patients and clinicians have always struggled to understand why patients respond differently to the same treatment, and while much hope has been held for the nascent field of predictive biomarkers (e.g. genetic markers), there is still much utility in exploring whether it is possible to estimate treatment efficacy based on demographic and baseline variables. METHODS: The pregnancy in polycystic ovary syndrome (PPCOS) study was a prospective, multi-center, randomized clinical trial comparing three ovulation induction regimens: clomiphene citrate (CC), metformin and the combination of the two. There were 446 women who ovulated in response to the treatments among the entire 626 participants. In this report, we focus on the 418 women who received CC (alone or combined with metformin) to determine if readily available baseline physical characteristics and/or easily obtainable baseline measures could be used to distinguish treatment effectiveness in stimulating ovulation. We used a recursive partitioning technique and developed a node-splitting rule to build decision tree models that reflected within-node and within-treatment responses. RESULTS: Overall, the combination of CC plus metformin resulted in an increased incidence of ovulation compared with CC alone. This is particularly so in women with relatively larger left ovarian volumes (≥ 19.5 cubic cm), and a left ovarian volume <19.5 cubic cm was related to treatment outcomes for all subsequent nodes. Women who were older, who had higher baseline insulin, higher waist-to-hip circumference ratio or higher sex hormone-binding globulin levels had better ovulatory rates with CC alone than with the combination of CC plus metformin. CONCLUSIONS: Polycystic ovary syndrome (PCOS) is a phenotypically diverse condition. Both baseline laboratory and clinical parameters can predict the ovulatory response in women with PCOS undergoing ovulation induction. Without a priori hypotheses with regard to any predictors, the observation regarding left ovary volume is novel and worthy of further investigation and validation.