Genetic architecture of cardiac dynamic flow volumes

Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function.

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study

BACKGROUND

Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing.

METHODS

In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems.

FINDINGS

45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]).

INTERPRETATION

A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications.

FUNDING

National Heart, Lung, and Blood Institute.

Epistasis regulates genetic control of cardiac hypertrophy

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141, IGF1R, TTN, and TNKS. Several loci not prioritized by univariate genome-wide association analysis are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we show that these interactions are preserved at the level of the cardiac transcriptome. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R. Our results expand the scope of genetic regulation of cardiac structure to epistasis.

Epistasis regulates genetic control of cardiac hypertrophy

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141, IGF1R, TTN, and TNKS. Several loci not prioritized by univariate genome-wide association analysis are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we show that these interactions are preserved at the level of the cardiac transcriptome. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R. Our results expand the scope of genetic regulation of cardiac structure to epistasis.

A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease

The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

Development and validation of a rapid visual technique for left ventricular hypertrophy detection from the electrocardiogram

Introduction

Left ventricular hypertrophy (LVH) detection techniques on by electrocardiogram (ECG) are cumbersome to remember with modest performance. This study validated a rapid technique for LVH detection and measured its performance against other techniques.

Methods

This was a retrospective cohort study of patients at Stanford Health Care who received ECGs and resting transthoracic echocardiograms (TTE) from 2006 through 2018. The novel technique, Witteles-Somani (WS), assesses for S- and R-wave overlap on adjacent precordial leads. The WS, Sokolow-Lyon, Cornell, and Peguero-Lo Presti techniques were algorithmically implemented on ECGs. Classification metrics, receiver-operator curves, and Pearson correlations measured performance. Age- and sex-adjusted Cox proportional hazard models evaluated associations between incident cardiovascular outcomes and each technique.

Results

A total of 53,333 ECG-TTE pairs from 18,873 patients were identified. Of all ECG-TTE pairs, 21,638 (40.6%) had TTE-diagnosed LVH. The WS technique had a sensitivity of 0.46, specificity of 0.66, and AUROC of 0.56, compared to Sokolow-Lyon (AUROC 0.55), Cornell (AUROC 0.63), and Peguero-Lo Presti (AUROC 0.63). Patients meeting LVH by WS technique had a higher risk of cardiovascular mortality [HR 1.18, 95% CI (1.12, 1.24), P < 0.001] and a higher risk of developing any cardiovascular disease [HR 1.29, 95% CI (1.22, 1.36), P < 0.001], myocardial infarction [HR 1.60, 95% CI (1.44, 1.78), P < 0.005], and heart failure [HR 1.24, 95% CI (1.17, 1.32), P < 0.001].

Conclusions

The WS criteria is a rapid visual technique for LVH detection with performance like other LVH detection techniques and is associated with incident cardiovascular outcomes.

Deep learning-based electrocardiographic screening for chronic kidney disease

BACKGROUND

Undiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs.

METHODS

We collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs between 2005 and 2018.

RESULTS

Using 12-lead ECG waveforms, our deep learning algorithm achieves discrimination for CKD of any stage with an AUC of 0.767 (95% CI 0.760-0.773) in a held-out test set and an AUC of 0.709 (0.708-0.710) in the external cohort. Our 12-lead ECG-based model performance is consistent across the severity of CKD, with an AUC of 0.753 (0.735-0.770) for mild CKD, AUC of 0.759 (0.750-0.767) for moderate-severe CKD, and an AUC of 0.783 (0.773-0.793) for ESRD. In patients under 60 years old, our model achieves high performance in detecting any stage CKD with both 12-lead (AUC 0.843 [0.836-0.852]) and 1-lead ECG waveform (0.824 [0.815-0.832]).

CONCLUSIONS

Our deep learning algorithm is able to detect CKD using ECG waveforms, with stronger performance in younger patients and more severe CKD stages. This ECG algorithm has the potential to augment screening for CKD.

Deep Electric Field Predictions by Drift-Reduced Braginskii Theory with Plasma-Neutral Interactions Based on Experimental Images of Boundary Turbulence

We present two-dimensional turbulent electric field calculations via physics-informed deep learning consistent with (i) drift-reduced Braginskii theory under the framework of an axisymmetric fusion plasma with purely toroidal field and (ii) experimental estimates of the fluctuating electron density and temperature on open field lines obtained from analysis of gas puff imaging of a discharge on the Alcator C-Mod tokamak. The inclusion of effects from the locally puffed atomic helium on particle and energy sources within the reduced plasma turbulence model is found to strengthen correlations between the electric field and electron pressure. The neutrals are also directly associated with broadening the distribution of turbulent field amplitudes and increasing E×B shearing rates. This demonstrates a novel approach in plasma experiments by solving for nonlinear dynamics consistent with partial differential equations and data without encoding explicit boundary nor initial conditions.

