<b>Single-dose replicon RNA Sudan virus vaccine uniformly protects female guinea pigs from disease</b> by Andrea Marzi (758337)

“…Antigen-specific humoral responses correlate with decreased virus replication and survival. This result warrants further studies in larger animal species to ensure that protective efficacy is maintained with the single-dose LION-SUDV vaccine.…”

<b>Rhizosphere response and resistance to fertilization</b> by Ran Tong (16888545)

“…This high resistance stems from stronger microbial control over chemical properties and a larger gap in response variability between rhizosphere and bulk soil. …”

Raw experimental data Fig 4. by Feline F. W. Benavides (13694053)

“…Viral RNA was detected in the brains of ferrets sacrificed 7 dpi, but <i>in situ</i> hybridization nor immunohistochemistry did confirm evidence for viral RNA or antigen in the brain. …”

Raw experimental data Fig 2. by Feline F. W. Benavides (13694053)

“…Viral RNA was detected in the brains of ferrets sacrificed 7 dpi, but <i>in situ</i> hybridization nor immunohistochemistry did confirm evidence for viral RNA or antigen in the brain. …”

Raw experimental data Fig 3. by Feline F. W. Benavides (13694053)

“…Viral RNA was detected in the brains of ferrets sacrificed 7 dpi, but <i>in situ</i> hybridization nor immunohistochemistry did confirm evidence for viral RNA or antigen in the brain. …”

Capsid stability can be affected by heat or CA mutations. by Tiana M. Scott (13825069)

“…Heat treatment slightly decreased the ability of plasmid DNA to activate cGAS (possibly through partial denaturation and/or aggregation of the DNA into a less accessible structure). …”

Inferring intrinsic noise intensities and reconstructing experimental data via END-nSDE. by Jiancheng Zhang (181059)

“…Groups of experimental and nSDE-reconstructed trajectories ranked by decreasing cosine similarity: #1 (E), #4 (F), #16 (G), #29 (H). …”

Effect of Molecular Structure on the B3LYP-Computed HOMO–LUMO Gap: A Structure −Property Relationship Using Atomic Signatures by Ahmed Mohamed (628889)

“…The developed QSPR can be utilized as a reliable initial screening tool to identify potential candidates possessing low <i>E</i>_gap values.…”

Extended data - The SnackerTracker: A novel home-cage monitoring device for measuring food-intake and food-seeking behaviour in mice by Marissa Mueller (20545196)

“…<p dir="ltr">ED - 1 - <i>SnackerTracker</i> Design Criteria and Constraints. …”

Image 1_Differential Ca2+ handling by isolated synaptic and non-synaptic mitochondria: roles of Ca2+ buffering and efflux.tif by Jyotsna Mishra (3703195)

“…Since, non-synaptic mitochondria displayed a significantly reduced ss[Ca²⁺]_e, this suggested a larger mCa²⁺ buffering capacity to maintain [Ca²⁺]_m with increasing mCa²⁺ loads. …”

Image 2_Differential Ca2+ handling by isolated synaptic and non-synaptic mitochondria: roles of Ca2+ buffering and efflux.tif by Jyotsna Mishra (3703195)

“…Since, non-synaptic mitochondria displayed a significantly reduced ss[Ca²⁺]_e, this suggested a larger mCa²⁺ buffering capacity to maintain [Ca²⁺]_m with increasing mCa²⁺ loads. …”

Image 3_Differential Ca2+ handling by isolated synaptic and non-synaptic mitochondria: roles of Ca2+ buffering and efflux.tif by Jyotsna Mishra (3703195)

“…Since, non-synaptic mitochondria displayed a significantly reduced ss[Ca²⁺]_e, this suggested a larger mCa²⁺ buffering capacity to maintain [Ca²⁺]_m with increasing mCa²⁺ loads. …”

Chemical kinetic analysis of multi-pollutant emissions from high temperature combustion of sewage sludge by Hui Chen (28597)

“…The results demonstrated good agreement between the experimental data and the simulated values of the simplified mechanism. Further analysis revealed that at a residence time 4 seconds and an excess air factor of 1.38, temperature had a significant effect on pollutant emissions. …”

Synergy is not captured by low-dimensional manifolds. by Thomas F. Varley (8446899)

“…<b>Top right:</b> The same pattern can be seen in synergy-dominated triads as well: a more strongly negative value is associated with decreasing variance explained by the first principal components. …”

Simulation of the effect of the experimental conditions on CSD speed. by Emre Baspinar (19369102)

“…The black arrow indicates the value of <i>c</i>₂ used in (B). (<b>E</b>) Simulation of concomitant blocking of receptors with GBZ and activation of ChR2, modeled by decreasing and increasing <i>c</i>₂, respectively, in which the propagation speed shows a large increase (purple arrow). …”

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

“…(H) RPE predictions from different models plotted against dopamine peak values (in black). (I) Left: Relative change in dopamine as R_chosen (past rewards observed at the selected port) and R_unchosen (past rewards observed at the opposing port) change, calculated via LMER regression weights for dopamine observed in trials where the animals switched their port choices (animal switch trials). …”

The risk of contracting VL. by Cameron Davis (19803404)

“…The parameter values are as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0311314#pone.0311314.t001" target="_blank">Table 1</a>.…”

Decision-maker informed optimal policy design. by Hongru Du (4640212)

“…Increasing <i>a</i> places greater emphasis on minimizing policy cost, while decreasing it prioritizes reducing infections averted. …”

SOX10 mRNA levels are not sensitive to glucose or G-6-P concentrations. by Ana Luisa Dian (22258504)

“…<p><b>(A)</b> RT-qPCR quantification of the <i>SOX10</i> mRNA level in A375 cells cultured for 24 h with decreasing concentrations of glucose or under glucose starvation, as indicated (SD, <i>n</i> = 3 biological replicates). …”

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

“…Only accurate for an individual HUC12.Calculated using RIMPRaw Important Areas for Surface Drinking Water (IMP) Value. 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. …”