Robust spectrotemporal decomposition by iteratively reweighted least squares

Classical nonparametric spectral analysis uses sliding windows to capture the dynamic nature of most real-world time series. This universally accepted approach fails to exploit the temporal continuity in the data and is not well-suited for signals with highly structured time–frequency representations. For a time series whose time-varying mean is the superposition of a small number of oscillatory components, we formulate nonparametric batch spectral analysis as a Bayesian estimation problem. We introduce prior distributions on the time–frequency plane that yield maximum a posteriori (MAP) spectral estimates that are continuous in time yet sparse in frequency. Our spectral decomposition procedure, termed spectrotemporal pursuit, can be efficiently computed using an iteratively reweighted least-squares algorithm and scales well with typical data lengths. We show that spectrotemporal pursuit works by applying to the time series a set of data-derived filters. Using a link between Gaussian mixture models, ℓ[subscript 1] minimization, and the expectation–maximization algorithm, we prove that spectrotemporal pursuit converges to the global MAP estimate. We illustrate our technique on simulated and real human EEG data as well as on human neural spiking activity recorded during loss of consciousness induced by the anesthetic propofol. For the EEG data, our technique yields significantly denoised spectral estimates that have significantly higher time and frequency resolution than multitaper spectral estimates. For the neural spiking data, we obtain a new spectral representation of neuronal firing rates. Spectrotemporal pursuit offers a robust spectral decomposition framework that is a principled alternative to existing methods for decomposing time series into a small number of smooth oscillatory components.National Institutes of Health (U.S.) (Transformative Research Award GM 104948)National Institutes of Health (U.S.) (New Innovator Award R01-EB006385

Physostigmine and Methylphenidate Induce Distinct Arousal States During Isoflurane General Anesthesia in Rats

BACKGROUND: Although emergence from general anesthesia is clinically treated as a passive process driven by the pharmacokinetics of drug clearance, agents that hasten recovery from general anesthesia may be useful for treating delayed emergence, emergence delirium, and postoperative cognitive dysfunction. Activation of central monoaminergic neurotransmission with methylphenidate has been shown to induce reanimation (active emergence) from general anesthesia. Cholinergic neurons in the brainstem and basal forebrain are also known to promote arousal. The objective of this study was to test the hypothesis that physostigmine, a centrally acting cholinesterase inhibitor, induces reanimation from isoflurane anesthesia in adult rats. METHODS: The dose-dependent effects of physostigmine on time to emergence from a standardized isoflurane general anesthetic were tested. It was then determined whether physostigmine restores righting during continuous isoflurane anesthesia. In a separate group of rats with implanted extradural electrodes, physostigmine was administered during continuous inhalation of 1.0% isoflurane, and the electroencephalogram changes were recorded. Finally, 2.0% isoflurane was used to induce burst suppression, and the effects of physostigmine and methylphenidate on burst suppression probability (BSP) were tested. RESULTS: Physostigmine delayed time to emergence from isoflurane anesthesia at doses ≥0.2 mg/kg (n = 9). During continuous isoflurane anesthesia (0.9% ± 0.1%), physostigmine did not restore righting (n = 9). Blocking the peripheral side effects of physostigmine with the coadministration of glycopyrrolate (a muscarinic antagonist that does not cross the blood-brain barrier) produced similar results (n = 9 each). However, during inhalation of 1.0% isoflurane, physostigmine shifted peak electroencephalogram power from δ ( < 4 Hz) to θ (4-8 Hz) in 6 of 6 rats. During continuous 2.0% isoflurane anesthesia, physostigmine induced large, statistically significant decreases in BSP in 6 of 6 rats, whereas methylphenidate did not. CONCLUSIONS: Unlike methylphenidate, physostigmine does not accelerate time to emergence from isoflurane anesthesia and does not restore righting during continuous isoflurane anesthesia. However, physostigmine consistently decreases BSP during deep isoflurane anesthesia, whereas methylphenidate does not. These findings suggest that activation of cholinergic neurotransmission during isoflurane anesthesia produces arousal states that are distinct from those induced by monoaminergic activation.National Institutes of Health (U.S.) (Grant TR01-GM104948)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant K08-GM094394

