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1. Exploring FlyBase Data Using QuickSearch

   https://doi.org/10.1002/cpz1.731  

   Marygold, Steven J.; the FlyBase Consortium (April 2023, Current Protocols)

   Abstract FlyBase (www.flybase.org) is the primary online database of genetic, genomic, and functional information aboutDrosophila melanogaster. The long and rich history ofDrosophilaresearch, combined with recent surges in genomic‐scale and high‐throughput technologies, means that FlyBase now houses a huge quantity of data. Researchers need to be able to query these data rapidly and intuitively, and the QuickSearch tool has been designed to meet these needs. This tool is conveniently located on the FlyBase homepage and is organized into a series of simple tabbed interfaces that cover the major data and annotation classes within the database. This article describes the functionality of all aspects of the QuickSearch tool. With this knowledge, FlyBase users will be equipped to take full advantage of all QuickSearch features and thereby gain improved access to data relevant to their research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Using the “Search FlyBase” tab of QuickSearch Basic Protocol 2: Using the “Data Class” tab of QuickSearch Basic Protocol 3: Using the “References” tab of QuickSearch Basic Protocol 4: Using the “Gene Groups” tab of QuickSearch Basic Protocol 5: Using the “Pathways” tab of QuickSearch Basic Protocol 6: Using the “GO” tab of QuickSearch Basic Protocol 7: Using the “Protein Domains” tab of QuickSearch Basic Protocol 8: Using the “Expression” tab of QuickSearch Basic Protocol 9: Using the “GAL4 etc” tab of QuickSearch Basic Protocol 10: Using the “Phenotype” tab of QuickSearch Basic Protocol 11: Using the “Human Disease” tab of QuickSearch Basic Protocol 12: Using the “Homologs” tab of QuickSearch Support Protocol 1: Managing FlyBase hit lists 

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2. Quantifying Soil Phosphorus Dynamics: A Data Assimilation Approach

   https://doi.org/10.1029/2018JG004903  

   Hou, Enqing; Lu, Xingjie; Jiang, Lifen; Wen, Dazhi; Luo, Yiqi (July 2019, Journal of Geophysical Research: Biogeosciences)

   Abstract The dynamics of soil phosphorus (P) control its bioavailability. Yet it remains a challenge to quantify soil P dynamics. Here we developed a soil P dynamics (SPD) model. We then assimilated eight data sets of 426‐day changes in Hedley P fractions into the SPD model, to quantify the dynamics of six major P pools in eight soil samples that are representative of a wide type of soils. The performance of our SPD model was better for labile P, secondary mineral P, and occluded P than for nonoccluded organic P (Po) and primary mineral P. All parameters describing soil P dynamics were approximately constrained by the data sets. The average turnover rates were labile P 0.040 g g⁻¹day⁻¹, nonoccluded Po 0.051 g g⁻¹day⁻¹, secondary mineral P 0.023 g g⁻¹day⁻¹, primary mineral P 0.00088 g g⁻¹day⁻¹, occluded Po 0.0066 g g⁻¹day⁻¹, and occluded inorganic P 0.0065 g g⁻¹day⁻¹, in the greenhouse environment studied. Labile P was transferred on average more to nonoccluded Po (transfer coefficient of 0.42) and secondary mineral P (0.38) than to plants (0.20). Soil pH and organic C concentration were the key soil properties regulating the competition for P between plants and soil secondary minerals. The turnover rate of labile P was positively correlated with that of nonoccluded Po and secondary mineral P. The pool size of labile P was most sensitive to its turnover rate. Overall, we suggest data assimilation can contribute significantly to an improved understanding of soil P dynamics. 

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3. A Century‐Long Trajectory of Phosphorus Loading and Export From Mississippi River Basin to the Gulf of Mexico: Contributions of Multiple Environmental Changes

   https://doi.org/10.1029/2022GB007347  

   Bian, Zihao; Pan, Shufen; Wang, Zhuonan; Yao, Yuanzhi; Xu, Rongting; Shi, Hao; Kalin, Latif; Anderson, Christopher; Justic, Dubravko; Lohrenz, Steven; et al (June 2022, Global Biogeochemical Cycles)

   Abstract Phosphorus (P) control is critical to mitigating eutrophication in aquatic ecosystems, but the effectiveness of controlling P export from soils has been limited by our poor understanding of P dynamics along the land‐ocean aquatic continuum as well as the lack of well‐developed process models that effectively couple terrestrial and aquatic biogeochemical P processes. Here, we coupled riverine P biogeochemical processes and water transport with terrestrial processes within the framework of the Dynamic Land Ecosystem Model to assess how multiple environmental changes, including fertilizer and manure P uses, land use, climate, and atmospheric CO₂, have affected the long‐term dynamics of P loading and export from the Mississippi River Basin to the Gulf of Mexico during 1901–2018. Simulations show that riverine exports of dissolved inorganic phosphorus (DIP), dissolved organic phosphorus, particulate organic phosphorus (POP), and particulate inorganic phosphorus (PIP) increased by 42%, 53%, 60%, and 53%, respectively, since the 1960s. Riverine DIP and PIP exports were the dominant components of the total P flux. DIP export was mainly enhanced by the growing mineral P fertilizer use in croplands, while increased PIP and POP exports were a result of the intensified soil erosion due to increased precipitation. Climate variability resulted in substantial interannual and decadal variations in P loading and export. Soil legacy P continues to contribute to P loading. Our findings highlight the necessity to adopt effective P management strategies to control P losses through reductions in soil erosion, and additionally, to improve P use efficiency in crop production. 