Robust identification of multiple-input single-output system response for efficient pickup noise removal from tokamak diagnostics

Electromagnetic pickup noise in the tokamak environment imposes an imminent challenge for measuring weak diagnostic photocurrents in the nA range. The diagnostic signal can be contaminated by an unknown mixture of crosstalk signals from coils powered by currents in the kA range. To address this issue, an algorithm for robust identification of linear multi-input single-output (MISO) systems has been developed. The MISO model describes the dynamic relationship between measured signals from power sources and observed signals in the diagnostic and allows for a precise subtraction of the noise component. The proposed method was tested on experimental diagnostic data from the DIII-D tokamak, and it has reduced noise by up to 20 dB in the 1-20 kHz range.

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease

BACKGROUND

Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR).

OBJECTIVES

This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination.

METHODS

A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision-recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model.

RESULTS

The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU-ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively.

CONCLUSIONS

Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program.

Deep modeling of plasma and neutral fluctuations from gas puff turbulence imaging

The role of turbulence in setting boundary plasma conditions is presently a key uncertainty in projecting to fusion energy reactors. To robustly diagnose edge turbulence, we develop and demonstrate a technique to translate brightness measurements of HeI line radiation into local plasma fluctuations via a novel integrated deep learning framework that combines neutral transport physics and collisional radiative theory for the 3³D - 2³P transition in atomic helium with unbounded correlation constraints between the electron density and temperature. The tenets for experimental validity are reviewed, illustrating that this turbulence analysis for ionized gases is transferable to both magnetized and unmagnetized environments with arbitrary geometries. Based on fast camera data on the Alcator C-Mod tokamak, we present the first two-dimensional time-dependent experimental measurements of the turbulent electron density, electron temperature, and neutral density, revealing shadowing effects in a fusion plasma using a single spectral line.

Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy. Eur Heart J Digit Health 2022;3:380-389. [PMID: 36712167 PMCID: PMC9707995 DOI: 10.1093/ehjdh/ztac033]

Aims

Determining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that an artificial intelligence method based joint interpretation of 12-lead electrocardiograms and echocardiogram videos could augment physician interpretation.

Methods and results

We chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage of electronic health record derived clinical blood pressure measurements and diagnostic consensus (often including molecular testing) among physicians in an HCM centre of excellence. Using more than 18 000 combined instances of electrocardiograms and echocardiograms from 2728 patients, we developed LVH-fusion. On held-out test data, LVH-fusion achieved an F1-score of 0.71 in predicting HCM, and 0.96 in predicting HTN. In head-to-head comparison with human readers LVH-fusion had higher sensitivity and specificity rates than its human counterparts. Finally, we use explainability techniques to investigate local and global features that positively and negatively impact LVH-fusion prediction estimates providing confirmation from unsupervised analysis the diagnostic power of lateral T-wave inversion on the ECG and proximal septal hypertrophy on the echocardiogram for HCM.

Conclusion

These results show that deep learning can provide effective physician augmentation in the face of a common diagnostic dilemma with far reaching implications for the prevention of sudden cardiac death.

Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation

Importance

Millions of clinicians rely daily on automated preliminary electrocardiogram (ECG) interpretation. Critical comparisons of machine learning-based automated analysis against clinically accepted standards of care are lacking.

Objective

To use readily available 12-lead ECG data to train and apply an explainability technique to a convolutional neural network (CNN) that achieves high performance against clinical standards of care.

Design, Setting, and Participants

This cross-sectional study was conducted using data from January 1, 2003, to December 31, 2018. Data were obtained in a commonly available 12-lead ECG format from a single-center tertiary care institution. All patients aged 18 years or older who received ECGs at the University of California, San Francisco, were included, yielding a total of 365 009 patients. Data were analyzed from January 1, 2019, to March 2, 2021.

Exposures

A CNN was trained to predict the presence of 38 diagnostic classes in 5 categories from 12-lead ECG data. A CNN explainability technique called LIME (Linear Interpretable Model-Agnostic Explanations) was used to visualize ECG segments contributing to CNN diagnoses.

Main Outcomes and Measures

Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated for the CNN in the holdout test data set against cardiologist clinical diagnoses. For a second validation, 3 electrophysiologists provided consensus committee diagnoses against which the CNN, cardiologist clinical diagnosis, and MUSE (GE Healthcare) automated analysis performance was compared using the F1 score; AUC, sensitivity, and specificity were also calculated for the CNN against the consensus committee.

Results

A total of 992 748 ECGs from 365 009 adult patients (mean [SD] age, 56.2 [17.6] years; 183 600 women [50.3%]; and 175 277 White patients [48.0%]) were included in the analysis. In 91 440 test data set ECGs, the CNN demonstrated an AUC of at least 0.960 for 32 of 38 classes (84.2%). Against the consensus committee diagnoses, the CNN had higher frequency-weighted mean F1 scores than both cardiologists and MUSE in all 5 categories (CNN frequency-weighted F1 score for rhythm, 0.812; conduction, 0.729; chamber diagnosis, 0.598; infarct, 0.674; and other diagnosis, 0.875). For 32 of 38 classes (84.2%), the CNN had AUCs of at least 0.910 and demonstrated comparable F1 scores and higher sensitivity than cardiologists, except for atrial fibrillation (CNN F1 score, 0.847 vs cardiologist F1 score, 0.881), junctional rhythm (0.526 vs 0.727), premature ventricular complex (0.786 vs 0.800), and Wolff-Parkinson-White (0.800 vs 0.842). Compared with MUSE, the CNN had higher F1 scores for all classes except supraventricular tachycardia (CNN F1 score, 0.696 vs MUSE F1 score, 0.714). The LIME technique highlighted physiologically relevant ECG segments.

