Clustering Brain Signals: A Robust Approach Using Functional Data Ranking

In this paper, we analyze electroencephalograms (EEG) which are recordings of brain electrical activity. We develop new clustering methods for identifying synchronized brain regions, where the EEGs show similar oscillations or waveforms according to their spectral densities. We treat the estimated spectral densities from many epochs or trials as functional data and develop clustering algorithms based on functional data ranking. The two proposed clustering algorithms use different dissimilarity measures: distance of the functional medians and the area of the central region. The performance of the proposed algorithms is examined by simulation studies. We show that, when contaminations are present, the proposed methods for clustering spectral densities are more robust than the mean-based methods. The developed methods are applied to two stages of resting state EEG data from a male college student, corresponding to early exploration of functional connectivity in the human brain

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases

Optimizing perinatal wellbeing in pregnancy with obesity: a clinical trial with a multi-component nutrition intervention for prevention of gestational diabetes and infant growth and neurodevelopment impairment

Pregnancy complicated by obesity represents an increased risk of unfavorable perinatal outcomes such as gestational diabetes mellitus (GDM), hypertensive disorders in pregnancy, preterm birth, and impaired fetal growth, among others. Obesity is associated with deficiencies of micronutrients, and pregnant women with obesity may have higher needs. The intrauterine environment in pregnancies complicated with obesity is characterized by inflammation and oxidative stress, where maternal nutrition and metabolic status have significant influence and are critical in maternal health and in fetal programming of health in the offspring later in life. Comprehensive lifestyle interventions, including intensive nutrition care, are associated with a lower risk of adverse perinatal outcomes. Routine supplementation during pregnancy includes folic acid and iron; other nutrient supplementation is recommended for high-risk women or women in low-middle income countries. This study is an open label randomized clinical trial of parallel groups (UMIN Clinical Trials Registry: UMIN000052753, https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000060194) to evaluate the effect of an intensive nutrition therapy and nutrient supplementation intervention (folic acid, iron, vitamin D, omega 3 fatty acids, myo-inositol and micronutrients) in pregnant women with obesity on the prevention of GDM, other perinatal outcomes, maternal and newborn nutritional status, and infant growth, adiposity, and neurodevelopment compared to usual care. Given the absence of established nutritional guidelines for managing obesity during pregnancy, there is a pressing need to develop and implement new nutritional programs to enhance perinatal outcomes

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

Spectra-based clustering methods for visualizing spatio-temporal patterns of winds and waves in the Red Sea.

In oceanic research, it is challenging to understand the patterns of winds and waves due to the complicated spatio-temporal dynamics. In this work, we propose new spectra-based methods for clustering hourly data of wind speed and wave height observed in the entire Red Sea. By clustering time series observed from different locations together, we identify spatial regions that share similar wind and wave directional spectra. We show that it is necessary to consider directional spectra for winds and waves, and that the clustering results may be very different, ignoring the direction.Non UBCUnreviewedAuthor affiliation: King Abdullah University of Science and TechnologyPostdoctora

Time series clustering using the total variation distance with applications in oceanography

A clustering procedure for time series based on the use of the total variation distance between normalized spectral densities is proposed in this work. The approach is thus based on classifying time series in the frequency domain by consideration of the similarity between their oscillatory characteristics. As an application of this procedure, an algorithm for determining stationary periods for time series of random sea waves is developed, a problem in which changes between stationary sea states is usually slow. The proposed clustering algorithm is compared to several other methods which are also based on features extracted from the original series, and the results show that its performance is comparable to the best methods available, and in some tests, it performs better. This clustering method may be of independent interest. Copyright © 2016 John Wiley & Sons, Ltd

The Hierarchical Spectral Merger Algorithm:A New Time Series Clustering Procedure

We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms. The extent of similarity between a pair of time series is measured using the total variation distance between their estimated spectral densities. At each step of the algorithm, every time two clusters merge, a new spectral density is estimated using the whole information present in both clusters, which is representative of all the series in the new cluster. The method is implemented in an R package HSMClust. We present two applications of the HSM method, one to data coming from wave-height measurements in oceanography and the other to electroencefalogram (EEG) data

Estimation and Clustering of Directional Wave Spectra

The directional wave spectrum (DWS) describes the energy of sea waves as a function of frequency and direction. It provides useful information for marine studies and guides the design of maritime structures. One of the challenges in the statistical estimation of DWS is to account for the circular nature of direction. To address this issue, this paper considers the 1-dimensional case of the direction-only DWS (DWSd) and applies the circular regression to smooth the DWSd observations. This paper then improves an existing clustering algorithm by incorporating circular smoothing in the clustering algorithm, automating the determination of the optimal number of clusters, and designing a more appropriate smoothing parameter selection procedure for data with correlated errors. Our simulation studies reveal an improvement in the performance of estimating the underlying DWSd using the circular smoother. Finally, the linear and circular smoothers are compared by clustering two real datasets, one from the Sofar Ocean network and the second from a buoy located at the Red Sea. For the Sofar Ocean data, clustering with the two smoothers results in different number of clusters. For the Red Sea data, a cluster with a peak at the boundary is only identified when the circular smoother is used