An open science resource for establishing reliability and reproducibility in functional connectomics

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included

Accuracy Evaluation of Advanced Geological Prediction Based on Improved AHP and GPR

Ground penetrating radar (GPR) is widely used in advanced geological prediction. It is necessary to choose a scientific and effective evaluation method to give a reasonable evaluation of the accuracy of prediction. In this paper, a method based on improved analytic hierarchy process (AHP) and GPR is proposed to evaluate the accuracy of advanced geological prediction. Based on the analysis and induction of the factors that affect the accuracy of GPR prediction, an improved AHP is proposed, in which a new measure of “numerical weight” is added and the principle of maximum membership degree is integrated, and an improved AHP model is established for GPR prediction accuracy classification and evaluation. The engineering application of Xiaobeishan Tunnel of Jie-Hui Highway is taken as a case study, and it is proved that the evaluation indices are easy to obtain and the evaluation results are accurate and reliable. The improved AHP-GPR method can be further used for other tunnel engineering

Serum-Exosome-Derived miRNAs Serve as Promising Biomarkers for HCC Diagnosis

Background: Serum exosomes are emerging as key liquid biopsy biomarkers for the early diagnosis of cancer. However, the proportion and distribution of small RNA (sRNA) species from serum exosomes of hepatocellular carcinoma (HCC) patients remain unclear. Effective and reliable biomarkers for HCC diagnosis should be explored. Methods: In this study, we aimed to use sRNA sequencing to profile the sRNAs of serum exosomes in HCC and non-tumor donors. The serum exosomes of 124 HCC patients and 46 non-tumor donors were enrolled for detecting the values of the potential biomarkers for the diagnosis of HCC. Results: We found that miRNAs accounted for the maximal percentage of all types of sRNAs both in the serum exosomes of HCC patients and non-tumor donors. This indicated that the serum-exosome-derived microRNAs (miRNAs) were the most valuable as potential biomarkers in HCC diagnosis. Then, miRNAs were set as research candidates. In our Chinese cohorts, three serum-exosome-derived miRNAs (miR-122-5p, let-7d-5p, and miR-425-5p) could be promising biomarkers for distinguishing HCC patients from non-tumor donors. In addition, they were preferred for the early diagnosis of HCC. We also presented the base distribution of some novel serum-exosome-derived miRNAs and described the potential values as biomarkers. Conclusions: The results suggested that the serum-exosome-derived miRNAs were the most crucial sRNA species and they highlighted the potential of serum-exosome-derived miRNAs as promising biomarkers for HCC diagnosis