Interplay Between Applied Force and Radical Attack in the Mechanochemical Chain Scission of Poly(acrylic acid) by Michael T. Robo (1342344)

Relative variable importance in predicting Conifer Refugia. by Teresa B. Chapman (2924709)

Concrete mix ratio. by Yan Chen (4308)

Presentation_1_Long-Term Conservation Effects of Protected Areas in Stochastic Population Dynamics.pdf by Nao Takashina (11358330)

Additional file 2 of Gut Microbiome dysbiosis and immune activation correlate with somatic and neuropsychiatric symptoms in COVID-19 patients by Paula L. Scalzo (20882386)

Interplay Between Applied Force and Radical Attack in the Mechanochemical Chain Scission of Poly(acrylic acid) by Michael T. Robo (1342344)

Interplay Between Applied Force and Radical Attack in the Mechanochemical Chain Scission of Poly(acrylic acid) by Michael T. Robo (1342344)

Hysteresis current controller. by Saad F. Al-Gahtani (17811090)

Harmonic mitigation techniques. by Saad F. Al-Gahtani (17811090)

Spatio-Temporal Variability of Carbon Monoxide Over South America Using Satellite-Sensed Data from 2003 to 2012 by Yara Luiza Farias dos Santos (6799046)

Interplay Between Applied Force and Radical Attack in the Mechanochemical Chain Scission of Poly(acrylic acid) by Michael T. Robo (1342344)

S1 Data - by Yan Chen (4308)

Interplay Between Applied Force and Radical Attack in the Mechanochemical Chain Scission of Poly(acrylic acid) by Michael T. Robo (1342344)

P_i‐treatment aggravates calcification and induces phenotypic transition of VSMCs. by Razia Rashid Rahil (22772495)

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. …”

FRPR-19 acts cell autonomously in FLP neurons and downstream interneurons. by Filipe Marques (245110)

“…(D) Cell-specific overexpression experiments in <i>frpr-19(wt)</i> background showing a strong reversal decrease with the <i>frpr-19p</i> promoter and a partial decrease with <i>mec-3p</i> and <i>glr-1p</i> promoter. …”

<i>Pocillopora</i> spp. corallum and corallite Random Forest model and Principal component analysis. by Guinther Mitushasi (21567784)

“…GSH09c represented in orange, GSH14 in blue, GSH01 in purple, GSH09b in green, GSH09a in black, GSH13c in red, GSH13a in dark blue, GSH12 in light green, GSH13b in yellow, GSH15 in cyan, GSH05 in grey, GSH10 in brown, GSH04 in pink. …”

DataSheet1_Chronic exposure to inhaled vaporized cannabis high in Δ9-THC suppresses Adderall-induced brain activity.zip by Jack M. Ognibene (19830132)

“…</p>Results<p>Mice exposed to cannabis when compared to placebo showed a decrease in brain activation to Adderall. The blunted Adderall response was characterized by a decrease in positive BOLD signal and increase in negative BOLD. …”

Sagittal Balance Parameters and Proximal Junctional Kyphosis in Adolescent Idiopathic Scoliosis: Dataset and Results. by Galateia Katzouraki (18243523)

“…All the analyzed factors were evaluated by using odds ratios and weighted mean differences with 95% confidence intervals. Moreover, the meta-analysis of proportions via MedCalc was used for analyzing quantitative data from the studies. …”

Efficacy of combined interventions including preventative NPIs (e.g., masking, distancing, lockdowns) and testing and isolation. by Emily Howerton (10988025)

“…Moves can maintain NPI intensity with a less sensitive test (thick arrows), decrease NPI intensity with the same test (medium arrows), and decrease NPI intensity with a less sensitive test (light arrows). …”