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A simulation study for comparing testing statistics in response-adaptive randomization
Xuemin Gu, J Jack Lee
BMC Medical Research Methodology , 2010, DOI: 10.1186/1471-2288-10-48
Abstract: Simulations are systematically conducted to characterize the statistical properties of eight test statistics in six response-adaptive randomization methods at six allocation targets with sample sizes ranging from 20 to 200. Since adaptive randomization is usually not recommended for sample size less than 30, the present paper focuses on the case with a sample of 30 to give general recommendations with regard to test statistics for contingency tables in response-adaptive randomization at small sample sizes.Among all asymptotic test statistics, the Cook's correction to chi-square test (TMC) is the best in attaining the nominal size of hypothesis test. The William's correction to log-likelihood ratio test (TML) gives slightly inflated type I error and higher power as compared with TMC, but it is more robust against the unbalance in patient allocation. TMC and TML are usually the two test statistics with the highest power in different simulation scenarios. When focusing on TMC and TML, the generalized drop-the-loser urn (GDL) and sequential estimation-adjusted urn (SEU) have the best ability to attain the correct size of hypothesis test respectively. Among all sequential methods that can target different allocation ratios, GDL has the lowest variation and the highest overall power at all allocation ratios. The performance of different adaptive randomization methods and test statistics also depends on allocation targets. At the limiting allocation ratio of drop-the-loser (DL) and randomized play-the-winner (RPW) urn, DL outperforms all other methods including GDL. When comparing the power of test statistics in the same randomization method but at different allocation targets, the powers of log-likelihood-ratio, log-relative-risk, log-odds-ratio, Wald-type Z, and chi-square test statistics are maximized at their corresponding optimal allocation ratios for power. Except for the optimal allocation target for log-relative-risk, the other four optimal targets could assign mor
Controlled multi
Brian P Hobbs,J Jack Lee,Nan Chen
- , 2018, DOI: 10.1177/0962280215620696
Abstract: The process of screening agents one-at-a-time under the current clinical trials system suffers from several deficiencies that could be addressed in order to extend financial and patient resources. In this article, we introduce a statistical framework for designing and conducting randomized multi-arm screening platforms with binary endpoints using Bayesian modeling. In essence, the proposed platform design consolidates inter-study control arms, enables investigators to assign more new patients to novel therapies, and accommodates mid-trial modifications to the study arms that allow both dropping poorly performing agents as well as incorporating new candidate agents. When compared to sequentially conducted randomized two-arm trials, screening platform designs have the potential to yield considerable reductions in cost, alleviate the bottleneck between phase I and II, eliminate bias stemming from inter-trial heterogeneity, and control for multiplicity over a sequence of a priori planned studies. When screening five experimental agents, our results suggest that platform designs have the potential to reduce the mean total sample size by as much as 40% and boost the mean overall response rate by as much as 15%. We explain how to design and conduct platform designs to achieve the aforementioned aims and preserve desirable frequentist properties for the treatment comparisons. In addition, we demonstrate how to conduct a platform design using look-up tables that can be generated in advance of the study. The gains in efficiency facilitated by platform design could prove to be consequential in oncologic settings, wherein trials often lack a proper control, and drug development suffers from low enrollment, long inter-trial latency periods, and an unacceptably high rate of failure in phase III
Etoposide Induces Nuclear Re-Localisation of AID
Laurens J. Lambert, Simon Walker, Jack Feltham, Heather J. Lee, Wolf Reik, Jonathan Houseley
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0082110
Abstract: During B cell activation, the DNA lesions that initiate somatic hypermutation and class switch recombination are introduced by activation-induced cytidine deaminase (AID). AID is a highly mutagenic protein that is maintained in the cytoplasm at steady state, however AID is shuttled across the nuclear membrane and the protein transiently present in the nucleus appears sufficient for targeted alteration of immunoglobulin loci. AID has been implicated in epigenetic reprogramming in primordial germ cells and cell fusions and in induced pluripotent stem cells (iPS cells), however AID expression in non-B cells is very low. We hypothesised that epigenetic reprogramming would require a pathway that instigates prolonged nuclear residence of AID. Here we show that AID is completely re-localised to the nucleus during drug withdrawal following etoposide treatment, in the period in which double strand breaks (DSBs) are repaired. Re-localisation occurs 2-6 hours after etoposide treatment, and AID remains in the nucleus for 10 or more hours, during which time cells remain live and motile. Re-localisation is cell-cycle dependent and is only observed in G2. Analysis of DSB dynamics shows that AID is re-localised in response to etoposide treatment, however re-localisation occurs substantially after DSB formation and the levels of re-localisation do not correlate with γH2AX levels. We conclude that DSB formation initiates a slow-acting pathway which allows stable long-term nuclear localisation of AID, and that such a pathway may enable AID-induced DNA demethylation during epigenetic reprogramming.
