Multiple Hypothesis Testing Framework for Spatial Signals

The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated with different hypotheses based on interpolated local false discovery rates. The benefits of our method are illustrated by applications to spatially propagating radio waves.Comment: Submitted to IEEE Transactions on Signal and Information Processing over Network

Identifying the Complete Correlation Structure in Large-Scale High-Dimensional Data Sets with Local False Discovery Rates

The identification of the dependent components in multiple data sets is a fundamental problem in many practical applications. The challenge in these applications is that often the data sets are high-dimensional with few observations or available samples and contain latent components with unknown probability distributions. A novel mathematical formulation of this problem is proposed, which enables the inference of the underlying correlation structure with strict false positive control. In particular, the false discovery rate is controlled at a pre-defined threshold on two levels simultaneously. The deployed test statistics originate in the sample coherence matrix. The required probability models are learned from the data using the bootstrap. Local false discovery rates are used to solve the multiple hypothesis testing problem. Compared to the existing techniques in the literature, the developed technique does not assume an a priori correlation structure and work well when the number of data sets is large while the number of observations is small. In addition, it can handle the presence of distributional uncertainties, heavy-tailed noise, and outliers.Comment: Preliminary versio

Prevalence and antimicrobial susceptibility of Arcobacter species in human stool samples derived from out- and inpatients: the prospective German Arcobacter prevalence study Arcopath

Background: Arcobacter species, particularly A. butzleri, but also A. cryaerophilus constitute emerging pathogens causing gastroenteritis in humans. However, isolation of Arcobacter may often fail during routine diagnostic procedures due to the lack of standard protocols. Furthermore, defined breakpoints for the interpretation of antimicrobial susceptibilities of Arcobacter are missing. Hence, reliable epidemiological data of human Arcobacter infections are scarce and lacking for Germany. We therefore performed a 13-month prospective Arcobacter prevalence study in German patients. Results: A total of 4636 human stool samples was included and Arcobacter spp. were identified from 0.85% of specimens in 3884 outpatients and from 0.40% of specimens in 752 hospitalized patients. Overall, A. butzleri was the most prevalent species (n = 24; 67%), followed by A. cryaerophilus (n = 10; 28%) and A. lanthieri (n = 2; 6%). Whereas A. butzleri, A. cryaerophilus and A. lanthieri were identified in outpatients, only A. butzleri could be isolated from samples of hospitalized patients. Antimicrobial susceptibility testing of Arcobacter isolates revealed high susceptibilities to ciprofloxacin, whereas bimodal distributions of MICs were observed for azithromycin and ampicillin. Conclusions: In summary, Arcobacter including A. butzleri, A. cryaerophilus and A. lanthieri could be isolated in 0.85% of German outpatients and ciprofloxacin rather than other antibiotics might be appropriate for antibiotic treatment of infections. Further epidemiological studies are needed, however, to provide a sufficient risk assessment of Arcobacter infections in humans

Cytochrome P450 2B6 (CYP2B6) and constitutive androstane receptor (CAR) polymorphisms are associated with early discontinuation of efavirenz-containing regimens

Objectives Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolic clearance of efavirenz and single nucleotide polymorphisms (SNPs) in the CYP2B6 gene are associated with efavirenz pharmacokinetics. Since the constitutive androstane receptor (CAR) and the pregnane X receptor (PXR) correlate with CYP2B6 in liver, and a CAR polymorphism (rs2307424) and smoking correlate with efavirenz plasma concentrations, we investigated their association with early (<3 months) discontinuation of efavirenz therapy. Methods Three hundred and seventy-three patients initiating therapy with an efavirenz-based regimen were included (278 white patients and 95 black patients; 293 male). DNA was extracted from whole blood and genotyping for CYP2B6 (516G → T, rs3745274), CAR (540C → T, rs2307424) and PXR (44477T → C, rs1523130; 63396C → T, rs2472677; and 69789A → G, rs763645) was conducted. Binary logistic regression using the backwards method was employed to assess the influence of SNPs and demographics on early discontinuation. Results Of the 373 patients, 131 withdrew from therapy within the first 3 months. Black ethnicity [odds ratio (OR) = 0.27; P = 0.0001], CYP2B6 516TT (OR = 2.81; P = 0.006), CAR rs2307424 CC (OR = 1.92; P = 0.007) and smoking status (OR = 0.45; P = 0.002) were associated with discontinuation within 3 months. Conclusions These data indicate that genetic variability in CYP2B6 and CAR contributes to early treatment discontinuation for efavirenz-based antiretroviral regimens. Further studies are now required to define the clinical utility of these association

Spatial Inference in Large-Scale Sensor Networks using Multiple Hypothesis Testing and Bayesian Clustering

