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Aims: Both left ventricular (LV) diastolic dysfunction (LVDD) and hypertrophy (LVH) as assessed by echocardiography are independent prognostic markers of future cardiovascular events in the community. However, selective screening strategies to identify individuals at risk who would benefit most from cardiac phenotyping are lacking. We, therefore, assessed the utility of several machine learning (ML) classifiers built on routinely measured clinical, biochemical, and electrocardiographic features for detecting subclinical LV abnormalities., Methods and Results: We included 1407 participants (mean age, 51 years, 51% women) randomly recruited from the general population. We used echocardiographic parameters reflecting LV diastolic function and structure to define LV abnormalities (LVDD, n = 252; LVH, n = 272). Next, four supervised ML algorithms (XGBoost, AdaBoost, Random Forest (RF), Support Vector Machines, and Logistic regression) were used to build classifiers based on clinical data (67 features) to categorize LVDD and LVH. We applied a nested 10-fold cross-validation set-up. XGBoost and RF classifiers exhibited a high area under the receiver operating characteristic curve with values between 86.2% and 88.1% for predicting LVDD and between 77.7% and 78.5% for predicting LVH. Age, body mass index, different components of blood pressure, history of hypertension, antihypertensive treatment, and various electrocardiographic variables were the top selected features for predicting LVDD and LVH., Conclusion: XGBoost and RF classifiers combining routinely measured clinical, laboratory, and electrocardiographic data predicted LVDD and LVH with high accuracy. These ML classifiers might be useful to pre-select individuals in whom further echocardiographic examination, monitoring, and preventive measures are warranted., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions, please email: journals.permissions@oup.com.)

Aims: There is a need for better phenotypic characterization of the asymptomatic stages of cardiac maladaptation. We tested the hypothesis that an unsupervised clustering analysis utilizing echocardiographic indexes reflecting left heart structure and function could identify phenotypically distinct groups of asymptomatic individuals in the general population., Methods and Results: We prospectively studied 1407 community-dwelling individuals (mean age, 51.2 years; 51.1% women), in whom we performed clinical and echocardiographic examination at baseline and collected cardiac events on average 8.8 years later. Cardiac phenotypes that were correlated at r  > 0.8 were filtered, leaving 21 echocardiographic features, and systolic blood pressure for phenogrouping. We employed hierarchical and Gaussian mixture model-based clustering. Cox regression was used to demonstrate the clinical validity of constructed phenogroups. Unsupervised clustering analyses classified study participants into three distinct phenogroups that differed markedly in echocardiographic indexes. Indeed, cluster 3 had the worst left ventricular (LV) diastolic function (i.e. lowest e ' velocity and left atrial (LA) reservoir strain, highest E / e ', and LA volume index) and LV remodelling. The phenogroups were also different in cardiovascular risk factor profiles. We observed increase in the risk for incidence of adverse events across phenogroups. In the third phenogroup, the multivariable adjusted risk was significantly higher than the average population risk for major cardiovascular events (51%, P  = 0.0028)., Conclusion: Unsupervised learning algorithms integrating routinely measured cardiac imaging and haemodynamic data can provide a clinically meaningful classification of cardiac health in asymptomatic individuals. This approach might facilitate early detection of cardiac maladaptation and improve risk stratification., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Profile hidden Markov models (profile HMMs) are known to efficiently predict whether an amino acid (AA) sequence belongs to a specific protein family. Profile HMMs can also be used to search for protein domains in genome sequences. In this case, HMMs are typically learned from AA sequences and then used to search on the six-frame translation of nucleotide (NT) sequences. However, this approach demands additional processing of the original data and search results. Here, we propose an alternative and more direct method which converts an AA alignment into an NT one, after which an NT-based HMM is trained to be applied directly on a genome., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Motivation: Identification of conserved motifs in biological sequences is crucial to unveil common shared functions. Many tools exist for motif identification, including some that allow degenerate positions with multiple possible nucleotides or amino acids. Most efficient methods available today search conserved motifs in a set of sequences, but do not check for their specificity regarding to a set of negative sequences., Results: We present a tool to identify degenerate motifs, based on a given classification of amino acids according to their physico-chemical properties. It returns the top K motifs that are most frequent in a positive set of sequences involved in a biological process of interest, and absent from a negative set. Thus, our method discovers discriminative motifs in biological sequences that may be used to identify new sequences involved in the same process. We used this tool to identify candidate effector proteins secreted into plant tissues by the root knot nematode Meloidogyne incognita. Our tool identified a series of motifs specifically present in a positive set of known effectors while totally absent from a negative set of evolutionarily conserved housekeeping proteins. Scanning the proteome of M. incognita, we detected 2579 proteins that contain these specific motifs and can be considered as new putative effectors., Availability and Implementation: The motif discovery tool and the proteins used in the experiments are available at http://dtai.cs.kuleuven.be/ml/systems/merci.

Lethal lesions after ionizing radiation are thought to be mainly unrepaired or misrepaired DNA double-strand breaks, ultimately leading to lethal chromosome aberrations. However, studies with radioprotectors and repair inhibitors indicate that single-strand breaks, damaged nucleotides or abasic sites can also influence cell survival. This paper reports on studies to further define the role of base damage and base excision repair on the radiosensitivity of human cells. We retrovirally transduced human tumor cells with a dominant negative form of DNA polymerase beta, comprising the 14 kDa DNA-binding domain of DNA polymerase beta but lacking polymerase function. Radiosensitization of two human carcinoma cell lines, A549 and SQD9, was observed, achieving dose enhancement factors of 1.5-1.7. Sensitization was dependent on expression level of the dominant negative and was seen in both single cell clones and in unselected virally transduced populations. Sensitization was not due to changes in cell cycle distribution. Little or no sensitization was seen in G(1)-enriched populations, indicating cell cycle specificity for the observed sensitization. These results contrast with the lack of effect seen in DNA polymerase beta knockout cells, suggesting that polDN also inhibits the long patch, DNA polymerase beta-independent repair pathway. These data demonstrate an important role for BER in determining sensitivity to ionizing radiation and might help identify targets for radiosensitizing tumor cells.