Synthetic control of a fitness tradeoff in yeast nitrogen metabolism
Travis S Bayer, Kevin G Hoff, Chase L Beisel, Jack J Lee, Christina D Smolke
Journal of Biological Engineering , 2009, DOI: 10.1186/1754-1611-3-1
Abstract: Here, we explored how synthetic control of an endogenous circuit can be used to regulate a tradeoff between fitness in resource abundant and resource limited environments in a population of Saccharomyces cerevisiae. We found that noise in the expression of a key enzyme in ammonia assimilation, Gdh1p, mediated a tradeoff between growth in low nitrogen environments and stress resistance in high ammonia environments. We implemented synthetic control of an endogenous Gdh1p regulatory network to construct an engineered strain in which the fitness of the population was tunable in response to an exogenously-added small molecule across a range of ammonia environments.The ability to tune fitness and biological tradeoffs will be important components of future efforts to engineer microbial communities.Many natural and man-made processes, such as lignocellulose digestion [1], wastewater treatment [2], environmental remediation [3], and biofilm formation [4] are mediated by consortia of microbes rather than a single organism. Often microbial consortia are composed of specialist strains that carry out individual metabolic reactions that benefit multiple community members, increase overall biochemical efficiency and buffer the community from environmental changes. In a recent example, a process involving two metabolic specialist strains of Escherichia coli was observed to efficiently convert xylose and glucose mixtures into fermentation products [5] more quickly than using a single generalist organism and adapted to changing concentrations of the two sugars by changing the relative abundance of each organism. The manipulation of existing microbial communities and the construction of synthetic communities will be increasingly important for engineering complex biological functions [6,7].Synthetic biologists are beginning to design microbial consortia using bacterial quorum sensing. A recent study demonstrated how communicating populations of E. coli can act as an AND gate, exhibitin
Guidelines for the prevention of travel-associated illness in older adults
Christie Joya,Jack N. Hutter,Jennifer Masel,Tida K. Lee,Timothy J. Whitman
- , 2017, DOI: 10.1186/s40794-017-0054-0
Abstract:
Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy  [PDF]
Jack Lee, Benny Zee, Qing Li
Journal of Biomedical Science and Engineering (JBiSE) , 2013, DOI: 10.4236/jbise.2013.63038
Abstract:

Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automated method to detect exudates in the literature and it would be useful in large scale screening if fully automatic method is available. In this paper we developed a novel method to detect exudates that based on interactions between texture analysis and segmentation with mathematical morphological technique by using multimodel inference. The texture analysis involves three components: they are statistical texture analysis, high order spectra analysis, and fractal analysis. The performance of the proposed method is assessed by the sensitivity, specificity and accuracy using the public data DIARETDB1. Our results show that the sensitivity, specificity and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown that the proposed method can be run automatically and also improve the accuracy of exudates detection significantly over most of the previous methods.

The application of wavelet-based neural network on DNA microarray data
Jack Lee,Benny Zee
Bioinformation , 2008,
Abstract: The advantage of using DNA microarray data when investigating human cancer gene expressions is its ability to generate enormous amount of information from a single assay in order to speed up the scientific evaluation process. The number of variables from the gene expression data coupled with comparably much less number of samples creates new challenges to scientists and statisticians. In particular, the problems include enormous degree of collinearity among genes expressions, likely violation of model assumptions as well as high level of noise with potential outliers. To deal with these problems, we propose a block wavelet shrinkage principal component (BWSPCA) analysis method to optimize the information during the noise reduction process. This paper firstly uses the National Cancer Institute database (NC160) as an illustration and shows a significant improvement in dimension reduction. Secondly we combine BWSPCA with an artificial neural network-based gene minimization strategy to establish a Block Wavelet-based Neural Network model in a robust and accurate cancer classification process (BWNN). Our extensive experiments on six public cancer datasets have shown that the method of BWNN for tumor classification performed well, especially on some difficult instances with large-class (more than two) expression data. This proposed method is extremely useful for data denoising and is competitiveness with respect to other methods such as BagBoost, RandomForest (RanFor), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN).
Relocation Decision of International Faculty in Kazakhstan
Aliya Kuzhabekova,Jack Lee
- , 2018, DOI: 10.1177/1028315318773147
Abstract: This mixed-methods study uses Push and Pull model, as well as the Kaleidoscope career model, to close the gap in understanding of the experiences of international faculty who work in the Global South. Treating these faculty members as self-initiating expatriates pursuing a boundaryless career, the study reveals that one of the key push factors is shortage of jobs in the international market. While salary remains an important pull factor, nonmonetary motivations, such as the desire to contribute to change, are also important motivators. When making the decision to relocate, international faculty are balancing career consideration with the desires to remain truthful to their values and to fit the career with their personal lives. Most faculty expect to stay in the country for a short term, thus presenting challenges for institution building. They also anticipate that international mobility will leave a positive effect on their careers
The Zinc Dyshomeostasis Hypothesis of Alzheimer's Disease
Travis J. A. Craddock, Jack A. Tuszynski, Deepak Chopra, Noel Casey, Lee E. Goldstein, Stuart R. Hameroff, Rudolph E. Tanzi
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0033552
Abstract: Alzheimer's disease (AD) is the most common form of dementia in the elderly. Hallmark AD neuropathology includes extracellular amyloid plaques composed largely of the amyloid-β protein (Aβ), intracellular neurofibrillary tangles (NFTs) composed of hyper-phosphorylated microtubule-associated protein tau (MAP-tau), and microtubule destabilization. Early-onset autosomal dominant AD genes are associated with excessive Aβ accumulation, however cognitive impairment best correlates with NFTs and disrupted microtubules. The mechanisms linking Aβ and NFT pathologies in AD are unknown. Here, we propose that sequestration of zinc by Aβ-amyloid deposits (Aβ oligomers and plaques) not only drives Aβ aggregation, but also disrupts zinc homeostasis in zinc-enriched brain regions important for memory and vulnerable to AD pathology, resulting in intra-neuronal zinc levels, which are either too low, or excessively high. To evaluate this hypothesis, we 1) used molecular modeling of zinc binding to the microtubule component protein tubulin, identifying specific, high-affinity zinc binding sites that influence side-to-side tubulin interaction, the sensitive link in microtubule polymerization and stability. We also 2) performed kinetic modeling showing zinc distribution in extra-neuronal Aβ deposits can reduce intra-neuronal zinc binding to microtubules, destabilizing microtubules. Finally, we 3) used metallomic imaging mass spectrometry (MIMS) to show anatomically-localized and age-dependent zinc dyshomeostasis in specific brain regions of Tg2576 transgenic, mice, a model for AD. We found excess zinc in brain regions associated with memory processing and NFT pathology. Overall, we present a theoretical framework and support for a new theory of AD linking extra-neuronal Aβ amyloid to intra-neuronal NFTs and cognitive dysfunction. The connection, we propose, is based on β-amyloid-induced alterations in zinc ion concentration inside neurons affecting stability of polymerized microtubules, their binding to MAP-tau, and molecular dynamics involved in cognition. Further, our theory supports novel AD therapeutic strategies targeting intra-neuronal zinc homeostasis and microtubule dynamics to prevent neurodegeneration and cognitive decline.
MicroRNAs can regulate human APP levels
Neha Patel, David Hoang, Nathan Miller, Sara Ansaloni, Qihong Huang, Jack T Rogers, Jeremy C Lee, Aleister J Saunders
Molecular Neurodegeneration , 2008, DOI: 10.1186/1750-1326-3-10
Abstract: Accumulating evidence suggests that increased expression of the amyloid precursor protein gene (APP) increases Alzheimer's disease (AD) risk. The resulting increase in APP protein levels results in increased Aβ levels, leading to synaptic dysfunction, neurodegeneration and, eventually, cognitive decline.APP levels can be regulated at the genomic, transcriptional or translational level. At the genomic level, Down's Syndrome (Trisomy 21) patients have three copies of the APP gene and develop AD symptoms early in life [1]. Similarly, duplication of the APP locus, in the absence of a full trisomy 21, also leads to early-onset AD [2]. Dysregulation of APP transcription can also increase the risk of AD. Genetic variants in the APP promoter increase APP transcription by ~2–3 fold and have been reported to increase AD risk [3]. Growth factors have been reported to control APP mRNA half-life [4]. These growth factors effects are dependent on a 29 bp sequence in the APP 3' UTR [4,5]. APP translation is also regulated; for example, IL-1 can induce an increase in APP translation [6]. IL-1 is a pro-inflammatory cytokine and genetic variants have been linked to increased AD risk [7,8]. Taken together, these findings provide strong evidence that increased APP levels increase AD risk.MicroRNAs (miRNAs) are small noncoding RNAs that control gene expression post-transcriptionally. Complementary binding between miRNAs and sequences within the 3' UTR of target genes results in repression of target gene expression by translational inhibition or mRNA degradation [9]. Approximately 700 miRNA genes are encoded in the human genome and recent evidence demonstrates that some miRNAs are differentially expressed in AD patients compared to age-matched controls [10]. These differences in miRNA expression may play an important role in AD pathogenesis. In an attempt to address this possibility, we test the hypothesis that miRNAs can regulate APP levels.Bioinformatic analysis predicts that the 3' UT
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