In this thesis, we address the problem of statistical inference in large-scale sensor networks observing spatially varying fields. First, we revisit traditional single-sensor hypothesis testing. We then present a multiple hypothesis framework to model spatial fields occurring in a multitude of practical signal processing applications. Observing and monitoring phenomena that occur within a spatial field is essential to a variety of applications. This includes tasks, such as, detecting occupied radio spectrum in shared spectrum environments, identifying regions of poor air quality in environmental monitoring, smart buildings and different Internet of Things (IoT) applications. Many of these practical problems can be modeled using a multiple hypothesis testing framework, with the goal of identifying homogeneous spatial regions within which a defined null hypothesis (e.g. pollution remaining at tolerable level, radio spectrum being unoccupied) is in place, and regions where alternative hypotheses are true. These regions can be formed assessing observations made by multiple sensors placed at distinct locations. To be scalable for largescale sensor networks, we suggest to compute local test statistics, such as, p-values at each individual sensor to avoid communication overhead due to a large number of sensors exchanging their raw measurement data. Individual test statistics are fed to a Fusion Center (FC), which performs the inference. At the FC, statistical inference is performed with a propose a method referred to as “Spatial Inference based on Clustering of p-values (SPACE-COP)” that uses multiple hypothesis testing and Bayesian clustering to detect occurring phenomena of interest within the spatial field. The method identifies homogeneous regions in a field based on similarity in decision statistics and locations of the sensors. The number of clusters, each of which is associated to a hypothesis, is determined by a newly derived Bayesian cluster enumeration criterion that is based on the statistical model that has been derived in this project. An EM-algorithm is developed to compute the probabilities that associate sensors with clusters. We present two different decision criteria, for maximum performance (SPACE-COP) and control of false discoveries (FDR SPACE-COP). The performance of the proposed methods is studied in a series of simulation examples and compared to competitors from the literature. Simulation results demonstrate the validity of proposed SPACE-COP methods also for cases in which the assumption on underlying spatial shape of alternative areas was clearly violated and true alternative areas followed arbitrary and even non-convex shapes. In summary, the derived algorithms are applicable to large-scale sensor networks to perform statistical inference and identify homogeneous regions in an observed phenomenon or field where the null hypothesis does not hold

Estimating Test Statistic Distributions for Multiple Hypothesis Testing in Sensor Networks

Funding Information: The work of M. Gölz is supported by the German Research Foundation (DFG) under grant ZO 215/17-2. Author for correspondence: M. Gölz. Publisher Copyright: © 2022 IEEE.We recently proposed a novel approach to perform spatial inference using large-scale sensor networks and multiple hypothesis testing [1]. It identifies the regions in which a spatial phenomenon of interest exhibits different behavior from its nominal statistical model. To reduce the intra-sensor-network communication overhead, the raw data is pre-processed at the sensors locally and a summary statistic is send to the cloud or fusion center where the actual spatial inference using multiple hypothesis testing and false discovery control takes place. Local false discovery rates (lfdrs) are estimated to express local believes in the state of the spatial signal. In this work, we extend our approach by proposing two novel lfdr estimators stemming from the Expectation-Maximization method. The estimation bias is considered to explain the differences in performance among the compared lfdr estimators.Peer reviewe

Improving Inference for Spatial Signals by Contextual False Discovery Rates

Funding Information: The work of M. Gölz is supported by the German Research Foundation (DFG) under grant ZO 215/17-2. E-mail: [email protected]. Publisher Copyright: © 2022 IEEEA spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal's spatial behavior. The spatial domain is modeled as a fine discrete grid. The contextual local false discovery rate is computed for each grid point. A decision on the local state of the signal is made for each grid point, hence, many decisions are made simultaneously. A multiple hypothesis testing approach with false discovery rate control is used. The proposed procedure estimates the areas of interesting signal behavior with higher precision than existing methods. No tuning parameters have to be defined by the user.Peer reviewe

Spatial Inference Using Censored Multiple Testing with FDR Control

A wireless sensor network performs spatial inference on a physical phenomenon of interest. The areas in which this phenomenon exhibits interesting or anomalous behavior are identified whilst controlling false positives. We expand our previous work based on multiple hypothesis testing (MHT) and local false discovery rates to save energy and reduce spectrum use. The number of transmissions from sensors producing uninformative statistics are reduced by introducing censoring for MHT that imposes a communication rate constraint while maintaining the desired performance. Two novel methods are proposed. As our numerical experiments demonstrate, both approaches reduce the number of transmissions while maintaining false discovery rate control. In addition, one method allows to either define a fixed number of total transmissions or to trade the number of transmissions off against the achieved detection power