Conclusions and Relevance

The results of this cross-sectional study suggest that readily available ECG data can be used to train a CNN algorithm to achieve comparable performance to clinical cardiologists and exceed the performance of MUSE automated analysis for most diagnoses, with some exceptions. The LIME explainability technique applied to CNNs highlights physiologically relevant ECG segments that contribute to the CNN's diagnoses.

Deep learning evaluation of biomarkers from echocardiogram videos

BACKGROUND

Laboratory testing is routinely used to assay blood biomarkers to provide information on physiologic state beyond what clinicians can evaluate from interpreting medical imaging. We hypothesized that deep learning interpretation of echocardiogram videos can provide additional value in understanding disease states and can evaluate common biomarkers results.

METHODS

We developed EchoNet-Labs, a video-based deep learning algorithm to detect evidence of anemia, elevated B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN), as well as values of ten additional lab tests directly from echocardiograms. We included patients (n = 39,460) aged 18 years or older with one or more apical-4-chamber echocardiogram videos (n = 70,066) from Stanford Healthcare for training and internal testing of EchoNet-Lab's performance in estimating the most proximal biomarker result. Without fine-tuning, the performance of EchoNet-Labs was further evaluated on an additional external test dataset (n = 1,301) from Cedars-Sinai Medical Center. We calculated the area under the curve (AUC) of the receiver operating characteristic curve for the internal and external test datasets.

FINDINGS

On the held-out test set of Stanford patients not previously seen during model training, EchoNet-Labs achieved an AUC of 0.80 (0.79-0.81) in detecting anemia (low hemoglobin), 0.86 (0.85-0.88) in detecting elevated BNP, 0.75 (0.73-0.78) in detecting elevated troponin I, and 0.74 (0.72-0.76) in detecting elevated BUN. On the external test dataset from Cedars-Sinai, EchoNet-Labs achieved an AUC of 0.80 (0.77-0.82) in detecting anemia, of 0.82 (0.79-0.84) in detecting elevated BNP, of 0.75 (0.72-0.78) in detecting elevated troponin I, and of 0.69 (0.66-0.71) in detecting elevated BUN. We further demonstrate the utility of the model in detecting abnormalities in 10 additional lab tests. We investigate the features necessary for EchoNet-Labs to make successful detection and identify potential mechanisms for each biomarker using well-known and novel explainability techniques.

INTERPRETATION

These results show that deep learning applied to diagnostic imaging can provide additional clinical value and identify phenotypic information beyond current imaging interpretation methods.

FUNDING

J.W.H. and B.H. are supported by the NSF Graduate Research Fellowship. D.O. is supported by NIH K99 HL157421-01. J.Y.Z. is supported by NSF CAREER 1942926, NIH R21 MD012867-01, NIH P30AG059307 and by a Chan-Zuckerberg Biohub Fellowship.

Uncovering turbulent plasma dynamics via deep learning from partial observations

One of the most intensely studied aspects of magnetic confinement fusion is edge plasma turbulence which is critical to reactor performance and operation. Drift-reduced Braginskii two-fluid theory has for decades been widely applied to model boundary plasmas with varying success. Towards better understanding edge turbulence in both theory and experiment, we demonstrate that a physics-informed deep learning framework constrained by partial differential equations can accurately learn turbulent fields consistent with the two-fluid theory from partial observations of electron pressure which is not otherwise possible using conventional equilibrium models. This technique presents a paradigm for the advanced design of plasma diagnostics and validation of magnetized plasma turbulence theories in challenging thermonuclear environments.

Feasibility study for a high-k temperature fluctuation diagnostic based on soft x-ray imaging

A new pseudolocal tomography algorithm is developed for soft X-ray(SXR) imaging measurements of the turbulent electron temperature fluctuations (δ T_e) in tokamaks and stellarators. The algorithm overcomes the constraints of limited viewing ports on the vessel wall (viewing angle) and limited number of lines of sight (LOS). This is accomplished by increasing the number of LOS locally in a region of interest. Numerical modeling demonstrates that the wavenumber spectrum of the turbulence can be reliably reconstructed, with an acceptable number of viewing angles and LOS and suitable low SNR detectors. We conclude that a SXR imaging diagnostic for measurements of turbulent δ T_e using a pseudolocal reconstruction algorithm is feasible.

A 1D Lyman-alpha profile camera for plasma edge neutral studies on the DIII-D tokamak

A one dimensional, absolutely calibrated pinhole camera system was installed on the DIII-D tokamak to measure edge Lyman-alpha (Ly-α) emission from hydrogen isotopes, which can be used to infer neutral density and ionization rate profiles. The system is composed of two cameras, each providing a toroidal fan of 20 lines of sight, viewing the plasma edge on the inboard and outboard side of DIII-D. The cameras' views lie in a horizontal plane 77 cm below the midplane. At its tangency radius, each channel provides a radial resolution of ∼2 cm full width at half maximum (FWHM) with a total coverage of 22 cm. Each camera consists of a rectangular pinhole, Ly-α reflective mirror, narrow-band Ly-α transmission filter, and a 20 channel AXUV photodetector. The combined mirror and transmission filter have a FWHM of 5 nm, centered near the Ly-α wavelength of 121.6 nm and is capable of rejecting significant, parasitic carbon-III (C-III) emission from intrinsic plasma impurities. To provide a high spatial resolution measurement in a compact footprint, the camera utilizes advanced engineering and manufacturing techniques including 3D printing, high stability mirror mounts, and a novel alignment procedure. Absolutely calibrated, spatially resolved Ly-α brightness measurements utilize a bright, isolated line with low parasitic surface reflections and enable quantitative comparison to modeling to study divertor neutral leakage, main chamber fueling, and radial particle transport.

Absolute calibration of the Lyman-α measurement apparatus at DIII-D

The LLAMA (Lyman-Alpha Measurement Apparatus) diagnostic was recently installed on the DIII-D tokamak [Rosenthal et al., Rev. Sci. Instrum. (submitted) (2020)]. LLAMA is a pinhole camera system with a narrow band Bragg mirror, a bandpass interference filter, and an absolute extreme ultraviolet photodiode detector array, which measures the Ly-α brightness in the toroidal direction on the inboard, high field side (HFS) and outboard, low field side (LFS). This contribution presents a setup and a procedure for an absolute calibration near the Ly-α line at 121.6 nm. The LLAMA in-vacuum components are designed as a compact, transferable setup that can be mounted in an ex situ vacuum enclosure that is equipped with an absolutely calibrated Ly-α source. The spectral purity and stability of the Ly-α source are characterized using a vacuum ultraviolet spectrometer, while the Ly-α source brightness is measured by a NIST-calibrated photodiode. The non-uniform nature of the Ly-α source emission was overcome by performing a calibration procedure that scans the Ly-α source position and employs a numerical optimization to determine the emission pattern. Nominal and measured calibration factors are determined and compared, showing agreement within their uncertainties. A first conversion of the measured signal obtained from DIII-D indicates that the Ly-α brightness on the HFS and LFS is on the order of 10²⁰ Ph sr^-1 m^-2 s^-1. The established calibration setup and procedure will be regularly used to re-calibrate the LLAMA during DIII-D vents to monitor possible degradation of optical components and detectors.

A digital biomarker of diabetes from smartphone-based vascular signals

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 2045¹. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity². Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.

Mineral separation protocol for accurate and precise rhenium-osmium (Re-Os) geochronology and sulphur isotope composition of individual sulphide species

A temporal framework for mineral deposits is essential when addressing the history of their formation and conceptualizing genetic models of their origin. This knowledge is critical to understand how crust-forming processes are related to metal accumulations at specific time and conditions of Earth evolution. To this end, high-precision absolute geochronology utilising the rhenium-osmium (Re-Os) radiometric system in specific sulphide minerals is becoming a method of choice. Here, we present a procedure to obtain mineral separates of individual sulphide species that may coexist within specific mineralized horizons in ore deposits. This protocol is based on preliminary petrographic and paragenetic investigations of sulphide and gangue minerals using reflected and transmitted light microscopy. Our approach emphasizes the key role of a stepwise use of a Frantz isodynamic separator to produce mineral separates of individual sulphide species that are subsequently processed for Re-Os and sulphur isotope geochemistry.•Detailed method and its graphical illustration modified from an original procedure introduced by [1], [2].•Quality control and validation of monophasic mineral separates made by microscopic investigations and qualitative analysis of aliquots embedded in epoxy mounts.•The present method, which contributed to the successful results presented in the co-publication by Saintilan et al. (2020), demonstrates why other studies reporting Re-Os isotope data for mixtures of sulphide minerals should be considered with caution.

Observation of Efficient Lower Hybrid Current Drive at High Density in Diverted Plasmas on the Alcator C-Mod Tokamak

Efficient lower hybrid current drive (LHCD) is demonstrated at densities up to n[over ¯]_{e}≈1.5×10^{20}  m^{-3} in diverted plasmas on the Alcator C-Mod tokamak by operating at increased plasma current-and therefore reduced Greenwald density fraction. This density exceeds the nominal "LH density limit" at n[over ¯]_{e}≈1.0×10^{20}  m^{-3} reported previously, above which an anomalous loss of current drive efficiency was observed. The recovery of current drive efficiency to a level consistent with engineering scalings is correlated with a reduction in density shoulders and turbulence levels in the far scrape-off layer. Concurrently, rf wave interaction with the edge and/or scrape-off-layer plasma is reduced, as indicated by a minimal broadening of the wave frequency spectrum measured at the plasma edge. These results have important implications for sustaining steady-state tokamak operation and indicate a pathway forward for implementing efficient LHCD in a reactor.

Fast Low-to-High Confinement Mode Bifurcation Dynamics in a Tokamak Edge Plasma Gyrokinetic Simulation

Transport barrier formation and its relation to sheared flows in fluids and plasmas are of fundamental interest in various natural and laboratory observations and of critical importance in achieving an economical energy production in a magnetic fusion device. Here we report the first observation of an edge transport barrier formation event in an electrostatic gyrokinetic simulation carried out in a realistic diverted tokamak edge geometry under strong forcing by a high rate of heat deposition. The results show that turbulent Reynolds-stress-driven sheared E×B flows act in concert with neoclassical orbit loss to quench turbulent transport and form a transport barrier just inside the last closed magnetic flux surface.

Turbulence Nonlinearities Shed Light on Geometric Asymmetry in Tokamak Confinement Transitions

A comprehensive study of fully frequency-resolved nonlinear kinetic energy transfer has been performed for the first time in a diverted tokamak, providing new insight into the parametric dependences of edge turbulence transitions. Measurements using gas puff imaging in the turbulent L-mode state illuminate the source of the long known but as yet unexplained "favorable-unfavorable" geometric asymmetry of the power threshold for transition to the turbulence-suppressed H mode. Results from the recently discovered I mode point to a competition between zonal flow (ZF) and geodesic-acoustic modes (GAM) for turbulent energy, while showing new evidence that the I-to-H transition is still dominated by ZFs. The availability of nonlinear drive for the GAM against net heat flux through the edge corresponds very well to empirical scalings found experimentally for accessing the I mode.

Temperature gradient scale length measurement: A high accuracy application of electron cyclotron emission without calibration

Calibration is a crucial procedure in electron temperature (T_e) inference from a typical electron cyclotron emission (ECE) diagnostic on tokamaks. Although the calibration provides an important multiplying factor for an individual ECE channel, the parameter ΔT_e/T_e is independent of any calibration. Since an ECE channel measures the cyclotron emission for a particular flux surface, a non-perturbing change in toroidal magnetic field changes the view of that channel. Hence the calibration-free parameter is a measure of T_e gradient. B_T-jog technique is presented here which employs the parameter and the raw ECE signals for direct measurement of electron temperature gradient scale length.

Observation of edge instability limiting the pedestal growth in tokamak plasmas

With fusion device performance hinging on the edge pedestal pressure, it is imperative to experimentally understand the physical mechanism dictating the pedestal characteristics and to validate and improve pedestal predictive models. This Letter reports direct evidence of density and magnetic fluctuations showing the stiff onset of an edge instability leading to the saturation of the pedestal on the Alcator C-Mod tokamak. Edge stability analyses indicate that the pedestal is unstable to both ballooning mode and kinetic ballooning mode in agreement with observations.

Effects of magnetic shear on toroidal rotation in tokamak plasmas with lower hybrid current drive

Application of lower hybrid (LH) current drive in tokamak plasmas can induce both co- and countercurrent directed changes in toroidal rotation, depending on the core q profile. For discharges with q(0) <1, rotation increments in the countercurrent direction are observed. If the LH-driven current is sufficient to suppress sawteeth and increase q(0) above unity, the core toroidal rotation change is in the cocurrent direction. This change in sign of the rotation increment is consistent with a change in sign of the residual stress (the divergence of which constitutes an intrinsic torque that drives the flow) through its dependence on magnetic shear.

Formation and stability of impurity "snakes" in tokamak plasmas

New observations of the formation and dynamics of long-lived impurity-induced helical "snake" modes in tokamak plasmas have recently been carried out on Alcator C-Mod. The snakes form as an asymmetry in the impurity ion density that undergoes a seamless transition from a small helically displaced density to a large crescent-shaped helical structure inside q<1, with a regularly sawtoothing core. The observations show that the conditions for the formation and persistence of a snake cannot be explained by plasma pressure alone. Instead, many features arise naturally from nonlinear interactions in a 3D MHD model that separately evolves the plasma density and temperature.

Rotation reversal bifurcation and energy confinement saturation in tokamak Ohmic L-mode plasmas

Direction reversals of intrinsic toroidal rotation have been observed in diverted Alcator C-Mod Ohmic L-mode plasmas following electron density ramps. For low density discharges, the core rotation is directed cocurrent, and reverses to countercurrent following an increase in the density above a certain threshold. Such reversals occur together with a decrease in density fluctuations with 2 cm(-1)≤k(θ)≤11 cm(-1) and frequencies above 70 kHz. There is a strong correlation between the reversal density and the density at which the Ohmic L-mode energy confinement changes from the linear to the saturated regime.

Edge temperature gradient as intrinsic rotation drive in Alcator C-Mod tokamak plasmas

Intrinsic rotation has been observed in I-mode plasmas from the C-Mod tokamak, and is found to be similar to that in H mode, both in its edge origin and in the scaling with global pressure. Since both plasmas have similar edge ∇T, but completely different edge ∇n, it may be concluded that the drive of the intrinsic rotation is the edge ∇T rather than ∇P. Evidence suggests that the connection between gradients and rotation is the residual stress, and a scaling for the rotation from conversion of free energy to macroscopic flow is calculated.

Observation of self-generated flows in tokamak plasmas with lower-hybrid-driven current

In Alcator C-Mod discharges lower hybrid waves have been shown to induce a countercurrent change in toroidal rotation of up to 60 km/s in the central region of the plasma (r/a approximately <0.4). This modification of the toroidal rotation profile develops on a time scale comparable to the current redistribution time (approximately 100 ms) but longer than the energy and momentum confinement times (approximately 20 ms). A comparison of the co- and countercurrent injected waves indicates that current drive (as opposed to heating) is responsible for the rotation profile modifications. Furthermore, the changes in central rotation velocity induced by lower hybrid current drive (LHCD) are well correlated with changes in normalized internal inductance. The application of LHCD has been shown to generate sheared rotation profiles and a negative increment in the radial electric field profile consistent with a fast electron pinch.

Observational study of temperature, moisture, pH and bacteria in straw bedding, and faecal consistency, cleanliness and mastitis in cows in four dairy herds

A study of four dairy farms showed that much of the straw stored for bedding was too wet (over 15 per cent moisture content). Most of the beds, including their top surfaces, were damp (above 75 per cent relative humidity). The temperature of the surface of most of the straw beds was related to the air temperature, many being below 15 degrees C, but below the surface the temperatures of most beds reached between 15 degrees C and 45 degrees C within about a week of their being renewed. Bacterial counts also reached a plateau within one to two weeks. The pH of the top layers of straw was usually between 8.5 and 9.5. Adding lime daily to the top layer of the straw failed to raise the pH to levels at which Escherichia coli and Streptococcus uberis do not survive. Most of the counts of E coli and faecal streptococci in the top layers of straw were above 10(6) colony-forming units/g. Counts of E coli and S uberis were much higher in the beds of early lactation cows than in those of dry cows. Many of the early lactation cows were heavily and persistently contaminated with faeces. Dry cows were much cleaner. Groups of cows with firmer faeces were also cleaner. The farm with the lowest incidence of mastitis had the cleanest cows and the most satisfactory beds.

Oral contraceptive use is associated with increased cardiovascular reactivity in nonsmokers

Women who smoke and take oral contraceptives (OCs) have significantly increased risk of cardiovascular disease, but the exact mechanismsfor the increased risk are not known. Cardiovascular reactivity to psychological stress may be one mechanism for the enhanced risk, but the small number of studies examining whether OC users who smoke have greater reactivity have produced mixed results. The purpose of this study was to examine the effect of chronic cigarette smoking, acute nicotine administration, and OC use on cardiovascular and lipid reactivity. Sixty healthy women, half of whom had been using OCs for at least the previous 6 months, participated in the study. Approximately two thirds were smokers and were randomized to be tested after either a 12-hr nicotine deprivation or administration of nicotine gum. One third were nonsmokers. Heart rate, blood pressure, and lipid measures were taken at rest, during a videotaped speech task, and during recovery from the task. Results indicated that, among OC nonusers, there was no effect of smoking status or nicotine administration on cardiovascular reactivity. However, among OC users, nonsmokers had significantly greater heart rate and diastolic blood pressure reactivity to stress. These data show that acute nicotine administration, in the form of nicotine gum, has no effect on cardiovascular or lipid stress reactivity in women. However OC use among nonsmoking women is associated with greater cardiovascular reactivity to stress.

Catecholamine stress responses in arterialized blood

Arterialized and venous blood was compared to determine if the arterialization procedure enhances the detection of stress-related changes in catecholamines. Lipid and hematologic measures were also compared for possible distortion by arterialization. Fifteen men completed two stressors. Indwelling venous catheters were placed retrograde in each hand, and the right hand was warmed to a constant temperature. Blood samples were taken simultaneously from both hands, and plasma catecholamines were determined. Arterialization increased baseline epinephrine; there were no effects of arterialization on catecholamines during stress, nor in lipid or hematologic measures during baseline or stress. Thus, arterialization of blood results in small increases in resting epinephrine levels, but does not obscure lipid measures. More importantly, arterialization of venous blood does not enhance the detection of stress-related changes in catecholamines.

Depressed mood is related to high-frequency heart rate variability during stressors

OBJECTIVE

The purpose of this study was to examine the relationships between depressed mood and parasympathetic control of the heart in healthy men and women at rest and during two stressors.

METHODS

Fifty-three healthy college students completed a laboratory stress protocol that included a baseline resting period, a challenging speech task, and a forehead cold pressor task. Depressed mood was assessed using the Beck Depression Inventory (BDI). Parasympathetic cardiac control was measured as the high-frequency (0.12-0.40 Hz) component (HF) of heart rate variability using power spectrum analysis. Blood pressure, respiration rate, and respiration amplitude were measured simultaneously.

