The strength of the commute time-functional connectivity relationship demonstrates weak pathological significance and weak dependency on age. by Rostam M. Razban (22232522)

“…</b> Strengthening commute time-FC correlations shown in <b>B</b> seem to be driven by a decrease in average commute time across age. Note that all reported correlations are calculated by considering all data points, i.e., they are not calculated for the binned data.…”

NN analysis to assign the importance of variables. by Sulmaz Ghahramani (3387635)

Cortical mapping of fraction of significant voxels. by Satoshi Nishida (690175)

The number of people who searched for a waste bin, added a waste bin to their shopping cart or purchased a waste bin. by Yaopei Wang (14046943)

Temporal profiles of the key BO-NN features. by Julia Berezutskaya (9080269)

“…The shifts associated with non-significant prediction accuracies are greyed-out. <b>(c)</b> The results of the MDS for the key BO-NN features. …”

Principal component analyses indicates no significant separation based on the type of brain regions. by Marcell Miski (11965487)

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things by Arash GhorbanniaDelavar (22563696)

Comparison of the fraction of significant voxels between original and control word vector-based models. by Satoshi Nishida (690175)

Cortical mapping of the fraction of significant voxels for binary-labeling models. by Satoshi Nishida (690175)

Distribution of voxels across functional brain networks in significant seed-based connectivity clusters. by Sadeq Mohammadi (22622219)

“…<p>Bar plot showing the number of voxels within each large-scale brain network for significant clusters derived from seed-based connectivity analysis. …”

Classification of the predictors based on the statistical significances. by Tadashi Miyagawa (9547104)

Attenuation reduction significance between OCAs. by Mathew Loren (6689951)

Cortical mapping of the fraction of significant voxels for other dimensions of GloVe vectors. by Satoshi Nishida (690175)

Cortical mapping of the fraction of significant voxels for other dimensions of fastText vectors. by Satoshi Nishida (690175)

“…<p>The mean fraction of significant voxels in each brain region for fastText vector-based models was mapped onto the cortical surface (<b>A</b>, vector dimension = 100; <b>B</b>, 300; <b>C</b>, 500; <b>D</b>, 2000). …”

Consistency of inter-regional patterns of significant-voxel fraction for GloVe vectors. by Satoshi Nishida (690175)

Statistical summary of the two significant ROIs identified by ROI-based inference. by Sadeq Mohammadi (22622219)

Loss of Bin1 in neurons affects gene expression in microglia <i>in vivo</i>. by Kathleen M. McAvoy (7224863)

Cell-autonomous and non-cell autonomous effects of neuronal BIN1 loss <i>in vivo</i> by Kathleen M. McAvoy (7224863)

“…Furthermore, the level of neuronal-specific BIN1 isoform 1 protein is decreased in sporadic AD cases in parallel with neuronal loss, despite an overall increase in BIN1 total mRNA. …”

Genetic deletion of Bin1 in neurons alters neuronal activation <i>in vivo</i>. by Kathleen M. McAvoy (7224863)

Consistency of inter-regional patterns of significant-voxel fraction for word2vec vectors. by Satoshi Nishida (690175)