Methyl-CpG-binding domain (MBD) proteins play important roles in epigenetic gene regulation, and have diverse molecular, cellular, and biological functions in plants. MBD proteins have been functionally characterized in various plant species, including Arabidopsis, wheat, maize, and tomato. In rice, 17 sequenceswerebioinformatically predicted as putative MBD proteins. However, very little is known regarding the function of MBD proteins in rice.

We explored the expression patterns of the riceOsMBD family genes and identified 13 OsMBDs with active expression in various rice tissues. We further characterized the function of a rice class I MBD protein OsMBD707, and demonstrated that OsMBD707 is constitutively expressed and localized in the nucleus. Transgenic rice overexpressingOsMBD707 displayed larger tiller angles and reduced photoperiod sensitivity-delayed flowering under short day (SD) and early flowering under long day (LD). RNA-seq analysis revealed that overexpression of OsMBD707 led to reduced photoperiod sensitivity in rice and to expression changes in flowering regulator genes in the Ehd1-Hd3a/RFT1 pathway.

The results of this studysuggestedthat OsMBD707 plays important roles in rice growth and development, and should lead to further studies on the functions of OsMBD proteins in growth, development, or other molecular, cellular, and biological processes in rice.

The results of this study suggested that OsMBD707 plays important roles in rice growth and development, and should lead to further studies on the functions of OsMBD proteins in growth, development, or other molecular, cellular, and biological processes in rice.

Studies have demonstrated that heart failure (HF) patients who receive direct pharmacist input as part of multidisciplinary care have better clinical outcomes. This study evaluated/compared the difference in prescribing practices of guideline-directed medical therapy (GDMT) for chronic HF patients between two multidisciplinary clinics-with and without the direct involvement of a pharmacist.

A retrospective audit of chronic HF patients, presenting to two multidisciplinary outpatient clinics between March 2005 and January 2017, was performed; a Multidisciplinary Ambulatory Consulting Service (MACS) with an integrated pharmacist model of care and a General Cardiology Heart Failure Service (GCHFS) clinic, without the active involvement of a pharmacist.

MACS clinic patients were significantly older (80 vs. 73years, p < .001), more likely to be female (p < .001), and had significantly higher systolic (123 vs. 112mmHg, p < .001) and diastolic (67 vs. 60mmHg, p < .05) blood pressures compared to theSBP),anemia, chronic renal failure, and othercomorbidities were the main significant predictors of utilization of GDMT in a multivariate binary logistic regression.

Lower prescription rates of some medications in the pharmacist-involved multidisciplinary team were found. Careful consideration of demographic and clinical characteristics, contraindications for use of medications, polypharmacy, and underlying comorbidities is necessary to achieve best practice.

Lower prescription rates of some medications in the pharmacist-involved multidisciplinary team were found. Careful consideration of demographic and clinical characteristics, contraindications for use of medications, polypharmacy, and underlying comorbidities is necessary to achieve best practice.

One component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. OligomycinA However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a pttps//github.com/imbs-hl/MBMDRClassifieR ).

The explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

The explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

Most health surveys have experienced a decline in response rates. A structured approach to evaluate whether a decreasing – and potentially more selective – response over time biased estimated trends in health behaviours is lacking. We developed a framework to explore the role of differential non-response over time. This framework was applied to a repeated cross-sectional survey in which the response rate gradually declined.

We used data from a survey conducted biannually between 1995 and 2017 in the city of Rotterdam, The Netherlands. Information on the sociodemographic determinants of age, sex, and ethnicity was available for respondents and non-respondents. The main outcome measures of prevalence of sport participation and watching TV were only available for respondents. The framework consisted of four steps 1) investigating the sociodemographic determinants of responding to the survey and the difference in response over time between sociodemographic groups; 2) estimating variation in health behaviour oe rates may have influenced estimated trends in health behaviours. The framework outlined in this study can be used for this purpose. In our example, in spite of a major decline in response rate, there was no evidence that the risk of non-response bias increased over time.

Providing insights on non-response patterns over time is essential to understand whether declines in response rates may have influenced estimated trends in health behaviours. The framework outlined in this study can be used for this purpose. In our example, in spite of a major decline in response rate, there was no evidence that the risk of non-response bias increased over time.