Biology subjects

Sui, J.

Publications and source records attributed to Sui, J..

5 recordsLinked to original sources

bioRxiv2018

Development of a novel signature of long noncoding RNAs as a prognostic biomarker for esophageal cancer

ObjectivesThis study aims to develop a lncRNA signature based on RNA-Seq data to predict overall survival in esophageal cancer patients.\n\nMethodsThe lncRNA expression profiles and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database on August 30, 2017. Differentially expressed lncRNAs were screened out between tumor tissues and adjacent normal tissues. The univariate and multivariate Cox regression models were used to develop a prognostic signature for all esophageal cancer patients. The receiver operating curve (ROC) was used to test the sensitivity and specificity of lncRNA signature. Survivals were compared via log-rank test. GO and KEGG enrichment analyses were used to explore the potential functions of prognostic lncRNAs.\n\nResultsWe identified two lncRNAs (RPL34-AS1 and GK3P) were significantly associated with the overall survival of the total 150 esophageal cancer patients. A novel two-lncRNA signature was constructed by Cox regression models. Signature low-risk cases showed better overall survival (median 625.560 days vs. 478.000 days, p = 0.002) than high-risk cases. Further analysis suggested that this two-lncRNA signature was independent of clinical characteristics. GO functional and KEGG pathway enrichment analyses revealed potential functional roles of the two prognostic lncRNAs in tumorigenesis.\n\nConclusionsOur findings suggest that the two-lncRNA signature may be a useful prognostic biomarker for predicting overall survival in esophageal cancer patients.

bioinformatics↗

bioRxiv2018

Group ICA for Identifying Biomarkers in Schizophrenia: ‘Adaptive’ Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio-temporal Regression

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45{+/-}8.9%, sensitivity 0.74{+/-}0.11, specificity 0.79{+/-}0.11) than STR (accuracy 67.45{+/-}8.13%, sensitivity 0.65{+/-}0.11, specificity 0.71{+/-}0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

neuroscience↗

bioRxiv2018

Isolation and characterization of antagonistic bacteria with the potential for biocontrol of soil-borne wheat diseases

Bacillus amyloliquefaciens subsp. plantarum XH-9 is a plant-beneficial rhizobacterium that shows good antagonistic potential against phytopathogens by releasing diffusible and volatile antibiotics, and secreting hydrolytic enzymes. Furthermore, the XH-9 strain possesses important plant growth-promoting characteristics, including nitrogen fixation (7.92 {+/-} 1.05 mg/g), phosphate solubilization (58.67 {+/-} 4.20 g/L), potassium solubilization (10.07 {+/-} 1.26 g/mL), and the presence of siderophores (4.92 {+/-} 0.46 g/mL), indole-3-acetic acid (IAA) (7.76 {+/-} 0.51 g/mL) and 1-aminocyclopropane-1-carboxylic acid deaminase (ACC-deaminase) (4.67 {+/-} 1.21 nmol/[mg*h]). Moreover, the XH-9 strain showed good capacities for wheat, corn, and chili root colonization, which are critical prerequisites for controlling soil-borne diseases as a bio-control agent. Real-time quantitative polymerase chain reaction experiments showed that the amount of Fusarium oxysporum DNA associated with the XH-9 strain after treatment significantly decreased compared with control group. Accordingly, wheat plants inoculated with the XH-9 strain showed significant increases in the plant shoot heights (14.20%), root lengths (32.25%), dry biomass levels (11.93%), and fresh biomass levels (16.28%) relative to the un-inoculated plants. The results obtained in this study suggest that the XH-9 strain has potential as plant-growth promoter and biocontrol agent when applied in local arable land to prevent damage caused by F. oxysporum and other phytopathogens.\n\nImportancePlant diseases, particularly soilborne pathogens, play a significant role in the destruction of agricultural resources. Although these diseases can be controlled to some extent with crop and fungicides, while these measures increase the cost of production, promote resistance, and lead to environmental contamination, so they are being phased out. Plant growth-promoting rhizobacteria are an alternative to chemical pesticides that can play a key role in crop production by means of siderophore and indole-3-acetic acid production, antagonism to soilborne root pathogens, phosphate and potassium solubilization, and nitrogen fixation. These rhizobacteria can also promote a beneficial change in the microorganism community by significantly reducing its pathogenic fungi component. Their use is fully in accord with the principles of sustainability.

microbiology↗

bioRxiv2017

Multimodal Neural Correlates Of Cognitive Control In The Human Connectome Project

Cognitive control is a construct that refers to the set of functions that enable decisionmaking and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA+jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.

neuroscience↗

bioRxiv2016

Shared neural basis for experiencing the beauty of human faces and visual art: Evidence from a meta-analyses of fMRI studies

The existence of a common beauty is a long-standing debate in philosophy and related disciplines. In the last two decades, cognitive neuroscientists have sought to elucidate this issue by exploring the common neural basis of the experience of beauty. Still, empirical evidence for such common neural basis of different forms of beauty is not conclusive. To address this question, we performed an activation likelihood estimation (ALE) meta-analysis on the existing neuroimaging studies of beauty appreciation of faces and visual art by non-expert adults (49 studies, 982 participants, meta-data are available at https://osf.io/s9xds/). We observed that perceiving these two forms of beauty activated distinct brain regions: while the beauty of faces convergently activated the left ventral striatum, the beauty of visual art convergently activated the anterior medial prefrontal cortex (aMPFC). However, a conjunction analysis failed to reveal any common brain regions for the beauty of visual art and faces. The implications of these results are discussed.

neuroscience↗