In the framework of null hypothesis significance testing for functional data, we propose a procedure able to select intervals of the domain imputable for the rejection of a null hypothesis. An unadjusted p-value function and an adjusted one are the output of the procedure, namely interval-wise testing. Depending on the sort and level α of type-I error control, significant intervals can be selected by thresholding the two p-value functions at level α. We prove that the unadjusted (adjusted) p-value function point-wise (interval-wise) controls the probability of type-I error and it is point-wise (interval-wise) consistent. To enlighten the gain in terms of interpretation of the phenomenon under study, we applied the interval-wise testing to the analysis of a benchmark functional data set, i.e. Canadian daily temperatures. The new procedure provides insights that current state-of-the-art procedures do not, supporting similar advantages in the analysis of functional data with less prior knowledge

Endogenous retroviruses (ERVs), the remnants of retroviral infections in the germ line, occupy ~8% and ~10% of the human and mouse genomes, respectively, and affect their structure, evolution, and function. Yet we still have a limited understanding of how the genomic landscape influences integration and fixation of ERVs. Here we conducted a genome-wide study of the most recently active ERVs in the human and mouse genome. We investigated 826 fixed and 1,065 in vitro HERV-Ks in human, and 1,624 fixed and 242 polymorphic ETns, as well as 3,964 fixed and 1,986 polymorphic IAPs, in mouse. We quantitated >40 human and mouse genomic features (e.g., non-B DNA structure, recombination rates, and histone modifications) in ±32 kb of these ERVs’ integration sites and in control regions, and analyzed them using Functional Data Analysis (FDA) methodology. In one of the first applications of FDA in genomics, we identified genomic scales and locations at which these features display their influence, and how they work in concert, to provide signals essential for integration and fixation of ERVs. The investigation of ERVs of different evolutionary ages (young in vitro and polymorphic ERVs, older fixed ERVs) allowed us to disentangle integration vs. fixation preferences. As a result of these analyses, we built a comprehensive model explaining the uneven distribution of ERVs along the genome. We found that ERVs integrate in late-replicating AT-rich regions with abundant microsatellites, mirror repeats, and repressive histone marks. Regions favoring fixation are depleted of genes and evolutionarily conserved elements, and have low recombination rates, reflecting the effects of purifying selection and ectopic recombination removing ERVs from the genome. In addition to providing these biological insights, our study demonstrates the power of exploiting multiple scales and localization with FDA. These powerful techniques are expected to be applicable to many other genomic investigations.

Hypothermic oxygenated machine perfusion (HOPE) has the potential to counterbalance the detrimental consequences of cold and warm ischemia time (WIT) in both donation after brain death (DBD) and donation after circulatory death (DCD). Herein we investigated the protective effects of HOPE in extended criteria donor (ECD) DBD and overextended WIT DCD grafts.The present retrospective case series included 50 livers subjected to end-ischemic HOPE or dual DHOPE in 2 liver transplantation (LT) centers from January 2018 to December 2019. All DCD donors were subjected to normothermic regional perfusion before organ procurement. Results are expressed as median (interquartile range [IQR]). In the study period, 21 grafts were derived from overextended WIT DCD donors (total WIT 54 [IQR, 40-60] minutes and 75% classified as futile), whereas 29 were from ECD DBD. A total of 3 biliary complications and 1 case of ischemia-type biliary lesion were diagnosed. The rate of early allograft dysfunction (EAD) was 20%, and those patients had higher Comprehensive Complication Index scores. Through a changing point analysis, cold preservation time >9 hours was associated with prolonged hospital stays (P = 0.02), higher rates of EAD (P = 0.009), and worst post-LT complications (P = 0.02). Logistic regression analyses indicated a significant relationship between cold preservation time and EAD. No differences were shown in terms of the early post-LT results between LTs performed with DCD and DBD. Overall, our data are fully comparable with benchmark criteria in LT. In conclusion, the application of DHOPE obtained satisfactory and promising results using ECD-DBD and overextended DCD grafts. Our findings indicate the need to reduce cold preservation time also in the setting of DHOPE, particularly for grafts showing poor quality.

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The curse of outlier measurements in estimation problems is a well known issue in a variety of fields. Therefore, outlier removal procedures, which enables the identification of spurious measurements within a set, have been developed for many different scenarios and applications. In this paper, we propose a statistically motivated outlier removal algorithm for time differences of arrival (TDOAs), or equivalently range differences (RD), acquired at sensor arrays. The method exploits the TDOA-space formalism and works by only knowing relative sensor positions. As the proposed method is completely independent from the application for which measurements are used, it can be reliably used to identify outliers within a set of TDOA/RD measurements in different fields (e.g. acoustic source localization, sensor synchronization, radar, remote sensing, etc.). The proposed outlier removal algorithm is validated by means of synthetic simulations and real experiments. Index TermsTDOA space, TDOA measurements, range differences, outlier removal.

We introduce in this work the Interval Testing Procedure (ITP), a novel inferential technique for functional data. The procedure can be used to test different functional hypotheses, e.g., distributional equality between two or more functional populations, equality of mean function of a functional population to a reference. ITP involves three steps: (i) the representation of data on a (possibly high-dimensional) functional basis; (ii) the test of each possible set of consecutive basis coefficients; (iii) the computation of the adjusted p-values associated to each basis component, by means of a new strategy here proposed. We define a new type of error control, the interval-wise control of the family wise error rate, particularly suited for functional data. We show that ITP is provided with such a control. A simulation study comparing ITP with other testing procedures is reported. ITP is then applied to the analysis of hemodynamical features involved with cerebral aneurysm pathology. ITP is implemented in the fdatest R package.

Motivated by the analysis of the dependence of knee movement patterns during functional tasks on subject-specific covariates, we introduce a distribution-free procedure for testing a functional-on-scalar linear model with fixed effects. The procedure does not only test the global hypothesis on the entire domain but also selects the intervals where statistically significant effects are detected. We prove that the proposed tests are provided with an asymptotic control of the intervalwise error rate, that is, the probability of falsely rejecting any interval of true null hypotheses. The procedure is applied to one-leg hop data from a study on anterior cruciate ligament injury. We compare knee kinematics of three groups of individuals (two injured groups with different treatments and one group of healthy controls), taking individual-specific covariates into account.

Summary. In many applications, process monitoring has to deal with functional responses, which are also known as profile data. In these scenarios, a relevant industrial problem consists of detecting faults by combining supervised learning with functional data analysis and statistical process monitoring. Supervised learning is usually applied to the whole signal domain, with the aim of discovering the features that are affected by the faults of interest. We explore a different perspective, which consists of performing supervised learning to select inferentially the parts of the signal data that are more informative in terms of underlying fault factors. The procedure is based on a non-parametric domain-selective functional analysis of variance and allows us to identify the specific subintervals where the profile is sensitive to process changes. Benefits achieved by coupling the proposed approach with profile monitoring are highlighted by using a simulation study. We show how applying profile monitoring only to the identified subintervals can reduce the time to detect the out-of-control state of the process. To illustrate its potential in industrial applications, the procedure is applied to remote laser welding, where the main aim is monitoring the gap between the welded plates through the observation of the emission spectra of the welded material.