RESULTS

Participants were categorized as having a high or low depressed mood on the basis of median splits of their BDI scores. Those in the high depressed mood group had significantly greater reductions in HF during the speech task and significantly smaller increases in HF during the forehead cold pressor task than those in the low depressed mood group. Women had significantly greater reductions in HF during the speech task and smaller increases in HF during the forehead cold pressor task than men. However, gender and depressed mood did not interact to predict changes in HF.

CONCLUSIONS

Depressed mood is related to the magnitude of decrease in parasympathetic cardiac control during stressors in healthy men and women. These findings extend those of previous studies, in which a similar phenomenon was observed among patients with cardiac disease. Because the participants in this study were healthy, the relationship between depressed mood and parasympathetic cardiac control does not seem to be secondary to cardiovascular disease.

Lipid reactivity among men with a parental history of myocardial infarction

In the current study, we examined lipid and cardiovascular responses to an acute stressor among men with and without a parental history of myocardial infarction. 37 men were selected from a large group who completed medical history questionnaires and interviews. Twenty-two men who denied parental history of heart disease (negative parental history) were compared with 15 men with one or both parents who had suffered a myocardial infarction (positive parental history). Total cholesterol, high- and low-density lipoprotein cholesterol, triglycerides, heart rate, and blood pressure were measured at rest and during a videotaped speech stressor. Positive parental history men had significantly higher low-density lipoprotein cholesterol levels and blood pressure at baseline, significantly lower high-density lipoprotein cholesterol levels at baseline, and significantly larger total cholesterol and low-density lipoprotein cholesterol reactivity, relative to negative parental history men. Because parental history is a risk factor for subsequent cardiovascular morbidity and mortality, these data suggest that lipid reactivity to stress may be biologically important.

Causes of blindness among Navajo Indians: an update

BACKGROUND

The causes of blindness among Navajo Indians, an ethnically distinct community within the United States, were last studied in 1982. This article presents an updated report on the causes of blindness among the Navajo.

METHODS

Staff optometrists at each of the hospitals and clinics on the Navajo Reservation collected information for each affected eye: date of onset, cause, blinding process, and best visual acuity. In terms of the total number of eyes affected, it was found that the most frequently encountered etiology was trauma, followed by congenital causes, diabetes mellitus, primary open-angle glaucoma, age-related macular degeneration, and trachoma.

CONCLUSIONS

Considering raw numbers as well as preventability, it is suggested that trauma, diabetes mellitus, and primary open-angle glaucoma be targeted for a focused intervention of patient-as well as public-education aimed at reducing blindness from these causes.

Epidemiology of lameness in dairy cattle: the influence of cubicles and indoor and outdoor walking surfaces

A survey of cubicles and indoor and outdoor walking surfaces on 37 farms served by four veterinary practices in Somerset, Cheshire, Wirral and west. Wales was carried out in 1989 to 1991. A study of the space requirements of Friesian/Holstein cows at pasture showed that they required approximately 240 cm x 120 cm lying space and a further 60 cm lunging space for rising. By these standards, 87 per cent of the cubicles were too short and 50 per cent were too wide or too narrow. Over 1500 observations on cows lying down, rising and standing indicated that only 12 per cent of the cubicles permitted real freedom of movement; 91 per cent of top partition rails were judged to be too low and 70 per cent of bottom rails too low or too high. In addition, the kerb was very high in 76 per cent of the cubicles. As a result, 10 per cent of cows appeared moderately or severely restricted when lying down, 33 per cent when rising and 55 per cent when standing. Over 2000 cubicle beds were also studied; 75 per cent had a concrete base and of those, 63 per cent were judged to have too little bedding and 11 per cent next to none. Higher incidences and prevalences of lameness were associated with limited borrowing space (P < 0.01) low bottom rails (P < 0.05), high kerbs (P < 0.05) and inadequate bedding (P < 0.01). Of 3190 walking surfaces, only 25 per cent were classified as satisfactory in the first winter and 34 per cent in the second. In general, surfaces in silage bays were too rough and those in other sites were too smooth. The farms with the smoothest indoor walking surfaces had a significantly higher incidence of lameness (P < 0.01). Of 3335 outdoor walking surfaces only 25 per cent were classified as satisfactory, and 70 per cent were too rough. The incidence of lameness was not significantly related to these findings.

Epidemiology of lameness in dairy cattle: description and analysis of foot lesions

Information from 37 dairy farms, in four regions of England and Wales provided data on 8991 lesions and the preventive trimming of 4837 cows' feet. Of the total of 13,828 forms returned, veterinary surgeons treated 32 per cent and farmers or stockmen 46 per cent. Of the 8645 lesions associated with episodes of lameness, lesions in the hindlimbs accounted for 92 per cent, of which 65 per cent were in the outer claw, 20 per cent in the skin and 14 per cent in the inner claw. Sole ulcers (40 per cent) and white line lesions (29 per cent) were the predominant diseases of horn, and digital dermatitis (40 per cent) was the most common disease of the skin. Subjective assessments showed that sandcrack, penetration of the sole by foreign bodies and interdigital necrobacillosis were associated with the most severe cases of lameness. There was a significant seasonal effect in the reporting of lesions.

