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PurposeTo compare the ability of diffusion parameters obtained by stretched-exponential and kurtosis models of diffusion-weighted imaging (DWI) to distinguish between patients with primary aldosteronism (PA) and healthy controls (HCs) in renal assessment.Materials and methodsA total of 44 participants (22 patients and 22 HCs) underwent renal MRI with an 11 b-value DWI sequence and a 3 b-value diffusion kurtosis imaging (DKI) sequence from June 2021 to April 2022. Binary logistic regression was used to construct regression models combining different diffusion parameters. Receiver-operating characteristic (ROC) curve analysis and comparisons were used to evaluate the ability of single diffusion parameters and combined diffusion models to distinguish between the two groups.ResultsA total of six diffusion parameters (including the cortical anomalous exponent term [α_Cortex], medullary fractional anisotropy [FA_Medulla], cortical FA [FA_Cortex], cortical axial diffusivity [Da_Cortex], medullary mean diffusivity [MD_Medulla] and medullary radial diffusivity [Dr_Medulla]) were included, and 10 regression models were studied. The area under the curve (AUC) of Dr_Medulla was 0.855, comparable to that of FA_Cortex and FA_Medulla and significantly higher than that of α_Cortex, Da_Cortex and MD_Medulla. The AUC of the Model_all parameters was 0.967, comparable to that of Model_FA (0.946) and Model_DKI (0.966) and significantly higher than that of the other models. The sensitivity and specificity of Model_all parameters were 87.2% and 95%, respectively.ConclusionThe Model_all parameters, Model_FA and Model_DKI were valid for differentiating between PA patients and HCs with similar differentiation efficacy and were superior to single diffusion parameters and other models.

Leaf senescence is an important biological process that contributes to grain yield in crops. To study the molecular mechanisms underlying natural leaf senescence, we harvested three different developmental ear leaves of maize, mature leaves (ML), early senescent leaves (ESL), and later senescent leaves (LSL), and analyzed transcriptional changes using RNA-sequencing. Three sets of data, ESL vs. ML, LSL vs. ML, and LSL vs. ESL, were compared, respectively. In total, 4,552 genes were identified as differentially expressed. Functional classification placed these genes into 18 categories including protein metabolism, transporters, and signal transduction. At the early stage of leaf senescence, genes involved in aromatic amino acids (AAAs) biosynthetic process and transport, cellular polysaccharide biosynthetic process, and the cell wall macromolecule catabolic process, were up-regulated. Whereas, genes involved in amino acid metabolism, transport, apoptosis, and response to stimulus were up-regulated at the late stage of leaf senescence. Further analyses reveals that the transport-related genes at the early stage of leaf senescence potentially take part in enzyme and amino acid transport and the genes upregulated at the late stage are involved in sugar transport, indicating nutrient recycling mainly takes place at the late stage of leaf senescence. Comparison between the data of natural leaf senescence in this study and previously reported data for Arabidopsis implies that the mechanisms of leaf senescence in maize are basically similar to those in Arabidopsis. A comparison of natural and induced leaf senescence in maize was performed. Athough many basic biological processes involved in senescence occur in both types of leaf senescence, 78.07% of differentially expressed genes in natural leaf senescence were not identifiable in induced leaf senescence, suggesting that differences in gene regulatory network may exist between these two leaf senescence programs. Thus, this study provides important information for understanding the mechanism of leaf senescence in maize.

It is difficult to derive all qualitative proteomic and metabolomic experimental data in male (pollen tube) and female (pistil) reproductive tissues during pollination because of the limited sensitivity of current technology. In this study, genome-scale enzyme correlation network models for plants (Arabidopsis/maize) were constructed by analyzing the enzymes and metabolic routes from a global perspective. Then, we developed a data-driven computational pipeline using the "guilt by association" principle to analyze the transcriptional coexpression profiles of enzymatic genes in the consecutive steps for metabolic routes in the fast-growing pollen tube and stigma during pollination. The analysis identified an inferred pattern of pollen tube-stigma ethanol coupling. When the pollen tube elongates in the transmitting tissue (TT) of the pistil, this elongation triggers the mobilization of energy from glycolysis in the TT cells of the pistil. Energy-rich metabolites (ethanol) are secreted that can be taken up by the pollen tube, where these metabolites are incorporated into the pollen tube's tricarboxylic acid (TCA) cycle, which leads to enhanced ATP production for facilitating pollen tube growth. In addition, our analysis also provided evidence for the cooperation of kaempferol, dTDP-alpha-L-rhamnose and cell-wall-related proteins; phosphatidic-acid-mediated Ca2+ oscillations and cytoskeleton; and glutamate degradation IV for γ-aminobutyric acid (GABA) signaling activation in Arabidopsis and maize stigmas to provide the signals and materials required for pollen tube tip growth. In particular, the "guilt by association" computational pipeline and the genome-scale enzyme correlation network models (GECN) developed in this study was initiated with experimental "omics" data, followed by data analysis and data integration to determine correlations, and could provide a new platform to assist inachieving a deeper understanding of the co-regulation and inter-regulation model in plant research.