Hintonlevy4093

The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI-age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability. CONCLUSIONS We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years. © The Author(s) 2020. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email journals.permissions@oup.com.In mammalian cells, distinct H3K4 methylation states are created by deposition of methyl groups by multiple complexes of histone lysine methyltransferase 2 (KMT2) family proteins. For comprehensive analyses that directly compare the catalytic properties of all six human KMT2 complexes, we employed a biochemically defined system reconstituted with recombinant KMT2 core complexes (KMT2CoreCs) containing minimal components required for nucleosomal H3K4 methylation activity. We found that each KMT2CoreC generates distinct states and different levels of H3K4 methylation, and except for MLL3 all are stimulated by H2Bub. Notably, SET1BCoreC exhibited the strongest H3K4 methylation activity and, to our surprise, did not require H2B ubiquitylation (H2Bub); in contrast, H2Bub was required for the H3K4me2/3 activity of the paralog SET1ACoreC. We also found that WDR5, RbBP5, ASH2L and DPY30 are required for efficient H3K4 methyltransferase activities of all KMT2CoreCs except MLL3, which could produce H3K4me1 in the absence of WDR5. Importantly, deletion of the PHD2 domain of CFP1 led to complete loss of the H3K4me2/3 activities of SET1A/BCoreCs in the presence of H2Bub, indicating a critical role for this domain in the H2Bub-stimulated H3K4 methylation. Collectively, our results suggest that each KMT2 complex methylates H3K4 through distinct mechanisms in which individual subunits differentially participate. © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.Retigabine is unique among anticonvulsant drugs by targeting the neuronal M-channel, which is composed of KV7.2/KV7.3 and contributes to the negative neuronal resting membrane potential. Unfortunately, retigabine causes adverse effects, which limits its clinical use. Adverse effects may be reduced by developing M-channel activators with improved KV7 subtype selectivity. Doramapimod The aim of this study was to evaluate the prospect of endocannabinoids as M-channel activators, either in isolation or combined with retigabine. Human KV7 channels were expressed in Xenopus laevis oocytes. The effect of extracellular application of compounds with different properties was studied using two-electrode voltage clamp electrophysiology. Site-directed mutagenesis was used to construct channels with mutated residues to aid in the mechanistic understanding of these effects. We find that arachidonoyl-L-serine (ARA-S), a weak endocannabinoid, potently activates the human M-channel expressed in Xenopus oocytes. Importantly, we show that ARA-S activates the M-channel via a different mechanism and displays a different KV7 subtype selectivity compared with retigabine. We demonstrate that coapplication of ARA-S and retigabine at low concentrations retains the effect on the M-channel while limiting effects on other KV7 subtypes. Our findings suggest that improved KV7 subtype selectivity of M-channel activators can be achieved through strategically combining compounds with different subtype selectivity. © 2020 Larsson et al.MOTIVATION Recent research has uncovered roles for transposable elements (TEs) in multiple evolutionary processes, ranging from somatic evolution in cancer to putatively adaptive germline evolution across species. Most models of TE population dynamics, however, have not incorporated actual genome sequence data. The effect of site integration preferences of specific TEs on evolutionary outcomes, and the effects of different selection regimes on TE dynamics in a specific genome, are unknown. We present a stochastic model of LINE-1 (L1) transposition in human cancer. This system was chosen because the transposition of L1 elements is well understood, the population dynamics of cancer tumors has been modeled extensively, and the role of L1 elements in cancer progression has garnered interest in recent years. RESULTS Our model predicts that L1 retrotransposition can play either advantageous or deleterious roles in tumor progression, depending on the initial lesion size, L1 insertion rate, and tumor driver genes. Small changes in the retrotransposition rate or set of driver tumor suppressor genes (TSGs) were observed to alter the dynamics of tumorigenesis. We found high variation in the density of L1 target sites across human protein-coding genes. We also present an analysis, across three cancer types, of the frequency of homozygous TSG disruption in wild-type hosts compared to those with an inherited driver allele. AVAILABILITY Source code is available at https//github.com/atallah-lab/neoplastic-evolution. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION Single-cell RNA sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of "drop-out" events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this paper, we present a novel Single-Cell RNA-seq Drop-Out Correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. RESULTS scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification, and differential expression detection in scRNA-seq data.