Essays in Problems in Sequential Decisions and Large-Scale Randomized Algorithms

In the first part of this dissertation, we consider two problems in sequential decision making. The first problem we consider is sequential selection of a monotone subsequence from a random permutation. We find a two term asymptotic expansion for the optimal expected value of a sequentially selected monotone subsequence from a random permutation of length $n$. The second problem we consider deals with the multiplicative relaxation or constriction of the classical problem of the number of records in a sequence of $n$ independent and identically distributed observations. In the relaxed case, we find a central limit theorem (CLT) with a different normalization than Renyi\u27s classical CLT, and in the constricted case we find convergence in distribution to an unbounded random variable. In the second part of this dissertation, we put forward two large-scale randomized algorithms. We propose a two-step sensing scheme for the low-rank matrix recovery problem which requires far less storage space and has much lower computational complexity than other state-of-art methods based on nuclear norm minimization. We introduce a fast iterative reweighted least squares algorithm, \textit{Guluru}, based on subsampled randomized Hadamard transform, to solve a wide class of generalized linear models

Sequential Selection of a Monotone Subsequence from a Random Permutation

We find a two term asymptotic expansion for the optimal expected value of a sequentially selected monotone subsequence from a random permutation of length n. A striking feature of this expansion is that it tells us that the expected value of optimal selection from a random permutation is quantifiably larger than optimal sequential selection from an independent sequence of uniformly distributed random variables; specifically, it is larger by at least (1/6) log n + O(1)

G ‐scores: A method for identifying disease‐causing pathogens with application to lower respiratory tract infections

Lower respiratory tract infections (LRTIs) are well known for the lack of a good diagnostic method. The main difficulty lies in the fact that there are a variety of pathogens causing LRTIs, and their management and treatment are quite different. The development of quantitative real‐time loop‐mediated isothermal amplification (qrt‐LAMP) made it possible to rapidly amplify and quantify multiple pathogens simultaneously. The question that remains to be answered is how accurate and reliable is this method? More importantly, how are qrt‐LAMP measurements utilized to inform/suggest medical decisions? When does a pathogen start to grow out of control and cause infection? Answers to these questions are crucial to advise treatment guidance for LRTIs and also helpful to design phase I/II trials or adaptive treatment strategies. In this article, our main contributions include the following two aspects. First, we utilize zero‐inflated mixture models to provide statistical evidence for the validity of qrt‐LAMP being used in detecting pathogens for LRTIs without the presence of a gold standard test. Our results on qrt‐LAMP suggest that it provides reliable measurements on pathogens of interest. Second, we propose a novel statistical approach to identify disease‐causing pathogens, that is, distinguish the pathogens that colonize without causing problems from those that rapidly grow and cause infection. We achieve this by combining information from absolute quantities of pathogens and their symbiosis information to form G ‐scores. Change‐point detection methods are utilized on these G ‐scores to detect the three phases of bacterial growth—lag phase, log phase, and stationary phase. Copyright © 2014 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107530/1/sim6129-sup-0001-supplemental_new.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/107530/2/sim6129.pd

Etiologic Diagnosis of Lower Respiratory Tract Bacterial Infections Using Sputum Samples and Quantitative Loop-Mediated Isothermal Amplification

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship

Analysis of Campylobacter spp. contamination and drug resistance in poultry sold in Jiading District， Shanghai from 2019 to 2021

ObjectiveTo investigate the contamination status and drug resistance of Campylobacter spp. in poultry sold in Jiading District， Shanghai.MethodsFour types of poultry meats （chickens， ducks， geese and pigeons） were sampled from commercial markets， and potential Campylobacter spp. contamination was isolated and identified. Furthermore， resistance of isolated Campylobacter spp. to 15 commonly used antibiotics was tested.ResultsTotally 29 Campylobacter jejuni strains and 34 Campylobacter. coli were isolated from 236 commercial poultry samples. The most severe contamination of Campylobacter spp. was found in chicken samples， with a detection rate of 34.04%， while the lowest detection rate of Campylobacter spp. was found in duck （19.67%）. Contamination status was categorized with different storage conditions. The lowest detection rate of 6.67% was noted under frozen condition， while highest detection rate of 41.27% was noted under cold storage. Campylobacter jejuni was completely resistant to cefazolin， ciprofloxacin， nalidixic acid and tetracycline， and Campylobacter coli was completely resistant to cefazolin， cefoxitin and nalidixic acid； Campylobacter spp. showed the lowest resistance to imipenem. Multi-drug resistant strains accounted for 100.00% of the isolated strains. 96.83% of the strains were resistant to more than 5 drugs， with the highest number reaching 14 kinds of antibiotics.ConclusionThere is a significant difference in the contamination status and drug resistance of Campylobacter spp. isolated from four types of poultry meats sold in Jiading District， Shanghai， and the drug resistance is serious. It is strongly recommended that the use of antibiotics should be strictly controlled. Freezing can effectively reduce Campylobacter spp. pollution

Distribution and Antimicrobial Resistance Characterization of Listeria monocytogenes in Poultry Meat in Jiading District, Shanghai

To investigate the distribution, contamination status, and antibiotic resistance of Listeria monocytogenes in four types of retail poultry meat, including chicken, duck, goose, and pigeon, sold in Jiading District, Shanghai, a total of 236 retail poultry meat samples were collected, and L. monocytogenes isolates were obtained for identification and antibiotic susceptibility testing against 14 common antibiotics. Forty-one L. monocytogenes isolates were detected from the 236 retail poultry meat samples, with detection rates of 24.47%, 19.44%, 14.75%, and 4.44% in chicken, goose, duck, and pigeon meat, respectively. Among refrigerated, frozen, and room temperature samples, refrigerated poultry had the highest detection rate at 25.40%, while frozen poultry had the lowest at 13.33%. The detection rate of L. monocytogenes in chicken meat differed significantly between the storage temperatures, while no significant differences were found for other poultry types. No significant differences in detection rates were observed between different retail locations or packaging methods. Isolates exhibited complete resistance to cefoxitin (FOX) and increasing resistance over time to tetracycline (TET) and clindamycin (CLI), while low levels of resistance were found for penicillin (PEN), oxacillin (OXA), and erythromycin (ERY). Resistance to ERY and TET suggests the potential for multidrug resistance. Significant differences in antibiotic resistance profiles were observed among L. monocytogenes from the various poultry types. In summary, contamination status and antibiotic resistance profiles differed among retail chicken, duck, goose, and pigeon meat sold and the resistance rate of strains continues to increase in Jiading District, Shanghai. Targeted control measures should be implemented to reduce the emergence of resistant strains, as retail conditions had minimal impact on L. monocytogenes prevalence in poultry meat

Hybridization and amplification rate correction for affymetrix SNP arrays

<p>Abstract</p> <p>Background</p> <p>Copy number variation (CNV) is essential to understand the pathology of many complex diseases at the DNA level. Affymetrix SNP arrays, which are widely used for CNV studies, significantly depend on accurate copy number (CN) estimation. Nevertheless, CN estimation may be biased by several factors, including cross-hybridization and training sample batch, as well as genomic waves of intensities induced by sequence-dependent hybridization rate and amplification efficiency. Since many available algorithms only address one or two of the three factors, a high false discovery rate (FDR) often results when identifying CNV. Therefore, we have developed a new CNV detection pipeline which is based on hybridization and amplification rate correction (CNVhac).</p> <p>Methods</p> <p>CNVhac first estimates the allelic concentrations (ACs) of target sequences by using the sample independent parameters trained through physicochemical hybridization law. Then the raw CN is estimated by taking the ratio of AC to the corresponding average AC from a reference sample set for one specific site. Finally, a hidden Markov model (HMM) segmentation process is implemented to detect CNV regions.</p> <p>Results</p> <p>Based on public HapMap data, the results show that CNVhac effectively smoothes the genomic waves and facilitates more accurate raw CN estimates compared to other methods. Moreover, CNVhac alleviates, to a certain extent, the sample dependence of inference and makes CNV calling with appreciable low FDRs.</p> <p>Conclusion</p> <p>CNVhac is an effective approach to address the common difficulties in SNP array analysis, and the working principles of CNVhac can be easily extended to other platforms.</p