Bin Lu

Bio: Bin Lu is an academic researcher from Fudan University. The author has contributed to research in topics: Type 2 diabetes & Diabetes mellitus. The author has an hindex of 32, co-authored 172 publications receiving 3486 citations. Previous affiliations of Bin Lu include Huazhong University of Science and Technology & Tongji Medical College.

Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes.

Abstract: Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect "true" effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300-318, 2018. © 2017 Wiley Periodicals, Inc.

Investigation of Type 2 Diabetes Risk Alleles Support CDKN2A/B, CDKAL1, and TCF7L2 As Susceptibility Genes in a Han Chinese Cohort

Abstract: Background: Recent genome-wide association studies (GWASs) have reported several genetic variants to be reproducibly associated with type 2 diabetes. Additional variants have also been detected from a metaanalysis of three GWASs, performed in populations of European ancestry. In the present study, we evaluated the influence of 17 genetic variants from 15 candidate loci, identified in type 2 diabetes GWASs and the metaanalysis, in a Han Chinese cohort. Methodology/Principal Findings: Selected type 2 diabetes-associated genetic variants were genotyped in 1,165 type 2 diabetic patients and 1,136 normoglycemic control individuals of Southern Han Chinese ancestry. The OR for risk of developing type 2 diabetes was calculated using a logistic regression model adjusted for age, sex, and BMI. Genotype-phenotype associations were tested using a multivariate linear regression model. Genetic variants in CDKN2A/B, CDKAL1, TCF7L2, TCF2, MC4R, and PPARG showed a nominal association with type 2 diabetes (P <= 0.05), of whom the three first would stand correction for multiple testing: CDKN2A/B rs10811661, OR: 1.26 (1.12-1.43) P = 1.8* 10(-4); CDKAL1 rs10946398, OR: 1.23 (1.09-1.39); P = 7.1* 10(-4), and TCF7L2 rs7903146, OR: 1.61 (1.19-2.18) P = 2.3* 10(-3). Only nominal phenotype associations were observed, notably for rs8050136 in FTO and fasting plasma glucose (P = 0.002), postprandial plasma glucose (P = 0.002), and fasting C-peptide levels (P = 0.006) in the diabetic patients, and with BMI in controls (P = 0.033). Conclusions/Significance: We have identified significant association between variants in CDKN2A/B, CDKAL1 and TCF7L2, and type 2 diabetes in a Han Chinese cohort, indicating these genes as strong candidates conferring susceptibility to type 2 diabetes across different ethnicities. (Less)

Serum uric acid level and its association with metabolic syndrome and carotid atherosclerosis in patients with type 2 diabetes

Abstract: We aimed to investigate whether elevated serum uric acid concentrations are associated with higher risk of metabolic syndrome (MetS) and carotid atherosclerosis in patients with type 2 diabetes. We conducted a population-based cross-sectional survey in Shanghai, with a total of 395 men and 631 women age 41 to 92 years. The carotid artery intima-media thickness (IMT) and carotid atherosclerotic plaques (PLQ) were measured by B-mode ultrasound. MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian Americans. Uric acid levels were negatively associated with duration of diabetes, fasting plasma glucose, glycohemoglobin, eGFR, HDL-cholesterol (all P < 0.001) and positively with BMI, CRP, waist circumference, triglycerides, systolic blood pressure, ACR, HOMA-IR and IMT (all P < 0.05). In the highest quartile of uric acid levels, the risks were substantially higher for MetS [odds ratio 3.97, (95% confidence interval 2.58-6.13)] (P < 0.001 for trend) and PLQ [odds ratio 2.71 (95% confidence interval 1.62-4.47)] (p = 0.013 for trend) compared with that in the lowest quartile of uric acid levels after multiple adjustment. These associations remained significant after further adjustment for potential confounders. Serum uric acid level is associated with MetS and is an independent risk factor for carotid atherosclerosis in patients with type 2 diabetes.

Outburst flood at 1920 BCE supports historicity of China’s Great Flood and the Xia dynasty

Abstract: China’s historiographical traditions tell of the successful control of a Great Flood leading to the establishment of the Xia dynasty and the beginning of civilization. However, the historicity of the flood and Xia remain controversial. Here, we reconstruct an earthquake-induced landslide dam outburst flood on the Yellow River about 1920 BCE that ranks as one of the largest freshwater floods of the Holocene and could account for the Great Flood. This would place the beginning of Xia at ~1900 BCE, several centuries later than traditionally thought. This date coincides with the major transition from the Neolithic to Bronze Age in the Yellow River valley and supports hypotheses that the primary state-level society of the Erlitou culture is an archaeological manifestation of the Xia dynasty.

Systematic review: the epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults.

Abstract: SUMMARY Background Non-alcoholic fatty liver disease (NAFLD) is a common cause of chronic liver disease, and its worldwide prevalence continues to increase with the growing obesity epidemic. This study assesses the epidemiology of NAFLD in adults based on clinical literature published over the past 30 years. Aim To review epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults based on clinical literature published over the past 30 years. Methods An in-depth search of PubMed (1980–2010) was based on five search terms: ‘nonalcoholic fatty liver disease’ OR ‘non-alcoholic steatohepatitis’ OR ‘fatty liver’ OR ‘steatosis’ AND ‘incidence’ [MeSH Terms] OR ‘prevalence’ [MeSH Terms] OR ‘natural history’. Studies of paediatric cohorts were excluded. Articles were categorised by topic and summarised, noting generalisations concerning their content.

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.