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

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)

Analysis of Staphylococcus aureus infection and enterotoxin gene carriage in diarrhoeal patients in Jiading District, Shanghai

ObjectiveTo investigate the prevalence of Staphylococcus aureus in patients with diarrhea, and to analyze the genes carriage of enterotoxin in the strains of these patients in Jiading District, Shanghai.MethodsFrom 2021 to 2023, anal swabs of diarrhea outpatients from one sentinel hospital and nine community health service centers in different townships in Jiading District, Shanghai, were tested for Staphylococcus aureus, from which five enterotoxin virulence genes such as SEA, SEB, SEC, SED, and SEE were tested simultaneously.ResultsA total of 1 080 anal swabs were collected, 81 of which were tested positive for S. aureus, with a detection rate of 7.50%, and the detection rate of S. aureus was similar in patients with diarrhea from 2021‒2023. There was no statistically significant difference in detection rates between males and females (χ2=0.821, P=0.365). S. aureus detection rate was highest in infants and young children with diarrhea (29.51%), followed by 14.06% in the people aged between 4‒<31 years, and 2.99% in those aged ≥31 years. Significant differences were observed in the detection rate of S. aureus in the diarrhoeal patients from different townships of Jiading District(χ2=66.134,P<0.05). The carriage rates of the 5 enterotoxin genes, namely SEA, SEB, SEC, SED, SEE, were 13.58%, 14.81%, 11.11%, 7.41%, and 0, respectively.ConclusionThe prevalence of S. aureus among the patients with diarrhea in Jiading District is relatively stable but with distinct geographical patterns. Children and adolescents are high-risk groups. SEB were the dominant gene, followed by SEA

Contamination of Staphylococcus aureus in food sold in Jiading District, Shanghai from 2021 to 2023

ObjectiveTo investigate the contamination status of Staphylococcus aureus in food and the presence of enterotoxin genes in Jiading District, Shanghai, and to provide a basis for the prevention and treatment of foodborne Staphylococcus aureus disease.MethodsFrom 2021 to 2023, 15 types of food were sampled for S. aureus testing, and the presence of five enterotoxin genes, including sea⁃see, was tested in the strains.ResultsOut of 705 food samples, 88 (12.48%) were positive for S. aureus. S. aureus was detected in 12 of the 15 food types, with the three food types with the highest positive rates being cold noodles (45.00%), raw poultry (26.25%), and vegetable salads (20.00%). The enterotoxin gene carriage rate was 32.95% in food strains. The carriage rates for sea, seb, and sec were 7.95%, 12.50%, and 14.77%, respectively. Neither sed nor see was detected. The detection rate of strains carrying two types of enterotoxin genes was 2.27%. The enterotoxin carriage rates in strains from vegetables, beverages, and raw meat were 57.14%, 40.00%, and 30.00%, respectively.ConclusionThe S. aureus detection rate in food in Jiading District is much higher than the national average. The enterotoxin gene carriage rates are high, with food strains carrying sea, seb, and sec, with sec being the most prevalent. There is a need to enhance monitoring of S. aureus and enterotoxins, especially in high-risk foods such as noodles, vegetables, and non-packaged beverages

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

A parallel algorithm for network traffic anomaly detection based on Isolation Forest

With the rapid development of large-scale complex networks and proliferation of various social network applications, the amount of network traffic data generated is increasing tremendously, and efficient anomaly detection on those massive network traffic data is crucial to many network applications, such as malware detection, load balancing, network intrusion detection. Although there are many methods around for network traffic anomaly detection, they are all designed for single machine, failing to deal with the case that the network traffic data are so large that it is prohibitive for a single computer to store and process the data. To solve these problems, we propose a parallel algorithm based on Isolation Forest and Spark for network traffic anomaly detection. We combine the advantages of Isolation Forest algorithm in network traffic anomaly detection and big data processing capability of Spark technology. Meanwhile, we apply the idea of parallelization to the process of modeling and evaluation. In the calculation process, by assigning tasks to multiple compute nodes, Isolation Forest and Spark can efficiently perform anomaly detection and evaluation process. By this way, we can also solve the problem of computation bottleneck on single machine. Extensive experiments on real world datasets show that our Isolation Forest and Spark is efficient and scales well for anomaly detection on large network traffic data