Molecular mechanism of ethylene stimulation of latex yield in rubber tree (Hevea brasiliensis) revealed by de novo sequencing and transcriptome analysis

Differential expression of unigenes involved in hormone signaling in E8 and E24 compared to C samples of Hevea brasiliensis. Ethylene signalling pathway: ETR1: ETHYLENE RESPONSE 1; CTR1: CONSTITUTIVE TRIPLE RESPONSE 1; EIN2: ETHYLENE INSENSITIVE 2; EIN3: ETHYLENE INSENSITIVE 3; ERF1/2: ETHYLENE RESPONSE FACTOR 1/2; EBF1/2: EIN3 binding F-Box protein 1/2; BR signaling pathway: BRI1: Brassinosteroid-Insensitive 1; BAK1: BRI1-associated kinase 1; BKI1: BRI1 KINASE INHIBITOR 1; BSK: BR SIGNALING KINASE; BSU1: bri1 SUPPRESSOR 1; BIN2: BRASSINOSTEROID-INSENSITIVE 2; BZR1/2: BRASSINAZOLE RESISTANT 1/2; TCH: TOUCH genes; CYCD3: CYCLIN D3; GA signaling pathway: GID1: GIBBERELLIN INSENSITIVE DWARF 1; GID2: GIBBERELLIN INSENSITIVE DWARF 2; DELLAs: DELLA growth inhibitors; TF: transcriptional factor; Auxin signaling pathway: AUX1: AUXIN1; TIR1: TRANSPORT INHIBITOR RESPONSE 1; IAA: INDOLE ACETIC ACID; ARF: AUXIN RESPONSE FACTOR; SAUR: Small Auxin-Up RNA; G10H: geraniol 10-hydroxylase gene; Cytokinin signaling pathway: CRE1: CYTOKININ RESPONSE 1; AHP: histidine phosphotransfer protein; B-ARR: type-B response regulator (ARR); A-ARR: type-A response regulator (ARR); SA signalling pathway: NPR1: Non-expressor of pathogenesis-related genes 1; TGA: the bZIP transcription factors; PR1: pathogenesis related protein 1; JA signaling pathway: JAR1: JASMONATES RESISTANT 1; JA-Ile: jasmonoyl isoleucine; JAZ: Jasmonate ZIM-domain-containing protein; MYC2: a basic helix-loop-helix (bHLH) transcription factor; ORCA3: Octadecanoid-derivative Responsive Catharanthus AP2-domain gene; ABA signalling pathway: PYR1/PYLs: Pyrabactin Resistance Protein1/PYR-Like proteins; PP2Cs: protein phosphatases which fall under the category of type 2C; SnRK2: SNF1 (Sucrose-Nonfermenting Kinase1)-related protein kinase 2: ABF: ABA responsive element (ABRE) binding factors. Cells with gray border lines in the upper rows represent differentially expressed unigenes in E8 compared to C and cells with green border lines in the lower rows represent differentially expressed unigenes in E24 compared to C. Relative levels of expression are showed by a color gradient from low (blue) to high (red). (JPG 249 kb

Vertices with the Second Neighborhood Property in Eulerian Digraphs

The Second Neighborhood Conjecture states that every simple digraph has a vertex whose second out-neighborhood is at least as large as its first out-neighborhood, i.e. a vertex with the Second Neighborhood Property. A cycle intersection graph of an even graph is a new graph whose vertices are the cycles in a cycle decomposition of the original graph and whose edges represent vertex intersections of the cycles. By using a digraph variant of this concept, we prove that Eulerian digraphs which admit a simple dicycle intersection graph have not only adhere to the Second Neighborhood Conjecture, but have a vertex of minimum outdegree that has the Second Neighborhood Property.Comment: fixed an error in an earlier version and made structural change

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN

A Regression Model to Predict Augmented Renal Clearance in Critically Ill Obstetric Patients and Effects on Vancomycin Treatment

Background: Augmented renal clearance (ARC) risk factors and effects on vancomycin (VCM) of obstetric patients were possibly different from other populations based on pathophysiological characteristics. Our study was to establish a regression model for prediction of ARC and analyze the effects of ARC on VCM treatment in critically ill obstetric patients.Methods: We retrospectively included 427 patients, grouped into ARC and non-ARC patients. Logistic regression analysis was used to analyze the factors related to ARC. Receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of the model for ARC. Patients who received VCM therapy were collected. The published VCM population pharmacokinetic (PPK) model was used to calculate pharmacokinetic parameters. A linear regression analysis was made between the predicted and measured concentrations.Results: Of the 427 patients, ARC was present in 201 patients (47.1%). The independent risk factors of ARC were heavier, greater gestational age, higher albumin level, fewer caesarean section, severe preeclampsia and vasoactive drug; more infection, hypertriglyceridemia and acute pancreatitis. We established the above nine-variable prediction regression model and calculated the predicted probability. ROC curve showed that the predicted probability of combined weight, albumin and gestational age had better sensitivity (70.0%) and specificity (89.8%) as well as the maximal area under the curve (AUC, AUC = 0.863). 41 cases received VCM; 21 cases (51.2%) had ARC. The initial trough concentration in ARC patients was lower than in non-ARC patients (7.9 ± 3.2 mg/L vs 9.5 ± 3.3 mg/L; p = 0.033). Comparing the predicted trough concentration of two published VCM PPK models with the measured trough concentration, correlation coefficients (r) were all more than 0.8 in the ARC group and non-ARC group. AUC was significantly decreased in the ARC group (p = 0.003; p = 0.013), and clearance (CL) increased in the ARC group (p < 0.001; p = 0.008) when compared with the non-ARC group.Conclusion: ARC is a common state in critically ill obstetric patients. The regression model of nine variables had high predictive value for predicting ARC. The published VCM PPK models had good predictive performance for predicting trough concentrations of obstetric patients. Pharmacokinetic parameters of VCM are different in ARC obstetric patients, which results in enhanced VCM clearance and decreased trough concentration

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties