Zero-Padding techniques in OFDM systems

Although the OFDM system has been gaining importance in recent years, the high peak to average power ratio is considered the main limitation of the system. The oversampling operation in the frequency-domain plays an essential role in the PAPR calculations precisely. The main purpose of the paper to draw attention to zero-padding methods which are used to oversampled baseband OFDM signals. Moreover, to study the influence of the zero-padding methods on the accuracy of the PAPR calculations, and the spectral spreading of the OFDM signals. Simulation results show that the zero-padding method which inserts the zeros at the center of the baseband OFDM signal is better than the other zero-padding methods in terms of both accuracy PAPR calculations and spectral distributio

A New Subblock Segmentation Scheme in Partial Transmit Sequence for Reducing PAPR Value in OFDM Systems

Partial transmit sequence (PTS) is considered an efficient algorithm to alleviate the high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. The PTS technique is depended on the partitioning the input data sequence into the several subblocks, and then weighting these subblocks with a group of the phase factors. There are three common types of partitioning schemes: interleaving scheme (IL-PTS), adjacent scheme (Ad-PTS), and pseudo-random scheme (PR-PTS). The three conventional partitioning schemes have various performances of the PAPR value and the computational complexity pattern which are considered the main problems of the OFDM system. In this paper, the three ordinary partition schemes are analyzed and discussed depending on the capability of reducing the PAPR value and the computational complexity. Furthermore, new partitioning scheme is introduced in order to improve the PAPR reduction performance. The simulation results indicated that the PR-PTS scheme could achieve the superiority in PAPR mitigation compared with the rest of the schemes at the expense of increasing the computational complexity. Furthermore, the new segmentation scheme improved the PAPR reduction performance better than that the Ad-PTS and IL-PTS schemes

Acute effects of subanesthetic ketamine on cerebrovascular hemodynamics in humans: A TD-fNIRS neuroimaging study

Quantifying neural activity in natural conditions (i.e. conditions comparable to the standard clinical patient experience) during the administration of psychedelics may further our scientific understanding of the effects and mechanisms of action. This data may facilitate the discovery of novel biomarkers enabling more personalized treatments and improved patient outcomes. In this single-blind, placebo-controlled study with a non-randomized design, we use time-domain functional near-infrared spectroscopy (TD-fNIRS) to measure acute brain dynamics after intramuscular subanesthetic ketamine (0.75 mg/kg) and placebo (saline) administration in healthy participants (n= 15, 8 females, 7 males, age 32.4 ± 7.5 years) in a clinical setting. We found that the ketamine administration caused an altered state of consciousness and changes in systemic physiology (e.g. increase in pulse rate and electrodermal activity). Furthermore, ketamine led to a brain-wide reduction in the fractional amplitude of low frequency fluctuations (fALFF), and a decrease in the global brain connectivity of the prefrontal region. Lastly, we provide preliminary evidence that a combination of neural and physiological metrics may serve as predictors of subjective mystical experiences and reductions in depressive symptomatology. Overall, our studies demonstrated the successful application of fNIRS neuroimaging to study the physiological effects of the psychoactive substance ketamine and can be regarded as an important step toward larger scale clinical fNIRS studies that can quantify the impact of psychedelics on the brain in standard clinical settings

Measuring acute effects of subanesthetic ketamine on cerebrovascular hemodynamics in humans using TD-fNIRS

Quantifying neural activity in natural conditions (i.e. conditions comparable to the standard clinical patient experience) during the administration of psychedelics may further our scientific understanding of the effects and mechanisms of action. This data may facilitate the discovery of novel biomarkers enabling more personalized treatments and improved patient outcomes. In this single-blind, placebo-controlled study with a non-randomized design, we use time-domain functional near-infrared spectroscopy (TD-fNIRS) to measure acute brain dynamics after intramuscular subanesthetic ketamine (0.75 mg/kg) and placebo (saline) administration in healthy participants (n = 15, 8 females, 7 males, age 32.4 ± 7.5 years) in a clinical setting. We found that the ketamine administration caused an altered state of consciousness and changes in systemic physiology (e.g. increase in pulse rate and electrodermal activity). Furthermore, ketamine led to a brain-wide reduction in the fractional amplitude of low frequency fluctuations, and a decrease in the global brain connectivity of the prefrontal region. Lastly, we provide preliminary evidence that a combination of neural and physiological metrics may serve as predictors of subjective mystical experiences and reductions in depressive symptomatology. Overall, our study demonstrated the successful application of fNIRS neuroimaging to study the physiological effects of the psychoactive substance ketamine in humans, and can be regarded as an important step toward larger scale clinical fNIRS studies that can quantify the impact of psychedelics on the brain in standard clinical settings

Nilearn for new use cases: Scaling up computational and community efforts

Introduction Nilearn (https://nilearn.github.io) is a well-established Python package that provides statistical and machine learning tools for fast and easy analysis of brain images with instructive documentation and a friendly community. This focus has led to its current position as a crucial part of the neuroimaging community’s open-source software ecosystem, supporting efficient and reproducible science [1]. It has been continuously developed over the past 10 years, currently with 900 stars, 500 forks, and 176 contributors on GitHub. Nilearn leverages and builds upon other central Python machine learning packages, such as Scikit-Learn [2], that are extensively used, tested, and optimized by a large scientific and industrial community. In recent years, efforts in Nilearn have been focused on meeting evolving community needs by increasing General Linear Model (GLM) support, interfacing with initiatives like fMRIPrep and BIDS, and improving the user documentation. Here we report on progress regarding our current priorities. Methods Nilearn is developed to be accessible and easy-to-use for researchers and the open-source community. It features user-focused documentation that includes a user guide and an example gallery as well as comprehensive contribution guidelines. Nilearn is also presented in tutorials and workshops throughout the year including the Montreal Artificial Intelligence and Neuroscience (MAIN) Educational Workshop, the OHBM Brainhack event, and for the Chinese Open Science Network. The community is encouraged to ask questions, report bugs, make suggestions for improvements or new features, and make direct contributions to the source code. We use the platforms Neurostars, GitHub, and Discord to interact with contributors and users on a daily basis. Nilearn adheres to best practices in software development including using version control, unit testing, and requiring multiple reviews of contributions. We also have a continuous integration infrastructure set up to automate many aspects of our development process and make sure our code is continuously tested and up-to-date. Results Nilearn supports methods such as image manipulation and processing, decoding, functional connectivity analysis, GLM, multivariate pattern analysis, along with plotting volumetric and surface data. In the latest release, cluster-level and TFCE-based family-wise error rate (FWER) control have been added to support the mass univariate and GLM analysis modules, expanding from the already implemented voxel-level correction method (see Fig1). Optimizing Nilearn’s maskers is also underway such as the recently added classes for handling multi-subject 4D image data. These also provide the option to use parallelization to speed up computation. In addition, Nilearn has introduced a new theme to update the documentation making the website more readable and accessible (https://nilearn.github.io/). This change also sets the stage for further improvement and modernisation of several aspects of the documentation, like the user guide. Development on Nilearn’s interfaces module added a new function to write BIDS-compatible model results to disk. This and further development of the BIDS interface will facilitate interaction with other relevant community tools such as FitLins [3]. Finally, several surface plotting enhancements are in progress including improving the API for background maps (see Fig2). Conclusion Nilearn is extensively used by researchers of the neuroimaging community due to its implementations of well-founded methods and visualization tools which are often essential in brain imaging research for quality control and communicating results. Recent work has highlighted areas where more active work is needed to scale the project both technically and socially, including: working with large datasets, better supporting analyses on the cortical surface, and advancing standard practice in neuroimaging statistics through active community outreach. References [1] Poldrack, R., Gorgolewski, K., Varoquaux, G. (2019). Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging Annual Review of Biomedical Data Science 2(1), 119-138. https://dx.doi.org/10.1146/annurev-biodatasci-072018-021237 [2] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825-2830. [3] Markiewicz, C. J., De La Vega, A., Wagner, A., Halchenko, Y. O., Finc, K., Ciric, R., Goncalves, M., Nielson, D. M., Kent, J. D., Lee, J. A., Bansal, S., Poldrack, R. A., Gorgolewski, K. J. (2022). poldracklab/fitlins: 0.11.0 (0.11.0). Zenodo. https://doi.org/10.5281/zenodo.7217447This poster was presented at OHBM 2023

Adaptive Density Estimation Based on the Mode Existence Test

The kernel persists as the most useful tool for density estimation. Although, in general, fixed kernel estimates have proven superior to results of available variable kernel estimators, Minnotte\u27s mode tree and mode existence test give us newfound hope of producing a useful adaptive kernel estimator that triumphs when the fixed kernel methods fail. It improves on the fixed kernel in multimodal distributions where the size of modes is unequal, and where the degree of separation of modes varies. When these latter conditions exist, they present a serious challenge to the best of fixed kernel density estimators. Capitalizing on the work of Minnotte in detecting multimodality adaptively, we found it possible to determine the bandwidth h adaptively in a most original fashion and to estimate the mixture normals adaptively, using the normal kernel with encouraging results

Enhancement of Antibacterial Activity of Paludifilum halophilum and Identification of N-(1-Carboxy-ethyl)-phthalamic Acid as the Main Bioactive Compound

The aim of this study was to determine the combined effect of fermentation parameters and enhance the production of cellular biomass and antibacterial compounds from Paludifilum halophilum SMBg3 using the response surface methodology (RSM). Eight variables were screened to assess the effects of fermentation parameters on growth and metabolite production by Taguchi experimental design. Among these, the initial pH, temperature, and the percentage of MgSO4·7H2O in the medium were found to be most influential. The Box–Behnken design was applied to derive a statistical model for the optimization of these three fermentation parameters. The optimal parameters were initial pH: 8.3, temperature growth: 44°C, and MgSO4·7H2O: 1.6%, respectively. The maximum yield of biomass and metabolite production were, respectively, 11 mg/mL dry weight and 15.5 mm inhibition zone diameter against Salmonella enterica, which were in agreement with predicted values. The bioactive compounds were separated by the thick-layer chromatography technique and analyzed by GC/MS, NMR (1D and 2D), and Fourier-transform infrared spectroscopy (FT-IR). In addition to several fatty acids, N-(1-carboxy-ethyl)-phthalamic acid was identified as the main antibacterial compound. This element exhibited a potent activity against the ciprofloxacin-resistant Salmonella enterica CIP 8039 and Pseudomonas aeruginosa ATCC 9027 with a minimum inhibitory concentration (MIC) value range of 12.5–25 μg/mL. Results demonstrated that P. halophilum strain SMBg3 is a promising resource for novel antibacterial production due to its high-level yield potential and the capacity for large-scale fermentation

Chemical Composition, Antibacterial Activity using Micro-broth Dilution Method and Antioxidant Activity of Essential Oil and Water Extract from Aerial Part of Tunisian Thymus algeriensis Boiss. & Reut.

In this study, we investigated the chemical composition, antioxidant and antibacterial activities of essential oil and aqueous extract from Thymus algeriensis Boiss. & Reut growing in Tunisia. GC/MS analysis of essential oil from the aerial part of the plant led to identifying 54 constituents representing 96.87 % of the total oil composition. Monoterpenes represented the major components of the essential oil. On the other hand, the aqueous extract and the essential oil were screened for their antioxidant and antibacterial activities. The latter was assessed against Gram-negative and Gram-positive bacterial strains using the broth dilution micro method for the determination of antibacterial activity. The antioxidant activity was evaluated using the DPPH scavenging activity and Ferric Reducing/Antioxidant Power (FRAP) assays. The essential oil was found to be more active in antibacterial screening (MIC = 0.54 μg/mL). Whereas the aqueous extract exhibited the most potent antioxidant activity with (IC50 = 0.04 μg/mL). UHPLC ESI(-)-HRMS/MS analysis of the aqueous extract allowed tentative identification of its constituents and showed that it is rich in phenolic compounds. Luteolin-glucuronide was found to be the most abundant compound followed by Vicenin-2 and Apigenin-diglucuronide.This work was supported by the Ministry of Higher Education, Scientific Research and Technologies, Tunisia, allowed to the Laboratory of Organic Chemistry, Natural Substances Section (LR17/ ES08), Faculty of Sciences of Sfax, University of Sfax. The authors S. Fontanay and R.E. Duval are grateful to the Ministry of Higher Education, Research and Innovation, France, CNRS and Région Grand-Est which financed part of this work. The authors are thankful to Professor Leandros A Skaltsounis, Division of Pharmacognosy and Natural Products Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens for precious facilities in LCESI-HRMS/MS analysis

New low‐complexity segmentation scheme for the partial transmit sequence technique for reducing the high PAPR value in OFDM systems

Orthogonal frequency division multiplexing (OFDM) has been the overwhelmingly prevalent choice for high‐data‐rate systems due to its superior advantages compared with other modulation techniques. In contrast, a high peak‐to‐average‐power ratio (PAPR) is considered the fundamental obstacle in OFDM systems since it drives the system to suffer from in‐band distortion and out‐of‐band radiation. The partial transmit sequence (PTS) technique is viewed as one of several strategies that have been suggested to diminish the high PAPR trend. The PTS relies upon dividing an input data sequence into a number of subblocks. Hence, three common types of the subblock segmentation methods have been adopted—interleaving (IL‐PTS), adjacent (Ad‐PTS), and pseudorandom (PR‐PTS). In this study, a new type of subblock division scheme is proposed to improve the PAPR reduction capacity with a low computational complexity. The results indicate that the proposed scheme can enhance the PAPR reduction performance better than the IL‐PTS and Ad‐PTS schemes. Additionally, the computational complexity of the proposed scheme is lower than that of the PR‐PTS and Ad‐PTS schemes