Using EEG total energy as a noninvasively tracking of intracranial (and cerebral perfussion) pressure in an animal model: A pilot study.

PURPOSE: This study aims to describe the total EEG energy during episodes of intracranial hypertension (IH) and evaluate its potential as a classification feature for IH. NEW METHODS: We computed the sample correlation coefficient between intracranial pressure (ICP) and the total EEG energy. Additionally, a generalized additive model was employed to assess the relationship between arterial blood pressure (ABP), total EEG energy, and the odds of IH. RESULTS: The median sample cross-correlation between total EEG energy and ICP was 0.7, and for cerebral perfusion pressure (CPP) was 0.55. Moreover, the proposed model exhibited an accuracy of 0.70, sensitivity of 0.53, specificity of 0.79, precision of 0.54, F1-score of 0.54, and an AUC of 0.7. COMPARISON WITH EXISTING METHODS: The only existing comparable methods, up to our knowledge, use 13 variables as predictor of IH, our model uses only 3, our model, as it is an extension of the generalized model is interpretable and it achieves the same performance. CONCLUSION: These findings hold promise for the advancement of multimodal monitoring systems in neurocritical care and the development of a non-invasive ICP monitoring tool, particularly in resource-constrained environments.

Selection of the optimal personalized treatment from multiple treatments with right-censored multivariate outcome measures.

We propose a novel personalized concept for the optimal treatment selection for a situation where the response is a multivariate vector that could contain right-censored variables such as survival time. The proposed method can be applied with any number of treatments and outcome variables, under a broad set of models. Following a working semiparametric Single Index Model that relates covariates and responses, we first define a patient-specific composite score, constructed from individual covariates. We then estimate conditional means of each response, given the patient score, correspond to each treatment, using a nonparametric smooth estimator. Next, a rank aggregation technique is applied to estimate an ordering of treatments based on ranked lists of treatment performance measures given by conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting to the corresponding estimators. An empirical study illustrates the performance of the proposed method in finite sample problems. To show the applicability of the proposed procedure for real data, we also present a data analysis using HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.

Sensory Gating during Voluntary Finger Movement in Amyotrophic Lateral Sclerosis with Sensory Cortex Hyperexcitability.

Cortical responses in somatosensory evoked potentials (SEP) are enhanced in patients with amyotrophic lateral sclerosis (ALS). This study investigated whether sensory gating is involved in the pathophysiology of sensory cortical hyperactivity in ALS patients. The median nerve SEP was recorded at rest and during voluntary finger movements in 14 ALS patients and 13 healthy control subjects. The parietal N20, P25, and frontal N30 were analyzed, and sensory gating was assessed by measuring the amplitude of each component during finger movement. The amplitudes of the N20 onset-peak, N20 peak-P25 peak, and N30 onset-peak were higher in ALS patients than in controls. Nonetheless, there were no significant differences in the amplitude reduction ratio of SEPs between patients and controls. There was a significant correlation between the baseline amplitudes of the N20 onset-peak or N20 peak-P25 peak and their gating ratios in patients with ALS. Our findings indicate that the excitability of the primary sensory cortex and secondary motor cortex is enhanced in ALS, while sensory gating is preserved in the early stages of ALS. This result suggests that enhanced SEP is caused by the hyperexcitability of the primary sensory and secondary motor cortices but not by the dysfunction of inhibitory mechanisms during voluntary movements.

Goal-oriented adaptive finite element methods with optimal computational complexity.

We consider a linear symmetric and elliptic PDE and a linear goal functional. We design and analyze a goal-oriented adaptive finite element method, which steers the adaptive mesh-refinement as well as the approximate solution of the arising linear systems by means of a contractive iterative solver like the optimally preconditioned conjugate gradient method or geometric multigrid. We prove linear convergence of the proposed adaptive algorithm with optimal algebraic rates. Unlike prior work, we do not only consider rates with respect to the number of degrees of freedom but even prove optimal complexity, i.e., optimal convergence rates with respect to the total computational cost.

Weak Galerkin finite element method for second order problems on curvilinear polytopal meshes with Lipschitz continuous edges or faces.

In this paper, we propose new basis functions defined on curved sides or faces of curvilinear elements (polygons or polyhedrons with curved sides or faces) for the weak Galerkin finite element method. Those basis functions are constructed by collecting linearly independent traces of polynomials on the curved sides/faces. We then analyze the modified weak Galerkin method for the elliptic equation and the interface problem on curvilinear polytopal meshes with Lipschitz continuous edges or faces. The method is designed to deal with less smooth complex boundaries or interfaces. Optimal convergence rates for H 1 and L 2 errors are obtained, and arbitrary high orders can be achieved for sufficiently smooth solutions. The numerical algorithm is discussed and tests are provided to verify theoretical findings.

Correlation of N30 somatosensory evoked potentials with spasticity and neurological function after stroke: A cross-sectional study.

OBJECTIVE: To test whether the presence of N30 somatosensory evoked potentials, generated from the supplementary motor area and premotor cortex, correlate with post-stroke spasticity, motor deficits, or motor recovery stage. DESIGN: A cross-sectional study. PATIENTS: A total of 43 patients with stroke hospitalized at Maoming People's Hospital, Maoming, China. METHODS: Forty-three stroke patients underwent neurofunctional tests, including Modified Ashworth Scale (MAS), Brunnstrom stage, manual muscle test and neurophysiological tests, including N30 somatosensory evoked potentials, N20 somatosensory evoked potentials, motor evoked potentials, H-reflex. The results were compared between groups. Correlation and regression analyses were performed as well. RESULTS: Patients with absence of N30 somatosensory evoked potential exhibited stronger flexor carpi radialis muscle spasticity (r = -0.50, p < 0.05) and worse motor function (r = 0.57, p < 0.05) than patients with presence of N30 somatosensory evoked potential. The generalized linear model (GLM) including both N30 somatosensory evoked potentials and motor evoked potentials (Akaike Information Criterion (AIC) = 121.99) better reflected the recovery stage of the affected proximal upper limb than the models including N30 somatosensory evoked potentials (AIC = 125.06) or motor evoked potentials alone (AIC = 127.45). CONCLUSION: N30 somatosensory evoked potential status correlates with the degrees of spasticity and motor function of stroke patients. The results showed that N30 somatosensory evoked potentials hold promise as a biomarker for the development of spasticity and the recovery of proximal limbs.

Error Bounds for Dynamical Spectral Estimation.

Dynamical spectral estimation is a well-established numerical approach for estimating eigenvalues and eigenfunctions of the Markov transition operator from trajectory data. Although the approach has been widely applied in biomolecular simulations, its error properties remain poorly understood. Here we analyze the error of a dynamical spectral estimation method called "the variational approach to conformational dynamics" (VAC). We bound the approximation error and estimation error for VAC estimates. Our analysis establishes VAC's convergence properties and suggests new strategies for tuning VAC to improve accuracy.

The influence of right-left error in the placement of the Cc electrode in tibial nerve somatosensory evoked potentials (SEPs).

OBJECTIVE: At our laboratory, we routinely record tibial nerve somatosensory evoked potentials (SEPs) using 5 channels including the second cervical vertebra (C2S)-contralateral central area (Cc) and Cz' (2 cm posterior to Cz)-Cc derivations. In a man with lumbar spondylotic myelopathy, symptoms improved after surgery, although the N21-P38 interval was markedly prolonged in comparison with that before surgery. We presumed that the Cc electrode was actually placed on the ipsilateral central area (Ci) at the second examination. Inspired by this episode, we investigated the influence of the right-left error in the placement of the Cc electrode. METHODS: Subjects were 20 healthy volunteers. Tibial nerve SEPs were recorded with 8 leads including Cz'-Cc, Cz'-Ci, C2S-Cc and C2S-Ci. RESULTS: For the Cz'-Ci lead, the P38 potential diminished in amplitude, was absent or became negative. For the C2S-Ci lead, a large negative potential corresponding to the phase reversal of P38 was frequently observed. CONCLUSIONS: Tibial nerve SEPs using the Cz'-Cc or C2S-Cc lead are distorted if the Cc electrode is placed on the opposite side. SIGNIFICANCE: When a strange result is obtained in tibial nerve SEPs, we should check for a right-left error in the Cc electrode placement.

Review of fractional epidemic models.

The global impact of corona virus (COVID-19) has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 influenza A(H1N1) pandemic. In this paper, we have focused on reviewing the results of epidemiological modelling especially the fractional epidemic model and summarized different types of fractional epidemic models including fractional Susceptible-Infective-Recovered (SIR), Susceptible-Exposed-Infective-Recovered (SEIR), Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) models and so on. Furthermore, we propose a general fractional SEIAR model in the case of single-term and multi-term fractional differential equations. A feasible and reliable parameter estimation method based on modified hybrid Nelder-Mead simplex search and particle swarm optimisation is also presented to fit the real data using fractional SEIAR model. The effective methods to solve the fractional epidemic models we introduced construct a simple and effective analytical technique that can be easily extended and applied to other fractional models, and can help guide the concerned bodies in preventing or controlling, even predicting the infectious disease outbreaks.

DECOUPLING PDE COMPUTATION WITH INTRINSIC OR INERTIAL ROBIN INTERFACE CONDITION.

