Can the Convective Temperature from the 12UTC Sounding be a Good Predictor for the Maximum Temperature, During the Summer Months?

Several forecasting techniques use soundings to get the value of the variable being forecasted. This study examines the validity of a using the convective temperature to forecast for the maximum temperature, while comparing it to other forecasting techniques that use soundings. These include adding 13 degrees to 850mb temperature and using the forecasted high that is included in the sounding analysis. This study also examined where the convective temperature matches the observed high temperature. To do this, most of the information was obtained from the Iowa State University Meteorology Archive and National Weather Service’s archived data. Days were chosen to include at least one day a week for the last week of May and the first week of September. The data points included the convective temperature from the 12UTC, the 850mb temperature, the forecasted high, cloud cover, and the month, region and latitude that the sounding was taken in. The difference was taken between the variable temperatures and the observed maximum temperature. The average was taken of these differences and were taken against each other and against the other variables: latitude, region, month, and cloud cover. Statically analysis was performed to determine how well the variables are correlated and their statistical significance Region and latitude showed the at least some correlation, with latitude being the best. Lower latitudes had the smallest average temperature difference. An additional 20 cases were added to determine how well this proposed convective temperature forecasting method performs in the lower latitudes

Ins and Outs - SuperCollider and external devices

Baalman M, Kersten S, Bovermann T. Ins and Outs - SuperCollider and external devices. In: Wilson S, Cottle D, Collins N, eds. The SuperCollider Book. Cambridge: MIT Press; 2011: 105-126

Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?

BACKGROUND: Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists. OBJECTIVES: We aimed to develop machine learning and deep learning models to diagnose PLN p.Arg14del cardiomyopathy using ECGs and evaluate their accuracy compared to an expert cardiologist. METHODS: We included 155 adult PLN mutation carriers and 155 age- and sex-matched control subjects. Twenty-one PLN mutation carriers (13.4%) were classified as symptomatic (symptoms of heart failure or malignant ventricular arrhythmias). The data set was split into training and testing sets using 4-fold cross-validation. Multiple models were developed to discriminate between PLN mutation carriers and control subjects. For comparison, expert cardiologists classified the same data set. The best performing models were validated using an external PLN p.Arg14del mutation carrier data set from Murcia, Spain (n = 50). We applied occlusion maps to visualize the most contributing ECG regions. RESULTS: In terms of specificity, expert cardiologists (0.99) outperformed all models (range 0.53-0.81). In terms of accuracy and sensitivity, experts (0.28 and 0.64) were outperformed by all models (sensitivity range 0.65-0.81). T-wave morphology was most important for classification of PLN p.Arg14del carriers. External validation showed comparable results, with the best model outperforming experts. CONCLUSION: This study shows that machine learning can outperform experienced cardiologists in the diagnosis of PLN p.Arg14del cardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis

Maternal and neonatal outcomes in women with history of coronary artery disease

Background Pregnancy outcomes in women with pre-existing coronary artery disease (CAD) are poorly described. There is a paucity of data therefore on which to base clinical management to counsel women, with regard to both maternal and neonatal outcomes. Method We conducted a retrospective multicentre study of women with established CAD delivering at 16 UK specialised cardiac obstetric clinics. We included pregnancies of 24 weeks’ gestation or more, delivered between January 1998 and October 2018. Data were collected on maternal cardiovascular, obstetric and neonatal events. Results 79 women who had 92 pregnancies (94 babies including two sets of twins) were identified. 35.9% had body mass index >30% and 24.3% were current smokers. 18/79 (22.8%) had prior diabetes, 27/79 (34.2%) had dyslipidaemia and 21/79 (26.2%) had hypertension. The underlying CAD was due to atherosclerosis in 52/79 (65.8%), spontaneous coronary artery dissection (SCAD) in 11/79 (13.9%), coronary artery spasm in 7/79 (8.9%) and thrombus in 9/79 (11.4%). There were six adverse cardiac events (6.6% event rate), one non-ST elevation myocardial infarction at 23 weeks’ gestation, two SCAD recurrences (one at 26 weeks’ gestation and one at 9 weeks’ postpartum), one symptomatic deterioration in left ventricular function and two women with worsening angina. 14% of women developed pre-eclampsia, 25% delivered preterm and 25% of infants were born small for gestational age. Conclusion Women with established CAD have relatively low rates of adverse cardiac events in pregnancy. Rates of adverse obstetric and neonatal events are greater, highlighting the importance of multidisciplinary care