The simulation results imply that the control limits should vary based on the particular patient population of interest in order to control the in-control performance of the risk-adjusted Bernoulli CUSUM method.
A novel
[4 + 3] annulation of indoline-based aza-dienes and crotonate-derived
sulfur ylides is described. This method could be further expanded
by using more efficient synthetic strategies, including three-component
[3 + 1 + 3] cascade and the direct sulfide-catalyzed [4 + 3] cyclization.
These protocols enable the rapid construction of azepino[2,3-b]indole cores, and a broad spectrum of the desired products
with diverse substituents was facilely accessed in generally high
yield.
Recent advances in high-throughput genotyping have inspired increasing research interests in genome-wide association study for diseases. To understand underlying biological mechanisms of many diseases, we need to consider simultaneously the genetic effects across multiple loci. The large number of SNPs often makes multilocus association study very computationally challenging because it needs to explicitly enumerate all possible SNP combinations at the genome-wide scale. Moreover, with the large number of SNPs correlated, permutation procedure is often needed for properly controlling family-wise error rates. This makes the problem even more computationally demanding, since the test procedure needs to be repeated for each permuted data. In this paper, we present FastChi, an exhaustive yet efficient algorithm for genome-wide two-locus chi-square test. FastChi utilizes an upper bound of the two-locus chi-square test, which can be expressed as the sum of two terms – both are efficient to compute: the first term is based on the single-locus chi-square test for the given phenotype; and the second term only depends on the genotypes and is independent of the phenotype. This upper bound enables the algorithm to only perform the two-locus chi-square test on a small number of candidate SNP pairs without the risk of missing any significant ones. Since the second part of the upper bound only needs to be precomputed once and stored for subsequence uses, the advantage is more prominent in large permutation tests. Extensive experimental results demonstrate that our method is an order of magnitude faster than the brute force alternative.
4-Aminopyridine is a clinically approved drug to improve motor symptoms in multiple sclerosis. A fluorine-18-labeled derivative of this drug, 3-[18F]fluoro-4-aminopyridine, is currently under investigation for positron emission tomography (PET) imaging of demyelination. Herein, the Yamada-Curtius reaction has been successfully applied for the preparation of this PET radioligand with a better radiochemical yield and improved specific activity. The overall radiochemical yield was 5 to 15% (n = 12, uncorrected) with a specific activity of 37 to 148 GBq/µmol (end of synthesis) in a 90 minute synthesis time. It is expected that this 1 pot Yamada-Curtius reaction can be used to prepare similar fluorine-18-labeled amino substituted heterocycles.
Background
Statistical evaluation of the association between microbial abundance and dietary variables can be done in various ways. Currently, there is no consensus on which methods are to be preferred in which circumstances. Application of particular methods seems to be based on the tradition of a particular research group, availability of experience with particular software, or depending on the outcomes of the analysis.
Results
We applied four popular methods including edgeR, limma, metagenomeSeq and shotgunFunctionalizeR, to evaluate the association between dietary variables and abundance of microbes. We found large difference in results between the methods. Our simulation studies revealed that no single method was optimal.
Conclusions
We advise researchers to run multiple analyses and focus on the significant findings identified by multiple methods in order to achieve a better control of false discovery rate, although the false discovery rate can still be substantial.
Gaussian graphical model (GGM)-based method, a key approach to reverse engineering biological networks, uses partial correlation to measure conditional dependence between two variables by controlling the contribution from other variables. After estimating partial correlation coefficients, one of the most critical processes in network construction is to control the false discovery rate (FDR) to assess the significant associations among variables. Various FDR methods have been proposed mainly for biomarker discovery, but it still remains unclear which FDR method performs better for network construction. Furthermore, there is no study to see the effect of the network structure on network construction. We selected the six FDR methods, the linear step-up procedure (BH95), the adaptive linear step-up procedure (BH00), Efron’s local FDR (LFDR), Benjamini-Yekutieli’s step-up procedure (BY01), Storey’s q-value procedure (Storey01), and Storey-Taylor-Siegmund’s adaptive step-up procedure (STS04), to evaluate their performances on network construction. We further considered two network structures, random and scale-free networks, to investigate their influence on network construction. Both simulated data and real experimental data suggest that STS04 provides the highest true positive rate or F1 score, while BY01 has the highest positive predictive value in network construction. In addition, no significant effect of the network structure is found on FDR methods.
Glutamine (Gln) and its analogues may serve as imaging agents for tumor diagnosis using positron emission tomography (PET), especially for tumors with negative [18F]FDG scan. We report the first automated synthesis of [18F](2S,4R)-4-fluoroglutamine ([18F]FGln) on a GE FX-N Pro synthesizer. [18F]FGln was obtained in 80 min with a radiochemical yield of 21 ± 3% (n = 5, uncorrected). The radiochemical purity was > 98%, and optical purity 90 ± 5%. The synthesis is highly reproducible with good chemical purity, radiochemical yield, and is suitable for translation to cGMP production.
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