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Summary: Neuromodulation can facilitate interactions between neurons to rescue impaired brain function after stroke. Here, we present a protocol for measuring putative monosynaptic connections between different cortical regions. We detail procedures for tetrode fabrication, implantation surgery, stroke induction in mice, multi-site in vivo electrophysiological recording, units clustering, principal neuron/interneuron classification, and functional connection analysis. This protocol allows us to understand the mechanisms of stroke recovery.For complete details on the use and execution of this protocol, please refer to Wang et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Neuromodulation can facilitate interactions between neurons to rescue impaired brain function after stroke. Here, we present a protocol for measuring putative monosynaptic connections between different cortical regions. We detail procedures for tetrode fabrication, implantation surgery, stroke induction in mice, multi-site in vivo electrophysiological recording, units clustering, principal neuron/interneuron classification, and functional connection analysis. This protocol allows us to understand the mechanisms of stroke recovery. For complete details on the use and execution of this protocol, please refer to Wang et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

In a continuous time nonlinear regression model the residual correlogram is considered as an estimator of the stationary Gaussian random noise covariance function. For this estimator the functional central limit theorem is proved in the space of continuous functions. The result obtained shows that the limiting sample continuous Gaussian random process coincides with the limiting process in the central limit theorem for standard correlogram of the random noise in the specified regression model.

The spatial autocorrelation measurement of land prices uses a covariance function to describe the spatial dependence and it can be identified as a geographic distance on the correlogram. The geographic distance of spatial dependence can state that land prices are interdependent to each other and scattered in the research area. Therefore, the purpose of this research is to define the geographic distance of spatial dependence on land prices using a nonparametric correlogram. A nonparametric approach to covariance functions using the composition of Bessel and Gaussian-type functions are adopted because they correspond to the positive definite characteristics. The cubic spline interpolation is used to refine the curve fitting, while the intersection between the nonparametric correlogram value C(h) against the horizontal axis is determined using the Jenkins Traub algorithm. The results showed that the nonparametric correlogram identified a geographic distance of land prices smaller than the correlogram used so far. A small distance means that the land price in a location is greatly affected by the neighbors compared to a larger distance.

The relevance of the study of identity formation stages of a university student is determined by changes in modern Russian higher education primarily related to digitalization processes. The article presents the description of students’ identity formation features under conditions of higher education digitalization. The expediency of using an ecopsychological (ontological) model of identity formation is shown, according to which the process of formation of subject qualities is viewed in the ontological continuum, i.e. “subject of spontaneous activity – subject of arbitrary action”. With this model used as a theoretical basis, the aim of the study was to empirically identify correlation relationships (correlograms) between the stages of identity formation in university students. We have put forward a hypothesis that the relationships between stages of identity formation are peculiar and describe the individual style of learning actions of a student in the temporal aspect. The necessity of using specifically developed methods of process diagnostics and nonparametric statistical methods has been shown. The results of a longitudinal study of the identity formation stages in the course of education at a technical university are presented (n = 150 students; first and second year students; M = 18,86 years; Sd = 0,79; 46% male), longitude duration is 8–10 weeks. There is an example of a correlogram, which demonstrates individual structure of correlation interrelations between the stages of student’s identity. Interpretations of this structure of interrelations are suggested taking into account the style of subjectivity formation. Recommendations on application of obtained data regarding the stages of identity formation in the university educational process are given.

This work presents an approach for text-independent and speaker-independent emotion recognition from speech in real application situations such as noisy and stressful talking conditions. We have incorporated a new way for feature extraction, representation, and noise reduction, replacing the frequently used cepstral features in the literature. The proposed algorithm is modeled as the combination of pitch-correlogram-based noise reduction pre-processing module, sparse-dense decomposition-based feature representation, and random forest classifier. The work is assessed in terms of efficiency and computational complexity using English and Arabic datasets corresponding to noisy and stressful talking conditions. Our system yields significant improvement in results in comparison with other techniques based on the same classifier model. The proposed network architecture achieves significant rise in performance correspond to the recent literature on benchmark datasets.

A correlogram is a statistical tool that is used to check time-series memory by computing the auto-correlation coefficient as a function of the time lag. If the time-series has no memory, then the auto-correlation must be close to zero for any time lag, otherwise if there is a memory, then the auto-correlations must be significantly different from zero. Therefore, based on the robust detrended cross-correlation coefficient, ρ DCCA , we propose the detrended correlogram method in this paper, which will be tested for some time-series (simulated and empirical). This new statistical tool is able to visualize a complete map of the auto-correlation for many time lags and time-scales, and can therefore analyze the memory effect for any time-series. [ABSTRACT FROM AUTHOR]

Human encroachment, urban and agricultural expansion, frequent droughts, eutrophication, infestation of weeds, and overexploitation of resources has predominantly impacted the water quality and biodiversity of the Baraila wetland. The present seasonal study has aimed to explore an under-researched subject of analyzing the water quality of the wetland through multi-metric biomonitoring approaches in 2022. A total of 24 water and benthic samples were collected from the four sampling locations. All the water parameters were within a suitable range to support a diverse range of flora and fauna, except total suspended solids (TSS) and nutrients, indicating toward the impact of agricultural expansion in the wetland's area. Of the 13 identified families of benthic macroinvertebrates, the semi-tolerant families of Viviparidae and Bithyniidae have dominated the wetland, with a relative abundance of 27.04 and 37.77%, respectively, in the comparative seasons. Moreover, the indices used in the study have categorized the wetland under moderately polluted condition. The Hierarchical Cluster Analysis (HCA) has revealed the sensitivity toward organic pollution and habitat preferences has played a major role in the species-specific assemblage of benthic community. The Mantel's correlogram further validates that the moderately polluted status of wetland has favored semi-tolerant to tolerant fauna in the wetland. HIGHLIGHTS The study uses biomonitoring to document the water quality of Baraila Wetland.; It was marked by the dominance of semi-tolerant to tolerant benthic species.; The TSS and nutrients mainly degrade the water quality of the wetland.; The diversity and biotic indices classified the wetland as moderately polluted.; The HCA and Mantel's correlogram have explained the assemblage of benthic fauna.;

In this article, single input–double output linear time-invariant systems are studied. Both components of system's impulse response function are supposed to be real-valued and L2-integrable. One component is unknown while the second one is controlled. The problem is to estimate the unknown component after observations of the other component. For this purpose, we apply cross-correlating of the outputs given that the input is a standard Wiener process on R. Weak asymptotic normality of appropriately centered estimators in spaces of continuous functions is proved. This enables us to construct confidence intervals in these spaces. Our results employ techniques related to Gaussian processes and bilinear forms of Gaussian processes. [ABSTRACT FROM AUTHOR]

Gaussian mixture models (GMMs) are commonly used in text-independent speaker identification systems. However, for large speaker databases, their high computational run-time limits their use in online or real-time speaker identification situations. Two-stage identification systems, in which the database is partitioned into clusters based on some proximity criteria and only a single-cluster GMM is run in every test, have been suggested in literature to speed up the identification process. However, most clustering algorithms used have shown limited success, apparently because the clustering and GMM feature spaces used are derived from similar speech characteristics. This paper presents a new clustering approach based on the concept of a pitch correlogram that captures frame-to-frame pitch variations of a speaker rather than short-time spectral characteristics like cepstral coefficient, spectral slopes, and so forth. The effectiveness of this two-stage identification process is demonstrated on the IVIE corpus of 110 speakers. The overall system achieves a run-time advantage of 500% as well as a 10% reduction of error in overall speaker identification.