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Thus far, despite the development of electric field sensors (EFSs) such as field mills, optoelectronic EFSs and microelectromechanical system (MEMS)-based EFSs, no sensor can accurately measure an electric field in space due to the existence of space charge and the influence of charge attachment. To measure a spatial synthetic electric field in an ion flow field, a double potential independent differential EFS based on MEMS is proposed. Compared with other EFSs, this method has the advantages of independent potential (without grounding) and the ability to support the measurement of the synthetic ion flow electric field in space. First, to analyse the charge distribution after the sensor is involved exposed to an electric field, a simulation model was constructed. Then, given the redistribution of the spatial electric field in space and the influence of the surface charge on the sensor, the quantitative relationship between the electric field to be measured and that measured by the proposed sensor was obtained. To improve the performance of the EFS, a set of synthetic field strength sensor calibration systems that consider spatial ion flow injection was established. Furthermore, the parameter λ, which is related to the relative position of the differential chips, was determined. Finally, a series of comparative experiments indicated that the differential EFS highlighted in the present study exhibits good linearity and accuracy.

This paper presents and evaluates an algorithm for extracting cerebral evoked potentials associated with the heartbeat (heartbeat-evoked potentials, HEP) using the independent components method. The algorithm includes simultaneous recording of the electroencephalogram (EEG) and photoplethysmogram (PPG), with extraction of EEG fragments corresponding to PPG pulse wave peaks, removal of cardiogenic activity from the resulting fragments by independent components analysis (ICA), and synchronization of the fragments with the cardiogram R-wave. The locations of sources of recorded potentials were clarified by applying a “current source density” (CSD) spatial filter. The algorithm was tested on 21 experimental participants and demonstrated a characteristic increase in the amplitude of the potential in the interval 0–400 msec after the heartbeat, i.e., the HEP. Thus, application of independent components analysis and spatial filtration to PPG-synchronized EEG fragments can isolate cerebral evoked potentials associated with heartbeats independently of the actual cardiac activity recorded by scalp leads.

Most quasi-static variational models of fracture are based on the splitting of the energy functional into the sum of two terms: bulk, depending on the displacement gradient, and surface, depending on the displacement discontinuities. In this paper we consider the simplest one-dimensional problem of this type, a bar stretched by a given axial displacement, and systematically compare two alternative interpretations of the surface energy term. In the first interpretation ( elastic model ), the surface energy is viewed as a cohesive energy which is stored and can be recovered. In the second ( inelastic model ), it is irreversibly lost. We show that by assuming an evolution scheme based on local minimization and by varying the convexity-concavity properties of the surface energy the elastic model can reproduce a broad class of macroscopic material responses which have been traditionally treated as unrelated. These responses are associated with monotone loading and range from brittle fracture to rate independent plasticity. However, a realistic description for both loading and unloading is achieved only within the inelastic model.

Background: The WHO 2016 classification of diffuse gliomas has incorporated molecular markers isocitrate dehydrogenase (IDH) gene mutations (IDHmut) and codeletion of chromosomal arms 1p and 19q (1p/19q codeletion) as tumor defining entities. The diagnosis of diffuse oligodendrogliomas (ODG) and anaplastic oligodendroglioma (AO) mandatorily requires the demonstration of IDH1 and/or IDH2 mutations along with 1p/19q codeletion, whereas the 1p/19q noncodeleted diffuse gliomas are labeled as astrocytomas. The current methodologies for assessing 1p/19q codeletion status are expensive and not widely available. Studies have proposed alpha internexin (INA) expression on immunohistochemistry (IHC) as a surrogate marker for 1p/19q codeletion and a good prognostic marker in gliomas. Materials and Methods: In this study, we performed IHC for INA expression on the retrospective cohort of anaplastic gliomas (AGs) from our previously published study. Results: INA positivity on IHC showed a significant positive correlation with 1p/19q codeletion (P < 0.001) with a Spearman's rank correlation coefficient (Rho) of 0.804, sensitivity of 87.5%, specificity of 93.0%, and a diagnostic odds ratio of 93:1 in AGs. Similar to the 1p/19q codeletion status, INA positivity showed a positive correlation with IDHmut (P = 0.002) and a negative correlation with α-thalassemia mental retardation X-linked protein (ATRX) loss of expression (P < 0.001). On univariate survival analysis, INA positivity was associated with significantly prolonged overall survival (OS) and recurrent free survival (RFS) in AGs (P < 0.001). Furthermore, within AO, INA positivity significantly improved RFS (P = 0.022) with OS trending towards significance (P = 0.094). Conclusions: INA expression on IHC could serve as a potential surrogate marker for 1p/19q, and highlights its prognostic value in AO and AGs.

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Soil salinization is one of the most serious abiotic stress factors affecting plant productivity through reduction of soil water potential, decreasing the absorptive capacity of the roots for water and nutrients. A weighted meta-analysis was conducted to study the effects of arbuscular mycorrhizal fungi (AMF) inoculation in alleviating salt stress in C and C plants. We analyzed the salt stress influence on seven independent variables such as chlorophyll, leaf area, photosynthetic rate ( ), stomatal conductance ( ), transpiration rate ( ), relative water content (RWC), and water use efficiency (WUE) on AMF inoculated plants. Responses were compared between C and C plants, AMF species, plant functional groups, level of salinity, and environmental conditions. Our results showed that AMF inoculated plants had a positive impact on gas exchange and water status under salt stress. The total chlorophyll contents of C plants were higher than C plants. However, C plants responses regarding , and were more positive compared to C plants. The increase in mainly maintained and it explains the increase in and increase in . When the two major AMF species ( and ) were considered, the effect sizes of RWC and WUE in were lower than those in indicating that inoculated plants performed better under salt stress. In terms of C and C plant photosynthetic pathways, the effect size of C was lower than C plants indicating that AMF inoculation more effectively alleviated salt stress in C compared to C plants.

