Representative significant SNPs in the top 10 modules identified by mgRF in the combined dataset.

<p>Representative significant SNPs in the top 10 modules identified by mgRF in the combined dataset.</p

Performance comparison of Group lasso, Elastic net, Support Vector Regressor (SVR), conventional RF, and mgRF.

<p>Average RMSEs of these methods in a 10-fold cross-validation on the mouse weight dataset. Even though the standard deviation (shown as error bar) of RMSEs among 10 trials of 10-fold cross-validation is relatively high due to the small number of samples, if we compare the RMSEs of different methods using the same set of training samples, the improvement is evident (mgRF vs conventional RF, one-tail paired t-test <i>p</i>-value<3.83×10⁻¹⁶).</p

Network structure of 30 top ranked genes from mgRF.

<p>(<b>A</b>) The size of nodes represents the relative cVIs from mgRF. (<b>B</b>) The size of nodes represents the relative VIs from the conventional RF algorithm.</p

LOD scores from QTL mapping versus VI scores from mgRF.

<p>The black curves represent the LOD scores of a single marker genome scan in conventional QTL analysis. The blue bars represent the cVI scores of genetic markers output by mgRF.</p

Flow chart of mgRF algorithm.

<p>(<b>A</b>) A subgraph of a constructed correlation network where a node indicates a variable and an edge represents the correlation between two variables. (<b>B</b>) Identification of network modules using HQcut, where different colors encode different modules. (<b>C</b>) Iterative construction of RFs. The panels on the left show multiple iterations of RFs and the panels on the right illustrate the sampling scheme in each node during of tree construction. <i>mtry</i> (<i>k</i>) candidate variables are sampled using a <i>two-stage candidate variable sampling</i> procedure, where a subset of modules (e.g. the blue, green, orange modules) is first sampled and then one representative variable from each module is selected. In the first iteration (iteration 0), all modules and variables have the same weights. At the end of one iteration, module and variable importance are re-estimated. In the figure, the importance of variables is encoded as the size of node. Then modules and variables are sampled by their corresponding weights using <i>modified weighted sampling</i>. The best splitting variable and value at each node in the tree in the left panel are selected from <i>mtry</i> (<i>k</i>) candidate variables.</p

Representative significant genes in the top 10 modules identified by mgRF in the combined dataset.^*

<p>(*Note: Top genes ranked by mgRF in the expression-only data were the same as the one above with slightly different cVIs).</p

Summary of prediction errors of mgRF using three types of data.

<p>(<b>A</b>) Boxplot of prediction errors in root-mean-square error (RMSE) in 10 trials of 10-folds cross-validation. Scatter plots of (<b>B</b>) genotype-only (x-axis) vs. expression-only (y-axis), (<b>C</b>) genotype-only vs. combined, and (<b>D</b>) expression-only vs. combined. The dashed diagonal lines (x = y) indicate points of equal RMSE. Given two vectors, the <i>p</i>-value of one-tail paired <i>t</i>-test evaluate if the distribution of one vector (100 RMSE values for 10 trials of 10-fold cross validation) is statistically smaller than the other one.</p

Statistical inference methods for cumulative incidence function curves at a fixed point in time

Competing risks data arise frequently in clinical trials. When the proportional subdistribution hazard assumption is violated or two cumulative incidence function (CIF) curves cross, researchers may be interested in focusing on a comparison of clinical utility at some fixed time points rather than comparing the overall treatment effects. This article extends a series of tests constructed based on a pseudo-value regression technique or different transformation functions for CIFs and their variances based on Gaynor’s or Aalen’s work, and the differences among CIFs at a given time point are compared.</p

DataSheet_1_ALA induces stomatal opening through regulation among PTPA, PP2AC, and SnRK2.6.zip