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Convergence and Stability of Iteratively Re-weighted Least Squares Algorithms

In this paper, we study the theoretical properties of iteratively re-weighted least squares (IRLS) algorithms and their utility in sparse signal recovery in the presence of noise. We demonstrate a one-to-one correspondence between the IRLS algorithms and a class of Expectation-Maximization (EM) algorithms for constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribution. The EM formalism, as well as the connection to GSMs, allow us to establish that the IRLS algorithms minimize smooth versions of the lν `norms', for . We leverage EM theory to show that the limit points of the sequence of IRLS iterates are stationary points of the smooth lν “norm” minimization problem on the constraint set. We employ techniques from Compressive Sampling (CS) theory to show that the IRLS algorithm is stable, if the limit point of the iterates coincides with the global minimizer. We further characterize the convergence rate of the IRLS algorithm, which implies global linear convergence for ν = 1 and local super-linear convergence for . We demonstrate our results via simulation experiments. The simplicity of IRLS, along with the theoretical guarantees provided in this contribution, make a compelling case for its adoption as a standard tool for sparse signal recovery.National Institutes of Health (U.S.) (New Innovator Award DP2-OD006454)National Institutes of Health (U.S.) (R01-EB006385

A Regularized Point Process Generalized Linear Model for Assessing the Functional Connectivity in the Cat Motor Cortex

Identification of multiple simultaneously recorded neural spike train recordings is an important task in understanding neuronal dependency, functional connectivity, and temporal causality in neural systems. An assessment of the functional connectivity in a group of ensemble cells was performed using a regularized point process generalized linear model (GLM) that incorporates temporal smoothness or contiguity of the solution. An efficient convex optimization algorithm was then developed for the regularized solution. The point process model was applied to an ensemble of neurons recorded from the cat motor cortex during a skilled reaching task. The implications of this analysis to the coding of skilled movement in primary motor cortex is discussed.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Grant R01-HL084502

The emerging threat of pre-extensively drug-resistant tuberculosis in West Africa: preparing for large-scale tuberculosis research and drug resistance surveillance

Background Drug-resistant tuberculosis (TB) is a global public health problem. Adequate management requires baseline drug-resistance prevalence data. In West Africa, due to a poor laboratory infrastructure and inadequate capacity, such data are scarce. Therefore, the true extent of drug-resistant TB was hitherto undetermined. In 2008, a new research network, the West African Network of Excellence for Tuberculosis, AIDS and Malaria (WANETAM), was founded, comprising nine study sites from eight West African countries (Burkina Faso, The Gambia, Ghana, Guinea-Bissau, Mali, Nigeria, Senegal and Togo). The goal was to establish Good Clinical Laboratory Practice (GCLP) principles and build capacity in standardised smear microscopy and mycobacterial culture across partnering laboratories to generate the first comprehensive West African drug-resistance data. Methods Following GCLP and laboratory training sessions, TB isolates were collected at sentinel referral sites between 2009–2013 and tested for first- and second-line drug resistance. Results From the analysis of 974 isolates, an unexpectedly high prevalence of multi-drug-resistant (MDR) strains was found in new (6 %) and retreatment patients (35 %) across all sentinel sites, with the highest prevalence amongst retreatment patients in Bamako, Mali (59 %) and the two Nigerian sites in Ibadan and Lagos (39 % and 66 %). In Lagos, MDR is already spreading actively amongst 32 % of new patients. Pre-extensively drug-resistant (pre-XDR) isolates are present in all sites, with Ghana showing the highest proportion (35 % of MDR). In Ghana and Togo, pre-XDR isolates are circulating amongst new patients. Conclusions West African drug-resistance prevalence poses a previously underestimated, yet serious public health threat, and our estimates obtained differ significantly from previous World Health Organisation (WHO) estimates. Therefore, our data are reshaping current concepts and are essential in informing WHO and public health strategists to implement urgently needed surveillance and control interventions in West Africa