The transcriptional response of IECs to <i>C</i>. <i>albicans</i> infection is largely damage- and filamentation-independent. by Jakob L. Sprague (18087148)

Supplementary Material for: Hidradenitis Suppurativa in a Large Cohort of Italian Patients: Evaluation of the Burden of Disease by Fabbrocini G. (7463126)

Associations of risk of lead exposure and area deprivation index with subcortical volume. by Andrew T. Marshall (10159199)

Fig 3 - by Barry Smyth (10826056)

Population fraction of risk-deniers affects epidemics in real networks. by Valeria d’Andrea (12095838)

Features of assembled parent communities. by Pablo Lechón-Alonso (11663433)

Serial coalescence of microbial communities. by Pablo Lechón-Alonso (11663433)

Differences in pTSS profiles between pre- and full-term pregnancies. by Zhiwei Guo (335819)

Modeling the relationship between size, environmental oxygen, and the fitness effects of myoglobin. by Whitney Wong (20642739)

“…(B) Simulated metabolic rate vs. depth of large (20 μm radius) and small (10 μm radius) clusters with and without 0.1 mM myoglobin expression. …”

Brain age monotonicity without application of a sliding window. by Siamak K. Sorooshyari (9014891)

Heatmaps with different genetic linkage and migration rate. by Georg Oberhofer (3401438)

“…As the degree of linkage decreases (e.g. 4cM and 10cM), so does the average frequency of <i>Rescue</i>/Cargo/gRNAs. …”

The Value of Dynamic Grip Force Modulation as a Potential Biomarkerfor Hand Function Recovery Following Stroke by Kirstin-Friederike Heise (7518953)

Study group background characteristics. by Zicheng Hu (3825313)

“…<b>C</b>. <b>Age</b>. The ages of the subjects were not specifically selected and consequently also varied among groups, with group medians ranging from 39 to 50 years with largely similar ranges. …”

The life cycle of intensifiers, with empirical examples. by Madison S. Krieger (4487053)

STMs for speech in impaired listeners (Venezia et al., 2019) by Jonathan H. Venezia (425218)

“…Ordinal regression was used to estimate weights showing which regions of the STM spectrum were associated with good performance (a “classification image” or CImg).</div><div><b>Results: </b>The results indicated that (a) large-scale CImg patterns did not differ between the 3 groups; (b) weights in a small region of the CImg decreased systematically as hearing loss increased; (c) CImgs were also nonsystematically distorted in OHI listeners, and the magnitude of this distortion predicted speech recognition performance even after accounting for audibility; and (d) YNH listeners performed better overall than the older groups.…”