We study decoupled numerical methods for multi-domain, multi-physics applications. By investigating various stages of numerical approximation and decoupling and tracking how the information is transmitted across the interface for a typical multi-modeling model problem, we derive an approximate intrinsic or inertial type Robin condition for its semi-discrete model. This new interface condition is justified both mathematically and physically in contrast to the classical Robin interface condition conventionally introduced for decoupling multi-modeling problems. Based on the intrinsic or inertial Robin condition, an equivalent semi-discrete model is introduced, which provides a general framework for devising effective decoupled numerical methods. Numerical experiments also confirm the effectiveness of this new decoupling approach.

Acute Effects of Aerobic Exercise on Somatosensory-Evoked Potentials in Patients with Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is becoming a serious problem for developing countries as the lifespan of populations increases. Exercise is known to be clinically beneficial for MCI patients. Somatosensory-evoked potentials (SEPs) may be a potential diagnostic and prognostic marker for this population. The objective of this study was to determine the acute effects of aerobic exercise on SEPs in patients with MCI, to test whether SEPs are sensitive enough to detect improvements in early somatosensory processing. The study had a randomized parallel-group design and included 28 MCI subjects (14 in the experimental group and 14 in the control group). The experimental intervention was 20 min of aerobic exercise using a stationary bicycle. The control intervention involved 20 min of movements and stretches. Subjects were assessed before and after a single intervention session. SEPs were recorded by stimulating the median nerve of the dominant hand. Analysis of normalized SEP peak amplitudes showed that a single session of aerobic activity significantly reduced the N30 peak at the F3 channel (p = 0.03). There were no significant effects of aerobic exercise on SEP peak latencies. The results indicate that 20 min of aerobic exercise has a significant effect on the N30 SEP peak amplitude in MCI patients. The results suggest that aerobic exercise is likely to provide sensory-enriching inputs that enhance sensorimotor integration. Future studies should assess the effects of aerobic exercise on somatosensory processing in progressive stages of Alzheimer's disease, longer exercise durations, and multiple exercise sessions.

Diversity and Conflict.

This research advances the hypothesis and establishes empirically that interpersonal population diversity, rather than fractionalization or polarization across ethnic groups, has been pivotal to the emergence, prevalence, recurrence, and severity of intrasocietal conflicts. Exploiting an exogenous source of variations in population diversity across nations and ethnic groups, as determined predominantly during the exodus of humans from Africa tens of thousands of years ago, the study demonstrates that population diversity, and its impact on the degree of diversity within ethnic groups, has contributed significantly to the risk and intensity of historical and contemporary civil conflicts. The findings arguably reflect the contribution of population diversity to the non-cohesivnesss of society, as reflected partly in the prevalence of mistrust, the divergence in preferences for public goods and redistributive policies, and the degree of fractionalization and polarization across ethnic, linguistic, and religious groups.

An optimal adaptive wavelet method for first order system least squares.

In this paper, it is shown that any well-posed 2nd order PDE can be reformulated as a well-posed first order least squares system. This system will be solved by an adaptive wavelet solver in optimal computational complexity. The applications that are considered are second order elliptic PDEs with general inhomogeneous boundary conditions, and the stationary Navier-Stokes equations.

MSIQ: JOINT MODELING OF MULTIPLE RNA-SEQ SAMPLES FOR ACCURATE ISOFORM QUANTIFICATION.

Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

A mathematical model of malaria transmission in a periodic environment.

In this paper, we present a mathematical model of malaria transmission dynamics with age structure for the vector population and a periodic biting rate of female anopheles mosquitoes. The human population is divided into two major categories: the most vulnerable called non-immune and the least vulnerable called semi-immune. By applying the theory of uniform persistence and the Floquet theory with comparison principle, we analyse the stability of the disease-free equilibrium and the behaviour of the model when the basic reproduction ratio [Formula: see text] is greater than one or less than one. At last, numerical simulations are carried out to illustrate our mathematical results.

An analysis of the TDNNS method using natural norms.

The tangential-displacement normal-normal-stress (TDNNS) method is a finite element method for mixed elasticity. As the name suggests, the tangential component of the displacement vector as well as the normal-normal component of the stress are the degrees of freedom of the finite elements. The TDNNS method was shown to converge of optimal order, and to be robust with respect to shear and volume locking. However, the method is slightly nonconforming, and an analysis with respect to the natural norms of the arising spaces was still missing. We present a sound mathematical theory of the infinite dimensional problem using the space [Formula: see text] for the displacement. We define the space [Formula: see text] for the stresses and provide trace operators for the normal-normal stress. Moreover, the finite element problem is shown to be stable with respect to the [Formula: see text] and a discrete [Formula: see text] norm. A-priori error estimates of optimal order with respect to these norms are obtained. Beside providing a new analysis for the elasticity equation, the numerical techniques developed in this paper are a foundation for more complex models from structural mechanics such as Reissner Mindlin plate equations, see Pechstein and Schöberl (Numerische Mathematik 137(3):713-740, 2017).

