Decreased spindle microtubules in CAMSAP2 KO cells. by Naoko Nishizawa (20536299)

Analysis of stress-related gene expression in cherry shrimp (<i>N</i>. <i>davidi</i> var. red). by Wei-Wei Hou (3405947)

Organic solvent exposure causes defects in salt chemotaxis learning. by Masahiro Tomioka (430683)

Changes in the eGFRs and serum potassium levels compared with baseline values in all patients over time. by Jia Song (495396)

MigC interacts with and decreases the activity of MurD. by Jeanette M. Critchlow (21553796)

Image 1_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…Introduction<p>This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.…”

Image 8_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…Introduction<p>This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.…”

Image 6_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…Introduction<p>This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.…”

Image 2_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…Introduction<p>This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.…”

Image 7_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…Introduction<p>This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.…”

Image 5_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…Introduction<p>This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.…”

Image 4_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…Introduction<p>This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.…”

Image 3_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…Introduction<p>This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.…”

HSF2 reversed the decrease in the MMP induced by Atr. by Wen Wang (6570)

Mean 0-30 m sprint times for TSG, VBT, and CG groups at pre-test and post-test (with 95% CI). by Murat Tutar (22250461)

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Experimental design. by Murat Tutar (22250461)

Subjects:

Group-based descriptive statistics and effect sizes. by Murat Tutar (22250461)

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Mean 0-20 m sprint times for TSG, VBT, and CG groups at pre-test and post-test (with 95% CI). by Murat Tutar (22250461)

Subjects:

Mean 0-10 m sprint times for TSG, VBT, and CG groups at pre-test and post-test (with 95% CI). by Murat Tutar (22250461)

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Composition of experimental diets fed to cherry shrimp (<i>N</i>. <i>davidi</i> var. red) including varying concentrations of astaxanthin and <i>Bidens pilosa</i> extract over a 8-... by Wei-Wei Hou (3405947)