BackgroundCurrent approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction.MethodsProspective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins).Findings24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm.ConclusionsMachine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

Post-translational histone modifications are known to be altered in cancer cells, and loss of selected histone acetylation and methylation marks has recently been shown to predict patient outcome in human carcinoma. Immunohistochemistry was used to detect a series of histone lysine acetylation (H3K9ac, H3K18ac, H4K12ac, and H4K16ac), lysine methylation (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) marks in a well-characterized series of human breast carcinomas (n = 880). Tissue staining intensities were assessed using blinded semiquantitative scoring. Validation studies were done using immunofluorescence staining and Western blotting. Our analyses revealed low or absent H4K16ac in the majority of breast cancer cases (78.9%), suggesting that this alteration may represent an early sign of breast cancer. There was a highly significant correlation between histone modifications status, tumor biomarker phenotype, and clinical outcome, where high relative levels of global histone acetylation and methylation were associated with a favorable prognosis and detected almost exclusively in luminal-like breast tumors (93%). Moderate to low levels of lysine acetylation (H3K9ac, H3K18ac, and H4K12ac), lysine (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) were observed in carcinomas of poorer prognostic subtypes, including basal carcinomas and HER-2-positive tumors. Clustering analysis identified three groups of histone displaying distinct pattern in breast cancer, which have distinct relationships to known prognostic factors and clinical outcome. This study identifies the presence of variations in global levels of histone marks in different grades, morphologic types, and phenotype classes of invasive breast cancer and shows that these differences have clinical significance. [Cancer Res 2009;69(9):3802-9]

Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAAbased reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 908 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 408 to the horizontal. We hypothesize roots use a "tipping point" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.environmental sensing | systems biology R oot gravitropism has fascinated researchers since Knight (1) and Darwin (2). More recently, reorientation of Arabidopsis seedlings has been shown to trigger the asymmetric release of the growth regulator auxin from gravity-sensing columella cells at the root apex (Fig. 1A) (3-5). The resulting lateral auxin gradient is hypothesized to drive a differential growth response, where cell expansion on the lower side of the elongation zone is reduced relative to the upper side, causing the root to bend downward (6-8). Despite representing one of the oldest hypotheses in plant biology, key questions about auxin-regulated root gravitropism remain to be experimentally determined. How rapidly does the lateral auxin gradient form? Is this timescale consistent with the theory that auxin redistribution drives root bending? How long does the lateral auxin gradient persist? What triggers auxin redistribution to return to equal levels?Our understanding of gravity-induced auxin redistribution has been limited by the tools available to monitor auxin concentrations at high spatiotemporal resolution. Currently, the most widely used tools to follow auxin distribution in tissues are auxin-inducible reporters such as DR5::GFP (3, 4). However, as an output of the auxin response pathway (Fig. 1B), the activity of the DR5 reporter does not directly relate to endogenous auxin abundance, but also depends on additional parameters including local auxin signaling capacities and rates of transcription and translation (Fig. 1B). In practice, these intermediate processes confer a time delay of ∼1.5-2 h between changes in auxin abundance and DR5 reporter activity (9, 4), making it difficult to quantify the speed and magnitude of fold changes in auxin distribution during a root gravitropic response.Auxi...

Objectives To address the practical problems of routine umbilical cord blood sampling, to determine the ranges for pH, pCO2 and base deficit and to examine the relationships of these parameters between cord vessels. Design An observational study of umbilical cord artery and vein blood gas results. Setting A large district general hospital in the UK. Subject One thousand nine hundred and forty‐two cord results from 2013 consecutive pregnancies of 34 weeks or more gestation, monitored by fetal scalp electrode during labour. Results Only 1448 (74.6%) of the 1942 supposedly paired samples had validated pH and pCO2data both from an artery and the vein; 54 (2.8%) had only one blood sample available, 90 (4.6%) had an error in the pH or pCO2 of one vessel and in 350 (18%) pairs the differences between vessels indicated that they were not sampled from artery and vein as intended. Only 60% of the cases with an arterial pH less than 7.05 had evidence of a metabolic acidosis (base deficit in the extracellular fluid 10 mmol/1 or more). Of all the cases, 2.5% had a venous‐arterial pH difference greater than 0.22 units. Conclusions Both artery and vein cord samples must be taken and the results screened to ensure separate vessels have been sampled. Interpretation of the results requires the examination of pCO2 and base deficit of the extracellular fluid from each vessel as well as the pH. Confusion about the value of cord gas measurements may be due to the use of erroneous data and inadequate definitions of acidosis which do not differentiate between respiratory and metabolic components.

Abstract-In this paper a new approach is presented to model interval-based data using Fuzzy Sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or multiple survey participants over single or repeated surveys can be modelled using type-1, interval type-2, or general type-2 FSs based on zSlices. The proposed approach is designed to minimise any loss of information when transferring the intervalbased data into FS models, and to avoid, as much as possible assumptions about the distribution of the data. Furthermore, our approach does not rely on data pre-processing or outlier removal which can lead to the elimination of important information. Different types of uncertainty contained within the data, namely intra-and inter-source uncertainty, are identified and modelled using the different degrees of freedom of type-2 FSs, thus providing a clear representation and separation of these individual types of uncertainty present in the data. We provide full details of the proposed approach, as well as a series of detailed examples based on both real-world and synthetic data. We perform comparisons with analogue techniques to derive fuzzy sets from intervals, namely the Interval Approach (IA) and the Enhanced Interval Approach (EIA) and highlight the practical applicability of the proposed approach.