Combining EEG with eye-tracking is a promising approach to study neural correlates of natural vision, but the resulting recordings are also heavily contaminated by activity of the eye balls, eye lids, and extraocular muscles. While Independent Component Analysis (ICA) is commonly used to suppress these ocular artifacts, its performance under free viewing conditions has not been systematically evaluated and many published reports contain residual artifacts. Here I evaluated and optimized ICA-based correction for two tasks with unconstrained eye movements: visual search in images and sentence reading. In a first step, four parameters of the ICA pipeline were varied orthogonally: the (1) high-pass and (2) low-pass filter applied to the training data, (3) the proportion of training data containing myogenic saccadic spike potentials (SP), and (4) the threshold for eye tracker-based component rejection. In a second step, the eye-tracker was used to objectively quantify the correction quality of each ICA solution, both in terms of undercorrection (residual artifacts) and overcorrection (removal of neurogenic activity). As a benchmark, results were compared to those obtained with an alternative spatial filter, Multiple Source Eye Correction (MSEC). With commonly used settings, Infomax ICA not only left artifacts in the data, but also distorted neurogenic activity during eye movement-free intervals. However, correction results could be strongly improved by training the ICA on optimally filtered data in which SPs were massively overweighted. With optimized procedures, ICA removed virtually all artifacts, including the SP and its associated spectral broadband artifact from both viewing paradigms, with little distortion of neural activity. It also outperformed MSEC in terms of SP correction. Matlab code is provided.

We review a simple yet versatile approach for the analysis of multichannel data, focusing in particular on brain signals measured with EEG, MEG, ECoG, LFP or optical imaging. Sensors are combined linearly with weights that are chosen to provide optimal signal-to-noise ratio. Signal and noise can be variably defined to match the specific need, e.g. reproducibility over trials, frequency content, or differences between stimulus conditions. We demonstrate how the method can be used to remove power line or cardiac interference, enhance stimulus-evoked or stimulus-induced activity, isolate narrow-band cortical activity, and so on. The approach involves decorrelating both the original and filtered data by joint diagonalization of their covariance matrices. We trace its origins; offer an easy-to-understand explanation; review a range of applications; and chart failure scenarios that might lead to misleading results, in particular due to overfitting. In addition to its flexibility and effectiveness, a major appeal of the method is that it is easy to understand. •Joint Decorrelation is a powerful, easy to use tool for multichannel data analysis.•It finds optimal weights to be applied to signals to maximize a criterion.•It can factor out noise, enhance weak sources, reveal oscillatory activity, etc.•It has been found effective for EEG, MEG, ECoG, LFP and optical imaging data.•We give examples of useful applications, and review failure scenarios and caveats.

Aims While acute itch comprises histamine‐dependent and ‐independent subtypes, critical mechanisms underlying histamine‐independent itch remain poorly understood. This study investigates the role of paired‐like homeobox 2a (Phox2a) in tachykinin 1‐positive (Tac1+) neurons of the lateral spinal nucleus (LSN) as a novel target for histamine‐independent pruritus intervention. Methods We combined chemogenetic manipulation (viral‐mediated neuronal activation/inhibition), whole‐cell patch‐clamp recordings, immunohistochemistry, fluorescence in situ hybridization, Western blotting, and behavioral assays to investigate the role of LSNTac1 neurons and Phox2a in itch modulation. Results LSNTac1 neurons were specifically activated during chloroquine (CQ)–induced histamine‐independent itch. Chemogenetic activation of these neurons exacerbated scratching, whereas inhibition suppressed itch behavior. Notably, Phox2a, expressed in LSNTac1 neurons, was downregulated during CQ‐induced itch. Overexpression of Phox2a in LSNTac1 neurons significantly alleviated CQ‐evoked scratching and was accompanied by a reduction in spontaneous excitatory postsynaptic currents (sEPSCs) amplitude without a change in sEPSCs frequency. Conclusions Our findings identify Phox2a in LSNTac1 neurons as a selective regulator of histamine‐independent acute itch through presynaptic excitability. This highlights Phox2a as a novel therapeutic target for histamine‐independent pruritus intervention. Phox2a expression in LSNTac1 neurons is selectively reduced in chloroquine‐induced itch. Overexpression of Phox2a alleviates this itch by the amplitude of spontaneous excitatory postsynaptic currents, revealing a potential treatment target for histamine‐independent pruritus.

Electroencephalography (EEG) has, historically, played a focal role in the assessment of neural function in children with attention deficit hyperactivity disorder (ADHD). We review here the most recent developments in the utility of EEG in the diagnosis of ADHD, with emphasis on the most commonly used and emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical heterogeneity of ADHD and the complexity of information available from the EEG signals, we suggest that considerable benefits are to be gained from multivariate analyses and a focus towards understanding of the neural generators of EEG. We conclude that while EEG cannot currently be used as a diagnostic tool, vast developments in analytical and technological tools in its domain anticipate future progress in its utility in the clinical setting.