5-Aminolevulinic acid (ALA), as a new natural plant growth regulator, has been proved to regulate protein phosphatase 2A (PP2A) activity to promote stomatal opening in apple (Malus domestica) leaves. However, the molecular mechanisms underlying remain unclear. Here, we cloned and transformed MdPTPA, MdPP2AC, and MdSnRK2.6 of apple into tobaccos (Nicotiana tabacum) and found that over-expression (OE)-MdPTPA or OE-MdPP2AC promoted stomatal aperture while OE-MdSnRK2.6 induced stomatal closure under normal or drought condition. The Ca2+ and H2O2 levels in the guard cells of OE-MdPTPA and OE-MdPP2AC was decreased but flavonols increased, and the results in OE-SnRK2.6 was contrary. Exogenous ALA stimulated PP2A activity but depressed SnRK2.6 activity in transgenic tobaccos, leading to less Ca2+, H2O2 and more flavonols in guard cells, and consequently stomatal opening. OE-MdPTPA improved stomatal opening and plant growth but impaired drought tolerance, while OE-MdSnRK2.6 improved drought tolerance but depressed the leaf Pn. Only OE-MdPP2AC improved stomatal opening, leaf Pn, plant growth, as well as drought tolerance. These suggest that the three genes involved in ALA-regulating stomatal movement have their respective unique biological functions. Yeast two-hybrid (Y2H) assays showed that MdPP2AC interacted with MdPTPA or MdSnRK2.6, respectively, but no interaction of MdPTPA with MdSnRK2.6 was found. Yeast three-hybrid (Y3H) assay showed that MdPTPA promoted the interactions between MdPP2AC and MdSnRK2.6. Therefore, we propose a regulatory module of PTPA-PP2AC-SnRK2.6 that may be involved in mediating the ALA-inducing stomatal aperture in green plants.</p

TRIM44 links the UPS to SQSTM1/p62-dependent aggrephagy and removing misfolded proteins

Until recently, the ubiquitin-proteasome system (UPS) and macroautophagy/autophagy were considered to be two independent systems that target proteins for degradation by proteasomes or via lysosomes, respectively. Here, we report that TRIM44 (tripartite motif containing 44) is a novel link that connects the UPS system with the autophagy degradation pathway. Suppressing the UPS degradation pathway leads to TRIM44 upregulation, which further promotes aggregated protein clearance through the binding of K48 ubiquitin chains on proteins. TRIM44 expression activates autophagy via promoting SQSTM1/p62 oligomerization, which rapidly increases the rate of aggregate protein removal. Overall, our data reveal that TRIM44 is a newly identified link between the UPS system and the autophagy pathway. Delineating the cross-talk between these two degradation pathways may reveal new mechanisms of targeting aggregate-prone diseases, such as cancer and neurodegenerative disease. Abbreviations: 3-MA: 3-methyladenine; ACTB: actin beta; ATG5: autophagy related 5; BB: B-box domain; BECN1: beclin1; BM: bone marrow; CC: coiled-coil domain; CFTR: cystic fibrosis transmembrane conductance regulator; CON: control; CQ: chloroquine; DOX: doxycycline; DSP: dithiobis(succinimidly propionate); ER: endoplasmic reticulum; FI: fluorescence intensity; FL: full length; HIF1A/HIF-1#x3B1;: hypoxia inducible factor 1 subunit alpha; HSC: hematopoietic stem cells; HTT: huntingtin; KD: knockdown; KD-CON: knockdown construct control; MM: multiple myeloma; MTOR: mechanistic target of rapamycin kinase; NP-40: nonidet P-40; NFE2L2/NRF2: nuclear factor, erythroid 2 like 2; OE: overexpression; OE-CON: overexpression construct control; PARP: poly (ADP-ribose) polymerase; SDS: sodium dodecyl sulfate; SQSTM1/p62: sequestosome 1; Tet-on: tetracycline; TRIM44: tripartite motif containing 44; UPS: ubiquitin-proteasome system; ZF: zinc-finger</p