Somatosensory electrical stimulation improves skill acquisition, consolidation, and transfer by increasing sensorimotor activity and connectivity.

The interaction between the somatosensory and motor systems is important for normal human motor function and learning. Enhancing somatosensory input using somatosensory electrical stimulation (SES) can increase motor performance, but the neuronal mechanisms underlying these effects are largely unknown. With EEG, we examined whether skill acquisition, consolidation, and interlimb transfer after SES was related to increased activity in sensorimotor regions, as assessed by the N30 somatosensory evoked potential or rather increased connectivity between these regions, as assessed by the phase slope index (PSI). Right- and left-hand motor performance and EEG measures were taken before, immediately after, and 24 h ( day 2) after either SES ( n = 12; 5 men) or Control ( n = 12; 5 men). The results showed skill acquisition and consolidation in the stimulated right hand immediately after SES (6%) and on day 2 (9%) and interlimb transfer to the nonstimulated left hand on day 2 relative to Control (8%, all P < 0.05). Increases in N30 amplitudes correlated with skill acquisition while PSI from electrodes that represent the posterior parietal and primary somatosensory cortex to the electrode representing the primary motor cortex correlated with skill consolidation. In contrast, interlimb transfer did not correlate with the EEG-derived neurophysiological estimates obtained in the present study, which may indicate the involvement of subcortical structures in interlimb transfer after SES. In conclusion, weak peripheral somatosensory inputs in the form of SES improve skill acquisition, consolidation, and interlimb transfer that coincide with different cortical adaptations, including enhanced N30 amplitudes and PSI. NEW & NOTEWORTHY The relationship between adaptations in synaptic plasticity and motor learning following somatosensory electrical stimulation (SES) is incompletely understood. Here, we used for the first time a multifactorial approach that examined skill acquisition, consolidation, and interlimb transfer following 20 min of SES. In addition, we quantified sensorimotor integration and the magnitude and direction of connectivity with EEG. Following artificial electrical stimulation, increases in sensorimotor integration and connectivity were found to correlate with skill acquisition and consolidation, respectively.

Refined saddle-point preconditioners for discretized Stokes problems.

This paper is concerned with the implementation of efficient solution algorithms for elliptic problems with constraints. We establish theory which shows that including a simple scaling within well-established block diagonal preconditioners for Stokes problems can result in significantly faster convergence when applying the preconditioned MINRES method. The codes used in the numerical studies are available online.

Spectral and temporal electroencephalography measures reveal distinct neural networks for the acquisition, consolidation, and interlimb transfer of motor skills in healthy young adults.

OBJECTIVE: Plasticity of the central nervous system likely underlies motor learning. It is however unclear, whether plasticity in cortical motor networks is motor learning stage-, activity-, or connectivity-dependent. METHODS: From electroencephalography (EEG) data, we quantified effective connectivity by the phase slope index (PSI), neuronal activity by event-related desynchronization, and sensorimotor integration by N30 during the stages of visuomotor skill acquisition, consolidation, and interlimb transfer. RESULTS: Although N30 amplitudes and event-related desynchronization in parietal electrodes increased with skill acquisition, changes in PSI correlated most with motor performance in all stages of motor learning. Specifically, changes in PSI between the premotor, supplementary motor, and primary motor cortex (M1) electrodes correlated with skill acquisition, whereas changes in PSI between electrodes representing M1 and the parietal and primary sensory cortex (S1) correlated with skill consolidation. The magnitude of consolidated interlimb transfer correlated with PSI between bilateral M1s and between S1 and M1 in the non-practiced hemisphere. CONCLUSIONS: Spectral and temporal EEG measures but especially PSI correlated with improvements in complex motor behavior and revealed distinct neural networks in the acquisition, consolidation, and interlimb transfer of motor skills. SIGNIFICANCE: A complete understanding of the neuronal mechanisms underlying motor learning can contribute to optimizing rehabilitation protocols.

Model Rejection and Parameter Reduction via Time Series.

We show how a graph algorithm for finding matching labeled paths in pairs of labeled directed graphs can be used to perform model invalidation for a class of dynamical systems including regulatory network models of relevance to systems biology. In particular, given a partial order of events describing local minima and local maxima of observed quantities from experimental time series data, we produce a labeled directed graph we call the pattern graph for which every path from root to leaf corresponds to a plausible sequence of events. We then consider the regulatory network model, which can itself be rendered into a labeled directed graph we call the search graph via techniques previously developed in computational dynamics. Labels on the pattern graph correspond to experimentally observed events, while labels on the search graph correspond to mathematical facts about the model. We give a theoretical guarantee that failing to find a match invalidates the model. As an application we consider gene regulatory models for the yeast S. cerevisiae.