Shandong Normal University

About: Shandong Normal University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Catalysis. The organization has 12378 authors who have published 12576 publications receiving 174572 citations.

Transcription Profiles of Genes Related to Hormonal Regulations Under Salt Stress in Sweet Sorghum

Abstract: Sweet sorghum is an annual C4 crop that has high salt tolerance. However, the role of hormones in salt tolerance of sweet sorghum remains unelucidated. In the present study, growth parameters, endogenous hormone concentrations, and transcriptomes of leaves and roots of two inbred lines of sweet sorghum (salt-tolerant M-81E and salt-sensitive Roma) were analyzed under 0 or 150 mM NaCl in order to elucidate hormonal regulation for salt tolerance in sweet sorghum. We found that salt stress inhibited the growth of both genotypes. The concentration of abscisic acid (ABA) changed more significantly in M-81E leaves, and concentration of jasmonate (JA) changed more significantly in Roma roots. While, the concentration of indole-3-acetic acid (IAA) increased in both genotypes, particularly in the leaves. We identified 17 and 15 differentially expressed genes in M-81E between control plants and those subjected to salt stress annotated into pathways of hormone biosynthesis and hormone signal transduction, respectively. In Roma, 16 and 34 differentially expressed genes annotated into pathways of hormone biosynthesis and hormone signal transduction were identified, respectively. Hormone biosynthesis, and signal transduction, may play an important role in regulating the growth and development of sweet sorghum under salt stress. In salt-tolerant inbred line M-81E, ABA may play a key role in salt stress response. In salt-sensitive inbred line Roma, JA may act as the key hormone in response to salt stress. These revealed that hormones are involved in the response of sweet sorghum to salt stress. Furthermore, in different inbred lines, different hormones might play significant roles in regulating the growth and development of sweet sorghum through different regulation pathways.

Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease: diagnosis, longitudinal progress and biological basis

Abstract: Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease (AD). However, whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia and whether these features provide any neurobiological foundation remains unclear. The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging (MRI) markers for AD. Multivariate classifier-based support vector machine (SVM) analysis provided individual-level predictions for distinguishing AD patients (n = 261) from normal controls (NCs; n = 231) with an accuracy of 88.21% and intersite cross-validation. Further analyses of a large, independent the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 1228) reinforced these findings. In MCI groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aβ, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up. These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI, and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus. The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.

Semi-supervised fuzzy clustering: A kernel-based approach

Abstract: Semi-supervised clustering algorithms aim to improve the clustering accuracy under the supervisions of a limited amount of labeled data. Since kernel-based approaches, such as kernel-based fuzzy c-means algorithm (KFCM), have been successfully used in classification and clustering problems, in this paper, we propose a novel semi-supervised clustering approach using the kernel-based method based on KFCM and denote it the semi-supervised kernel fuzzy c-mean algorithm (SSKFCM). The objective function of SSKFCM is defined by adding classification errors of both the labeled and the unlabeled data, and its global optimum has been obtained through repeatedly updating the fuzzy memberships and the optimized kernel parameter. The objective function may have more than one local optimum, so we employ a function transformation technique to reformulate the objective function after a local minimum has been obtained, and select the best optimum as the solution to the objective function. Experimental results on both the artificial and several real data sets show SSKFCM performs better than its conventional counterparts and it achieves the best accurate clustering results when the parameter is optimized.

A Distributed Swarm Optimizer With Adaptive Communication for Large-Scale Optimization

Abstract: Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master–slave model. Specifically, in this distributed optimizer, the master is mainly responsible for communication with slaves, while each slave iterates a swarm to traverse the solution space. An asynchronous and adaptive communication strategy based on the request–response mechanism is especially devised to let the slaves communicate with the master efficiently. Particularly, the communication between the master and each slave is adaptively triggered during the iteration. To aid the slaves to search the space efficiently, an elite-guided learning strategy is especially designed via utilizing elite particles in the current swarm and historically best solutions found by different slaves to guide the update of particles. Together, this distributed optimizer asynchronously iterates multiple swarms to collaboratively seek the optimum in parallel. Extensive experiments on a widely used large-scale benchmark set substantiate that the distributed optimizer could: 1) achieve competitive effectiveness in terms of solution quality as compared to the state-of-the-art large-scale methods; 2) accelerate the execution of the algorithm in comparison with the sequential one and obtain almost linear speedup as the number of cores increases; and 3) preserve a good scalability to solve higher dimensional problems.