Baseline characteristics by group. by Carol Kotliar (22351525)

“…</p><p>Participants</p><p>120 volunteers were enrolled, of which 76 participants (56 in the intervention group, 20 in the control group) met the study requirements and selected a site closest to their residence.</p><p>Methods</p><p>The MTT24.5 program consists of 12 weekly in-person sessions (totaling 24.5 hours), during which participants learned 40 knowledge units (DATA) and 100 learning techniques (TECHS). …”

Differential expression patterns and functional gene groups associated with SARS-CoV-2 viral load across serially collected samples. by Vasanthi Avadhanula (259546)

The basic flowchart of analysis in this study. by Jing Wang (6206297)

“…Subsequently, we attempted to classify AD patients by unsupervised clustering. Then, four machine-learning models were used to screen hub-genes from the DECAGs. …”

The enriched GO terms of the 19 DECAGs. by Jing Wang (6206297)

“…Subsequently, we attempted to classify AD patients by unsupervised clustering. Then, four machine-learning models were used to screen hub-genes from the DECAGs. …”

Primers of the tested genes. by Jing Wang (6206297)

“…Subsequently, we attempted to classify AD patients by unsupervised clustering. Then, four machine-learning models were used to screen hub-genes from the DECAGs. …”

Summary of patient demographic characteristics. by Jing Wang (6206297)

“…Subsequently, we attempted to classify AD patients by unsupervised clustering. Then, four machine-learning models were used to screen hub-genes from the DECAGs. …”

Differential gene sets associated with the transcriptional host response to SARS-CoV-2 infection across serially collected samples. by Vasanthi Avadhanula (259546)

Subchondral bone thickness in the distal radius and ulna. by Andy Xavier (20141395)

Genotype-based analysis of the LINE-1. by Wendy J. Bailey (14926392)

NAc dLight dopamine dynamics consistent with RPE predictions by models with Bayesian inference. by Albert J. Qü (21648066)

Data_Sheet_1_Post-implementation knowledge retention of stroke: the lasting influence of FAST Heroes.docx by Emilia Orologa (19768545)

The biological characteristics of the I7L protein (pI7L). by Meilin Li (1832905)

Illustration of the main differences in ulna morphology between running groups, based on morphometric and microarchitecture analyses. by Andy Xavier (20141395)

In-depth understanding of how the model works. by Antoine Grigis (6659024)

DDX5 and DDX17 are required for EBV lytic reactivation. by Praneet Kaur Sandhu (21156149)

Geography, Trajectories, and Controls of Coastal Water Quality: More Rapid Improvement in the Shallow Zone of the Chesapeake Bay by Qian Zhang (49333)

“…In the Chesapeake Bay, water quality degradation is manifested in low dissolved oxygen, poor water clarity, and decreased submerged aquatic vegetation acreage. This research combines long-term monitoring data, science-based assessment methods, and novel data analysis approaches (i.e., machine learning) toward understanding the geography, trajectories, and controls of water quality in the Chesapeake Bay, which provides an example for the assessment and management of complex coastal ecosystems. …”

Prediction of individual spatial patterns of nodal module overlap from structural brain features in children. by Tianyuan Lei (19683861)

Inhibition of KC activity with GtACR1 prevents the formation of shock-conditioned and DAN-conditioned olfactory memories. by Farhan Mohammad (256409)

“…<p><b>(a)</b> Optogenetic olfactory conditioning protocol: 2 odors were presented in sequence, one paired with green light; the 2 odors were then presented in different arms of the chamber to test the conditioned preference (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3002843#sec026" target="_blank">Methods</a> for details). <b>(b)</b> Silencing the KCs with ACR1 actuation decreased aversive shock memory by 75.0% [95 CI −44%, −104%]. …”

Raw qPCR data for Fig. 1. by Parisa Afzal Haghjoo (22453647)

Ablation results. by Gang Xu (219455)

“…The key to overcoming this challenge lies in effectively capturing the geometric relationships across multiple viewpoints, thereby establishing strong geometric priors. Current methods commonly back-project voxels onto images to align voxel-pixel features, yet during this process, pixel features are insufficiently involved in learning, leading to a decrease in geometric perception accuracy and, consequently, impacting detection performance. …”