Potential target protein interaction (PPI) network (Annotation: The smaller circle represents the target, and the larger the circle, the more important the target). by Lin Zhou (141050)

Seasonal spatial-temporal trend of NDVI in burned areas across Africa (2001–2020). Percentages (%) of increases or decreases (%) based on the Mann-Kendall test and statistically si... by Oswaldo Maillard (20660661)

RetinaFace multi-task loss function. by Muhammad Mobeen Abid (19106074)

Data Sheet 1_Significance of multi-task deep learning neural networks for diagnosing clinically significant prostate cancer in plain abdominal CT.docx by Yujun Geng (21232145)

“…Objective<p>Early detection and timely surgical intervention are crucial in reducing mortality rates associated with clinically significant prostate cancer (csPCa). …”

Shear stress-strain responses of the reticulocyte membrane at decreased connectivity of the cytoskeleton. by He Li (176971)

Univariate BOLD activity in the hippocampus did not significantly predict inference performance. by Sam C. Berens (13970497)

Statistically-significant cytokine changes between the pre-infusion and post-infusion blood draws plot of the mean (dot) and confidence interval (whiskers) for the coefficients of... by Jordan K. Abbott (8934239)

Subjects:

Inter-subject, intra-subject, common variance, and minimal detectable effect size (MDES), calculated at the well-rested visit. by Ingrid Koopmans (14559146)

Summary of significant LMM coefficients and contrasts on RTs. by Arianna Compostella (8843135)

Summary of significant GLMM coefficients on correct anticipations. by Arianna Compostella (8843135)

Summary of significant GLMM coefficients on correct anticipations. by Arianna Compostella (8843135)

Results of application of optimized machine learning approach for the task-related and non-task-related sEMG channels. by Ahmadreza Keihani (5297090)

Subjects:

Examples of pseudo-characters in Chinese VSL task. by Jinglei Ren (17055334)

Subjects:

The direct comparison of prediction errors and checkpoints revealed a significant N400 component peaking around 418 ms over parieto-central electrodes. by Daniel S. Kluger (5979131)

Spatial information is significantly decreased in dCA1 and vCA1 in APP/PS1 mice. by Udaysankar Chockanathan (18510288)

“…The spatial information in dCA1 was significantly larger than circularly shuffled spike trains with similar mean firing rates for C57BL/6 mice (mean ± std: empirical = 0.132 ± 0.048, shuffled = 0.124 ± 0.035, p < 0.001, two-sided Wilcoxon rank-sum test, n_empirical = 305 units from 5 recording sessions, n_shuffled = 30500 simulated units from 5 recording sessions), but not for APP/PS1 mice (mean ± std: empirical = 0.128 ± 0.051, shuffled = 0.123 ± .047, p = 0.39, two-sided Wilcoxon rank-sum test, n_empirical = 180 units from 4 recording sessions, n_shuffled = 18000 simulated units from 4 recording sessions). …”

Table_2_Electrophysiological and Behavioral Correlates of Valence, Arousal and Subjective Significance in the Lexical Decision Task.XLSX by Kamil K. Imbir (5906273)

“…Indeed, the results confirmed our expectations and showed that subjective significance is a factor independent from the arousal and valence that shapes the involuntary processing of verbal stimuli, especially the detection of a link between stimulus and meaning indexed by the FN400. …”

Table_3_Electrophysiological and Behavioral Correlates of Valence, Arousal and Subjective Significance in the Lexical Decision Task.DOCX by Kamil K. Imbir (5906273)

“…Indeed, the results confirmed our expectations and showed that subjective significance is a factor independent from the arousal and valence that shapes the involuntary processing of verbal stimuli, especially the detection of a link between stimulus and meaning indexed by the FN400. …”

Table_1_Electrophysiological and Behavioral Correlates of Valence, Arousal and Subjective Significance in the Lexical Decision Task.XLS by Kamil K. Imbir (5906273)

“…Indeed, the results confirmed our expectations and showed that subjective significance is a factor independent from the arousal and valence that shapes the involuntary processing of verbal stimuli, especially the detection of a link between stimulus and meaning indexed by the FN400. …”

Random mechanosensory weights enable learning with fewer neurons in time-series classification tasks. by Biraj Pandey (13933248)

Subjects:

SD-FSL-CLSTM spam detection framework. by Amna Iqbal (20685314)

Subjects: