Overall effects for inorganic phosphate. by Luke R. Arieta (10517126)

ACh signals measured in rat mPFC by Howe et al. during the SAT. by Sahiti Chebolu (14035041)

Expression of the ETS-5 target gene <i>gcy-9</i> restores CO₂-chemosensitivity to <i>ets-5</i> mutants. by Julia P. Brandt (176082)

“…<p>(A) <i>gcy-36</i> and <i>gcy-18</i> promoters, which are active in oxygen-sensing and thermosensory neurons, respectively, are not regulated by ETS-5. …”

Upper panel: The minimum-norm solution for the difference between the Grand Means of the tinnitus and the control groups indicates a greater prefrontal activity (dark shading) in t... by Nathan Weisz (3118)

Box-whisker plots of the relative abundances (%) of coccolithophore species characteristic for the “pseudo-stadial” (PS; 1854.6–1852.8 cm), the interstadial before the YTT eruption... by Jose Dominick Guballa (19739909)

Intelligibility of dysphonic speech in classrooms (Botallico et al., 2023) by Pasquale Bottalico (16318258)

Image_1_A rhoptry protein, localizing in the bulb region of rhoptries, could induce protective immunity against Eimeria tenella infection.tif by Tuan-yuan Shi (17544435)

Speech features for Mandarin speakers with Parkinson's disease (He et al., 2023) by Deling He (16846591)

Narration: A systematic review and meta-analysis (Winters et al., 2022) by Katherine L. Winters (13851665)

Age, language use, and SiN in bilingual adults (Bieber et al., 2025) by Rebecca E. Bieber (18847614)

Carboxylate-Assisted β‑(<i>Z</i>) Stereoselective Hydrosilylation of Terminal Alkynes Catalyzed by a Zwitterionic Bis-NHC Rhodium(III) Complex by Raquel Puerta-Oteo (2867876)

The ratio 2-NBDG/R_D facilitates assessment of glucose demand in heterogeneous regions of metastatic mammary tumors. by Amy E. Frees (474256)

“…After correction for low R_D, 2-NBDG₆₀/R_D increased slightly but significantly in hypoxic regions (p<0.01 for 02,4T1<10 vs. 402,4T1<60). For 4T07, 2-NBDG uptake for the highest SO_2,4T07 regions decreased compared to the lowest SO_2,4T07 (p<0.01 for all 202,4T07<40 vs. 602,4T1<80). …”

Relationship between trial-to-trial formant change and N1 suppression. by Kevin R. Sitek (497401)

“…We found an overall trial Consistency effect on N1 suppression (<i>p</i> = .048) as well as a greater Near versus Far effect on SIS (<i>p</i> = .014), showing decreased N1 suppression when an utterance varies highly from its previous neighbor.…”

Data_Sheet_1_A New Analysis on Self-Control in Intertemporal Choice and Mediterranean Dietary Pattern.docx by Brian C. Howatt (6877706)

Table_1_Long-term exercise training and inflammatory biomarkers in healthy subjects: a meta-analysis of randomized controlled trials.docx by Ya-Hai Wang (13181307)

File S1 - Diminished Vision in Healthy Aging Is Associated with Increased Retinal L-Type Voltage Gated Calcium Channel Ion Influx by David Bissig (189073)

“…Mirrors placed on the floor and ceiling reflected a moving sine wave grating, displayed and distorted identically on each screen with Vision Egg (v.1.0), thereby forming a virtual barrel (i.e., the width of one dark-light-dark cycle appearing similar in all directions) when viewed from the center of the device. …”

Image_1_Tremor supression following treatment with MRgFUS: skull density ratio consistency and degree of posterior dentatorubrothalamic tract lesioning predicts long-term clinical... by Kain Kyle (15350116)

Image_1_Tremor suppression following treatment with MRgFUS: skull density ratio consistency and degree of posterior dentatorubrothalamic tract lesioning predicts long-term clinical... by Kain Kyle (15350116)

Measurements and analysis of colon crypt production. by Chin Wee Tan (183609)

“…(E) “Stage 2” and “Stage 1+2” crypt budding rates, showing a significant decrease with mice age. …”

Forests to Faucets 2.0 by U.S. Forest Service (17476914)

“…As developed in Forests to Faucets (USFS 2011), the Important Areas for Surface Drinking Water (IMP) model can be broken down into two parts: IMPn = (PRn) * (Qn)Calculated using R, Updated September 2023IMP_RIMP, Important Areas for Surface Drinking Water (0-100 Quantiles)Calculated using R, Updated September 2023NON_FORESTAcres of non-forestPADUS and NLCDPRIVATE_FORESTAcres of private forestPADUS and NLCDPROTECTED_FORESTAcres of protected forest (State, Local, NGO, Permanent Easement)PADUS, NCED, and NLCDNFS_FORESTAcres of National Forest System (NFS) forestPADUS and NLCDFEDERAL_FORESTAcres of Other Federal forest (Non-NFS Federal)PADUS and NLCDPER_FORPRIPercent Private ForestCalculated using ArcGISPER_FORNFSPercent NFS ForestCalculated using ArcGISPER_FORPROPercent Protected (Other State, Local, NGO, Permanent Easement, NFS, and Federal) ForestCalculated using ArcGISWFP_HI_ACAcres with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_WFPPercent of HU 12 with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_IDRISKPercent of HU 12 that is at risk for mortality - 25% of standing live basal area greater than one inch in diameter will die over a 15- year time frame (2013 to 2027) due to insects and diseases.Krist, et Al,. 2014PERDEV_1040_45% Landuse Change 2010-2040 (low)ICLUSPERDEV_1090_45% Landuse Change 2010-2090 (low)ICLUSPERDEV_1040_85% Landuse Change 2010-2040 (high)ICLUSPERDEV_1090_85% Landuse Change 2010-2090 (high)ICLUSPER_Q40_45% Water Yield Change 2010-2040 (low) WASSI , Updated September 2023PER_Q90_45% Water Yield Change 2010-2090 (low) WASSI , Updated September 2023PER_Q40_85% Water Yield Change 2010-2040 (high) WASSI , Updated September 2023PER_Q90_85% Water Yield Change 2010-2090 (high) WASSI , Updated September 2023WFP(APCW_R * IMP_R * PER_WFP )/ 10,000Wildfire Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023IDRISK(APCW_R * IMP_R * PER_IDRISK )/ 10,000Insect & Disease Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023DEV1040_45(APCW_R * IMP_R * PERDEV_1040_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_45(APCW_R * IMP_R * PERDEV_1090_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023DEV1040_85(APCW_R * IMP_R * PERDEV_1040_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_85(APCW_R * IMP_R * PERDEV_1090_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_45-1 * (APCW_R * IMP_R * PER_Q40_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_45-1 * (APCW_R * IMP_R * PER_Q90_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_85-1 * (APCW_R * IMP_R * PER_Q40_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_85-1 * (APCW_R * IMP_R * PER_Q90_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023WFP_IMP_RWildfire Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023IDRISK_RInsect & Disease Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023RegionUS Forest Service Region numberUSFSRegionnameUS Forest Service Region nameUSFSHUC_Num_DiffThis field compares the value in column HUC12(circa 2019 wbd) with the value in HUC_12 (circa 2009 wassi)-1 = No equivalent WASSI HUC. …”