Incidence and prevalence of lameness in dairy cattle

A survey was made of 37 dairy farms in Wirral, mid-Cheshire, mid-Somerset and Dyfed, Wales, to assess the incidence and prevalence of lameness in the cows between May 1989 and September 1991. The incidence was obtained from records made whenever a cow was examined for lameness or received preventive foot-trimming. The mean annual incidence was 54.6 new cases per 100 cows with a range from 10.7 to 170.1 and the mean values during summer and winter were 22.9 and 31.7, respectively. The prevalence of lameness was measured by regular visits at which locomotion was scored on a scale of 1 to 5, and the prevalence of lameness was calculated for each visit as the proportion of cows with scores of 3 or more. The mean annual prevalence over the whole period was 20.6 per cent with a range from 2.0 to 53.9 per cent for the 37 farms. The mean prevalences during summer and winter were 18.6 and 25.0 per cent, respectively. The prevalence measured at a single visit in midsummer or midwinter was significantly correlated with the mean prevalence over the whole corresponding period and may be useful as an assessment of the extent of lameness in a herd and the efficacy of control measures. There was evidence that training farmers to recognise early cases of lameness and request veterinary treatment resulted in a marked reduction in the duration of cases of lameness.

Behaviour of dairy cows in a straw yard in relation to lameness

Lying down and other behavioural activities of dairy cows were studied for three 24-hour periods in a straw yard. The cows spent a total of 13.6 hours in the straw yard and lay down for 9.7 hours. The lying down time in one observation was 10.8 hours and this period may be considered ideal because there was little disturbance during that observation. Significantly more time was spent lying down at night than in the day and significantly more time was spent lying down and ruminating than standing up and ruminating. The total time spent lying down was significantly positively correlated with the time spent lying down and ruminating. Ten hours or more spent lying down may be adequate for proper rest in dairy cattle. Straw yards are better than many cubicles for lying and a longer period spent lying down may be important for the prevention of lameness in dairy cows.

Behaviour of first lactation and adult dairy cows while housed and at pasture and its relationship with sole lesions

The times spent lying down and standing by first lactation and adult cows while they were housed and while they were at pasture were studied and related during the period of housing to the incidence of sole lesions in first lactation cows. First lactation cows lay down for a shorter time in the early housing period than later. First lactation and adult cows lay down for longer when at pasture. Maximum lying time was significantly longer and the frequency of lying lower on pasture than indoors. The times spent lying and standing and the frequency of lying were related to the incidence of sole lesions. Rumination time was not related to the occurrence of sole lesions although there were significant variations in rumination behaviour while the animals were housed and at pasture. The patterns of lying and other activities of first lactation and adult cows while they were housed were quite different from those while they were at pasture.

Factors associated with failure to complete treatment for diabetic retinopathy among Navajo Indians

OBJECTIVE

To determine the proportion of patients with suspected proliferative diabetic retinopathy who did not receive the recommended follow-up ophthalmological evaluation and care, and to examine associations between various patient characteristics and the failure to obtain care.

RESEARCH DESIGN AND METHODS

The study cohort included all Navajo Indians identified by a retrospective review of records who had proliferative diabetic retinopathy diagnosed at an Indian Health Service Optometry Clinic between 1 October 1985 and 30 September 1988. Follow-up data were obtained by medical record reviews and by interviews with subjects.

RESULTS

Of 69 patients identified, 57 of 61 living patients were interviewed. Twenty-three (40.4%) had failed to obtain recommended follow-up. The RR for incomplete treatment among those without a vehicle in the household compared with those with a vehicle was 1.91 (95% CI 1.32-2.76). Other factors associated with incomplete treatment were female sex and marital status other than currently married. Twelve (21%) patients answered "no" to the question, "Have you been told that diabetes was affecting your eyes?" Eight of 38 (21%) who confirmed that they had been told that diabetes was affecting their eyes responded "no" to the question, "Do you think that diabetes is affecting your eyes?" However, the answers to these questions did not distinguish between patients who obtained or did not obtain recommended care.

CONCLUSIONS

Interventions to increase the proportion of Navajo Indians with diabetic retinopathy who receive appropriate ophthalmologic care must address the issue of transportation.

Mastitis in a large, zero-grazed dairy herd

A zero-grazed herd of approximately 400 cows had a significant mastitis problem associated with Escherichia coli and Streptococcus uberis during a study over three and a half years. Dry cow therapy and post-milk teat dipping effectively controlled staphylococci and the bulk milk cell count averaged less than 400 X 10(3) cells/ml, but over 1800 clinical cases of mastitis occurred over this period, 32 per cent of which were associated with E coli and 25 per cent with Str uberis. Only 8 per cent of the cases associated with E coli showed obvious systemic disturbance and 75 per cent were cured following penicillin and streptomycin treatment. The incidence was highest during spring and summer when the housed cows were dirtiest. Gross teat-end contamination came mainly from sources other than cubicle bedding, and changing the bedding from sawdust to sand did not alter the incidence of clinical mastitis. It was not possible to maintain adequate cleanliness either inside or outside the parlour, nor maintain a trouble-free milking apparatus. The costs of mastitis in this herd during one year are calculated.