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Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals.Author SummaryCircadian and multiday cycles are an important part of many long-term neuro-behavioral phenomena such as pathological inter-ictal epileptiform discharges (IEDs) in epilepsy. Long-term, ambulatory, neuro-behavioral monitoring in human patients involves complex recording systems that can be subject to intermittent, irregular data loss and storage limitations, resulting in sparse, irregularly sampled data. Cycle identification in sparse data or irregular data using traditional frequency decomposition techniques typically requires interpolation to create a regular timeseries. Using unique, long-term recordings of pathological brain activity in patients with epilepsy implanted with an investigational device, we developed a method to identify cycles in sparse, irregular neuro-behavioral data without interpolation. We anticipate this approach will enable retrospective cycle identification in sparse neuro-behavioral timeseries and support prospective sparse sampling in monitoring systems to enable long-term monitoring of patients and to extend storage capacity in a variety of ambulatory monitoring applications.

BackgroundTime series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data withouta prioriassumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types.MethodsWe first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data.ResultsFirst, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of “suboptimal” data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data.ConclusionsWe propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linearandnonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power and accuracy leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.Highlights● Combining CBPT with GLMEs allows statistical analysis to match experimental design.● CBPT with GLMEs accounts for subject variability and hierarchical random effects.● The proposed method maintains control of type I error, type II error, and FWER.● CBPT with GLMEs can be applied to individual channels and pseudo-population data.

We report the genome sequences of 24 newly discovered bacteriophages that infect Rhodobacter capsulatus , a model for photosynthesis and horizontal gene transfer studies. All have substantial relatedness to previously reported siphovirus bacteriophages. Most are categorized in known clusters (RcB, RcC, RcD, and RcF), with one forming a new cluster, RcG.

BACKGROUND: Drug resistant epilepsy (DRE) patients not amenable to epilepsy surgery can benefit from neurostimulation. Few data compare different neuromodulation strategies. OBJECTIVE: Compare five invasive neuromodulation strategies for treatment of DRE: anterior thalamic nuclei deep brain stimulation (ANT-DBS), centromedian thalamic nuclei DBS (CM-DBS), responsive neurostimulation (RNS), chronic subthreshold stimulation (CSS), and vagus nerve stimulation (VNS). METHODS: Single center retrospective review and phone survey for patients implanted with invasive neuromodulation for 2004–2021. RESULTS: N=159 (ANT-DBS=38, CM-DBS=19, RNS=30, CSS=32, VNS=40). Total median seizure reduction (MSR) was 61% for the entire cohort (IQR 5–90) and in descending order: CSS (85%), CM-DBS (63%), ANT-DBS (52%), RNS (50%), and VNS (50%); p=0.07. Responder rate was 60% after median follow-up time of 26 months. Seizure severity, life satisfaction and quality of sleep were improved. Cortical stimulation (RNS and CSS) was associated with improved seizure reduction compared to subcortical stimulation (ANT-DBS, CM-DBS, and VNS) (67% vs. 52%). Effectiveness was similar for focal epilepsy vs generalized epilepsy, closed loop vs open loop stimulation, pediatric vs. adult cases, and high frequency (>100 Hz) vs low frequency (

The heterotrophic flagellate Percolomonas cosmopolitus (Heterolobosea) is often observed in saline habitats worldwide that range from coastal waters to saturated brines. However, only two cultures assigned to this morphospecies have been examined using molecular methods, and their 18S rRNA gene sequences are extremely different. Further the salinity tolerances of individual strains are unknown. Thus, our knowledge on the autecology and diversity in this morphospecies is deficient. Here, we report 18S rRNA gene data on seven strains similar to P. cosmopolitus from seven geographically remote locations (New Zealand, Kenya, Korea, Poland, Russia, Spain, and the USA) with sample salinities ranging from 4‰ to 280‰, and compare morphology and salinity tolerance of the nine available strains. Percolomonas cosmopolitus-like strains show few-to-no consistent morphological differences, and form six clades separated by often extremely large 18S rDNA divergences (up to 42.4%). Some strains grew best at salinities from 75 to 125‰ and represent halophiles. All but one of these belonged to two geographically heterogeneous clusters that formed a robust monophyletic group in phylogenetic trees; this likely represents an ecologically specialized subclade of halophiles. Our results suggest that P. cosmopolitus is a cluster of several cryptic species (at least), which are unlikely to be distinguished by geography. Interestingly, the 9 Percolomonas strains formed a clade in 18S rDNA phylogenies, unlike most previous analyses based on two sequences.

Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measures of the living human brain. However, data collection is limited to highly specialized clinical environments. To improve internal (re)use and external sharing of these unique data, we present a structure for storing and sharing iEEG data: BIDS-iEEG, an extension of the Brain Imaging Data Structure (BIDS) specification, along with freely available examples and a bids-starter-kit. BIDS is a framework for organizing and documenting data and metadata with the aim to make datasets more transparent and reusable and to improve reproducibility of research. It is a community-driven specification with an inclusive decision-making process. As an extension of the BIDS specification, BIDS-iEEG facilitates integration with other modalities such as fMRI, MEG, and EEG. As the BIDS-iEEG extension has received input from many iEEG researchers, it provides a common ground for data transfer within labs, between labs, and in open-data repositories. It will facilitate reproducible analyses across datasets, experiments, and recording sites, allowing scientists to answer more complex questions about the human brain. Finally, the cross-modal nature of BIDS will enable efficient consolidation of data from multiple sites for addressing questions about generalized brain function.

We performed one of the first systematic, population-based surveys of women in Amish culture. We used these data to examine health status and health risks in a representative sample of 288 Amish women ages 18–45 living in Lancaster County, Pennsylvania, in particular for risks associated with preterm and low birthweight infants, compared with a general population sample of 2,002 women in Central Pennsylvania. Compared with women in the general population, Amish women rated their physical health approximately at the same level, but reported less stress, fewer symptoms of depression, and had higher aggregate scores for mental health. Amish women reported low levels of intimate partner violence, high levels of social support, and they perceived low levels of unfair treatment owing to gender compared with the general population. Amish women also reported higher fertility, fewer low birthweight babies, but the same number of preterm births as the general population. The findings suggest that these outcomes may be due to higher levels of social support and better preconceptional behavior among Amish women.

Containerisation is one of the youngest sectors in the shipping industry. It first emerged in 1955. A decade later, the container fleet stood at r.d. 2.8 m TEU, and it has continued this significant growth. As of May 2012, the total container fleet is recorded at r.d. 15.7 m TEU, an increase of 458 % in 16 years at the rate of 28.6 % per year.

The Fifth International Workshop on Advances in Electrocorticography convened in San Diego, CA, on November 7–8, 2013. In the interval year since the last workshop, advancements in methodology, implementation, and commercialization across both research and clinical interests were the focus of the gathering. Electrocorticography (ECoG) is now firmly established as a preferred signal source for advanced research in functional, cognitive, and neuroprosthetic domains. Published output in ECoG fields has increased tenfold in the past decade. This proceedings attempts to summarize the state of the art.

Stephanopogon is a taxon of multiciliated protists that is now known to belong to Heterolobosea. Small subunit ribosomal DNA (SSU rDNA) phylogenies indicate that Stephanopogon is closely related to or descended from Percolomonas, a small tetraflagellate with a different feeding structure, thus these morphologically dissimilar taxa are of ongoing evolutionary interest. A new strain of Stephanopogon, KM041, was cultured, then characterized by light microscopy, electron microscopy, and SSU rDNA sequencing. KM041 is 18–35 μm (mean 26.8 μm) long, with six main ventral ciliary rows, one ventro-lateral ciliary row, and three anterior barbs. It closely resembles Stephanopogon minuta Lei et al. 1999 in morphology, and is very closely related to an extinct culture “S. aff. minuta”, yet is markedly dissimilar in SSU rDNA sequence from a different isolate identified as S. minuta. This confirms that there are at least two distinct lineages of S. minuta-like cells, and we describe KM041 as a new species, Stephanopogon pattersoni n. sp. The ultrastructure of KM041 resembles that of previously studied Stephanopogon species, though it has a novel paraxonemal structure in a few cilia. We note that a sub-basal-body pad and bulbous axosome are unlikely to be apomorphies for the Stephanopogon–Percolomonas clade.

The Fourth International Workshop on Advances in Electrocorticography (ECoG) convened in New Orleans, LA, on October 11-12, 2012. The proceedings of the workshop serves as an accurate record of the most contemporary clinical and experimental work on brain surface recording and represents the insights of a unique multidisciplinary ensemble of expert clinicians and scientists. Presentations covered a broad range of topics, including innovations in passive functional mapping, increased understanding of pathologic high-frequency oscillations, evolving sensor technologies, a human trial of ECoG-driven brain-machine interface, as well as fresh insights into brain electrical stimulation.