Effects of cell culture patterns on injury and inflammation induced by the S1 protein. by Yanru Liu (1662463)

Effects of S1 protein and exposure duration on co-cultures of HPAEpics and AMs. by Yanru Liu (1662463)

<i>Leptospira interrogans</i> from Okinawa induces epithelial barrier disruption with different kinetics. by Tetsuya Kakita (4922584)

Representative Western blots for proteins from specific gene groups, nitric oxide production, and NETs formation. by Daniel R. Ambruso (12042605)

Pediatric CPP with Praat and ADSV (Joshi et al., 2025) by Ashwini Joshi (21594817)

“…Age and <i>F</i>0 are significant predictors of CPP; however, the observed increase in CPP with increasing age in males is primarily due to the substantial decrease in <i>F</i>0 postpuberty. …”

Fig 1 - by Ingmar Lundquist (46422)

PIK3C3 promotes <i>Listeria monocytogenes</i> cell-to-cell spread in Caco-2 cells. by Steven J. Rolland (21376879)

PIK3C3 promotes <i>Shigella flexneri</i> cell-to-cell spread in Caco-2 cells. by Steven J. Rolland (21376879)

DNM2 promotes <i>Shigella flexneri</i> cell-to-cell spread. by Steven J. Rolland (21376879)

The sexual stage phenotype of <i>pbpp6</i> gene knockout clones. by Yonghui Feng (12227596)

PbPP6 disruption impairs the cGMP-PKG-Ca²⁺ signaling pathway. by Yonghui Feng (12227596)

PIK3C3 promotes the resolution of VLPs into DMVs through formation of PtdIns(3)P at the protrusion/VLP membrane. by Steven J. Rolland (21376879)

PIK3C3 promotes <i>Listeria monocytogenes</i> cell-to-cell spread in HT-29 cells through DMV escape. by Steven J. Rolland (21376879)

S1 File - by Ingmar Lundquist (46422)

“…Additionally, the significance of extracellular NO on GSIS was studied. …”

The phenotype of the Δ<i>ace1</i> and (hemi)cellulase production of various mutants. by Zhongjiao Liu (11877464)

PIK3C3 promotes <i>Shigella flexneri</i> cell-to-cell spread through resolution of VLPs into DMVs. by Steven J. Rolland (21376879)

Trends in spatial beta diversity over time. by Zoë J. Kitchel (21688386)

“…A lack of significant trend is shown in blue. The average linear trend across surveys (black line with 95% confidence interval in gray) is also plotted from a linear mixed effect model with a random slope and intercept for survey. …”

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

“…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …”

Changes in rumen fluid metabolites. by Shiqi Zhang (709298)

Data file without outliers. by Helena Wallin (6103205)

“…At follow-up, the increase in KYN and KYN/TRP was related to a decrease in ExCap in CKD 2–3. After adjusting for GFR, increase in KYN/TRP remained an independent significant predictor of a decline in ExCap in CKD 2–3.…”