Epigenomic regulation of heart failure: integrating histone marks, long noncoding RNAs, and chromatin architecture.

Epigenetic processes are known to have powerful roles in organ development across biology. It has recently been found that some of the chromatin modulatory machinery essential for proper development plays a previously unappreciated role in the pathogenesis of cardiac disease in adults. Investigations using genetic and pharmacologic gain- and loss-of-function approaches have interrogated the function of distinct epigenetic regulators, while the increased deployment of the suite of next-generation sequencing technologies have fundamentally altered our understanding of the genomic targets of these chromatin modifiers. Here, we review recent developments in basic and translational research that have provided tantalizing clues that may be used to unlock the therapeutic potential of the epigenome in heart failure. Additionally, we provide a hypothesis to explain how signal-induced crosstalk between histone tail modifications and long non-coding RNAs triggers chromatin architectural remodeling and culminates in cardiac hypertrophy and fibrosis

Recent advances in understanding anorexia nervosa.

Anorexia nervosa is a complex psychiatric illness associated with food restriction and high mortality. Recent brain research in adolescents and adults with anorexia nervosa has used larger sample sizes compared with earlier studies and tasks that test specific brain circuits. Those studies have produced more robust results and advanced our knowledge of underlying biological mechanisms that may contribute to the development and maintenance of anorexia nervosa. It is now recognized that malnutrition and dehydration lead to dynamic changes in brain structure across the brain, which normalize with weight restoration. Some structural alterations could be trait factors but require replication. Functional brain imaging and behavioral studies have implicated learning-related brain circuits that may contribute to food restriction in anorexia nervosa. Most notably, those circuits involve striatal, insular, and frontal cortical regions that drive learning from reward and punishment, as well as habit learning. Disturbances in those circuits may lead to a vicious cycle that hampers recovery. Other studies have started to explore the neurobiology of interoception or social interaction and whether the connectivity between brain regions is altered in anorexia nervosa. All together, these studies build upon earlier research that indicated neurotransmitter abnormalities in anorexia nervosa and help us develop models of a distinct neurobiology that underlies anorexia nervosa

Recovery Guarantees for Quadratic Tensors with Limited Observations

We consider the tensor completion problem of predicting the missing entries of a tensor. The commonly used CP model has a triple product form, but an alternate family of quadratic models which are the sum of pairwise products instead of a triple product have emerged from applications such as recommendation systems. Non-convex methods are the method of choice for learning quadratic models, and this work examines their sample complexity and error guarantee. Our main result is that with the number of samples being only linear in the dimension, all local minima of the mean squared error objective are global minima and recover the original tensor accurately. The techniques lead to simple proofs showing that convex relaxation can recover quadratic tensors provided with linear number of samples. We substantiate our theoretical results with experiments on synthetic and real-world data, showing that quadratic models have better performance than CP models in scenarios where there are limited amount of observations available

Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learing approaches. This enables not only better performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The memory-based attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by $6\%-7.5\%$ in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.Comment: WWW 201

Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.

ObjectivesEvaluating children's oral health status and treatment needs is challenging. We aim to build oral health assessment toolkits to predict Children's Oral Health Status Index (COHSI) score and referral for treatment needs (RFTN) of oral health. Parent and Child toolkits consist of short-form survey items (12 for children and 8 for parents) with and without children's demographic information (7 questions) to predict the child's oral health status and need for treatment.MethodsData were collected from 12 dental practices in Los Angeles County from 2015 to 2016. We predicted COHSI score and RFTN using random Bootstrap samples with manually introduced Gaussian noise together with machine learning algorithms, such as Extreme Gradient Boosting and Naive Bayesian algorithms (using R). The toolkits predicted the probability of treatment needs and the COHSI score with percentile (ranking). The performance of the toolkits was evaluated internally and externally by residual mean square error (RMSE), correlation, sensitivity and specificity.ResultsThe toolkits were developed based on survey responses from 545 families with children aged 2 to 17 y. The sensitivity and specificity for predicting RFTN were 93% and 49% respectively with the external data. The correlation(s) between predicted and clinically determined COHSI was 0.88 (and 0.91 for its percentile). The RMSEs of the COHSI toolkit were 4.2 for COHSI (and 1.3 for its percentile).ConclusionsSurvey responses from children and their parents/guardians are predictive for clinical outcomes. The toolkits can be used by oral health programs at baseline among school populations. The toolkits can also be used to quantify differences between pre- and post-dental care program implementation. The toolkits' predicted oral health scores can be used to stratify samples in oral health research.Knowledge transfer statementThis study creates the oral health toolkits that combine self- and proxy- reported short forms with children's demographic characteristics to predict children's oral health and treatment needs using Machine Learning algorithms. The toolkits can be used by oral health programs at baseline among school populations to quantify differences between pre and post dental care program implementation. The toolkits can also be used to stratify samples according to the treatment needs and oral health status

Reporting ethics committee approval and patient consent by study design in five general medical journals.

BACKGROUND: Authors are required to describe in their manuscripts ethical approval from an appropriate committee and how consent was obtained from participants when research involves human participants. OBJECTIVE: To assess the reporting of these protections for several study designs in general medical journals. DESIGN: A consecutive series of research papers published in the Annals of Internal Medicine, BMJ, JAMA, Lancet and The New England Journal of Medicine between February and May 2003 were reviewed for the reporting of ethical approval and patient consent. Ethical approval, name of approving committee, type of consent, data source and whether the study used data collected as part of a study reported elsewhere were recorded. Differences in failure to report approval and consent by study design, journal and vulnerable study population were evaluated using multivariable logistic regression. RESULTS: Ethical approval and consent were not mentioned in 31% and 47% of manuscripts, respectively. 88 (27%) papers failed to report both approval and consent. Failure to mention ethical approval or consent was significantly more likely in all study designs (except case-control and qualitative studies) than in randomised controlled trials (RCTs). Failure to mention approval was most common in the BMJ and was significantly more likely than in The New England Journal of Medicine. Failure to mention consent was most common in the BMJ and was significantly more likely than in all other journals. No significant differences in approval or consent were found when comparing studies of vulnerable and non-vulnerable participants. CONCLUSION: The reporting of ethical approval and consent in RCTs has improved, but journals are less good at reporting this information for other study designs. Journals should publish this information for all research on human participants

Computerized adaptive testing and short form development for child and adolescent oral health patient-reported outcomes measurement.

ObjectivesTo develop computerized adaptive testing (CAT) and short forms of self-report oral health measures that are predictive of both the children's oral health status index (COHSI) and the children's oral health referral recommendation (COHRR) scales, for children and adolescents, ages 8-17.Material and methodsUsing final item calibration parameters (discrimination and difficulty parameters) from the item response theory analysis, we performed post hoc CAT simulation. Items most frequently administered in the simulation were incorporated for possible inclusion in final oral health assessment toolkits, to select the best performing eight items for COHSI and COHRR.ResultsTwo previously identified unidimensional sets of self-report items consisting of 19 items for the COHSI and 22 items for the COHRR were administered through CAT resulting in eight-item short forms for both the COHSI and COHRR. Correlations between the simulated CAT scores and the full item bank representing the latent trait are r = .94 for COHSI and r = .96 for COHRR, respectively, which demonstrated high reliability of the CAT and short form.ConclusionsUsing established rigorous measurement development standards, the CAT and corresponding eight-item short form items for COHSI and COHRR were developed to assess the oral health status of children and adolescents, ages 8-17. These measures demonstrated good psychometric properties and can have clinical utility in oral health screening and evaluation and clinical referral recommendations