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4. Data accompanying "Drought Characterization with GPS: Insights into Groundwater and Reservoir Storage in California" [Young et al., (2024)]

   https://doi.org/10.6084/m9.figshare.25898044.v1  

   Young, Zachary M; Martens, Hilary R; Hoylman, Zachary H; Gardner, W Payton (June 2024, figshare)

   The data provided here accompany the publication "Drought Characterization with GPS: Insights into Groundwater and Reservoir Storage in California" [Young et al., (2024)] which is currently under review with Water Resources Research. (as of 28 May 2024)Please refer to the manuscript and its supplemental materials for full details. (A link will be appended following publication)File formatting information is listed below, followed by a sub-section of the text describing the Geodetic Drought Index Calculation. The longitude, latitude, and label for grid points are provided in the file "loading_grid_lon_lat".Time series for each Geodetic Drought Index (GDI) time scale are provided within "GDI_time_series.zip".The included time scales are for 00- (daily), 1-, 3-, 6-, 12- 18- 24-, 36-, and 48-month GDI solutions.Files are formatted following...Title: "grid point label L****"_"time scale"_monthFile Format: ["decimal date" "GDI value"]Gridded, epoch-by-epoch, solutions for each time scale are provided within "GDI_grids.zip".Files are formatted following...Title: GDI_"decimal date"_"time scale"_monthFile Format: ["longitude" "latitude" "GDI value" "grid point label L****"]2.2 GEODETIC DROUGHT INDEX CALCULATION We develop the GDI following Vicente-Serrano et al. (2010) and Tang et al. (2023), such that the GDI mimics the derivation of the SPEI, and utilize the log-logistic distribution (further details below). While we apply hydrologic load estimates derived from GPS displacements as the input for this GDI (Figure 1a-d), we note that alternate geodetic drought indices could be derived using other types of geodetic observations, such as InSAR, gravity, strain, or a combination thereof. Therefore, the GDI is a generalizable drought index framework. A key benefit of the SPEI is that it is a multi-scale index, allowing the identification of droughts which occur across different time scales. For example, flash droughts (Otkin et al., 2018), which may develop over the period of a few weeks, and persistent droughts (>18 months), may not be observed or fully quantified in a uni-scale drought index framework. However, by adopting a multi-scale approach these signals can be better identified (Vicente-Serrano et al., 2010). Similarly, in the case of this GPS-based GDI, hydrologic drought signals are expected to develop at time scales that are both characteristic to the drought, as well as the source of the load variation (i.e., groundwater versus surface water and their respective drainage basin/aquifer characteristics). Thus, to test a range of time scales, the TWS time series are summarized with a retrospective rolling average window of D (daily with no averaging), 1, 3, 6, 12, 18, 24, 36, and 48-months width (where one month equals 30.44 days). From these time-scale averaged time series, representative compilation window load distributions are identified for each epoch. The compilation window distributions include all dates that range ±15 days from the epoch in question per year. This allows a characterization of the estimated loads for each day relative to all past/future loads near that day, in order to bolster the sample size and provide more robust parametric estimates [similar to Ford et al., (2016)]; this is a key difference between our GDI derivation and that presented by Tang et al. (2023). Figure 1d illustrates the representative distribution for 01 December of each year at the grid cell co-located with GPS station P349 for the daily TWS solution. Here all epochs between between 16 November and 16 December of each year (red dots), are compiled to form the distribution presented in Figure 1e. This approach allows inter-annual variability in the phase and amplitude of the signal to be retained (which is largely driven by variation in the hydrologic cycle), while removing the primary annual and semi-annual signals. Solutions converge for compilation windows >±5 days, and show a minor increase in scatter of the GDI time series for windows of ±3-4 days (below which instability becomes more prevalent). To ensure robust characterization of drought characteristics, we opt for an extended ±15-day compilation window. While Tang et al. (2023) found the log-logistic distribution to be unstable and opted for a normal distribution, we find that, by using the extended compiled distribution, the solutions are stable with negligible differences compared to the use of a normal distribution. Thus, to remain aligned with the SPEI solution, we retain the three-parameter log-logistic distribution to characterize the anomalies. Probability weighted moments for the log-logistic distribution are calculated following Singh et al., (1993) and Vicente-Serrano et al., (2010). The individual moments are calculated following Equation 3. These are then used to calculate the L-moments for shape (), scale (), and location () of the three-parameter log-logistic distribution (Equations 4 – 6). The probability density function (PDF) and the cumulative distribution function (CDF) are then calculated following Equations 7 and 8, respectively. The inverse Gaussian function is used to transform the CDF from estimates of the parametric sample quantiles to standard normal index values that represent the magnitude of the standardized anomaly. Here, positive/negative values represent greater/lower than normal hydrologic storage. Thus, an index value of -1 indicates that the estimated load is approximately one standard deviation dryer than the expected average load on that epoch. *Equations can be found in the main text. 

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5. Ethane dehydrogenation performance and high temperature stability of silica supported cobalt phosphide nanoparticles

   https://doi.org/10.1039/D1CY01737C  

   Muhlenkamp, Jessica A.; LiBretto, Nicole J.; Miller, Jeffrey T.; Hicks, Jason C. (February 2022, Catalysis Science & Technology)

   A series of Co–P materials with varying P : Co ratio from 0 to 4 supported on SBA-15 were evaluated for ethane dehydrogenation (EDH) performance. In comparison to monometallic Co, the Co–P materials have improved ethylene selectivity from 41% for Co to 88–90% for Co–P, which was attributed to the segregation of Co atoms and the formation of partial positive Co δ + sites in the Co–P materials due to charge transfer. Among the Co–P materials studied, an optimum in stability was observed in those containing a P : Co ratio in the range 1 to 2. Below this range, limited P is available to adequately separate Co atoms. Above this range, the excess P promotes coke formation through possible acid catalyzed pathways. The stability of two of the Co–P materials containing the Co 2 P and CoP phase, respectively, were further tested for EDH at 700 °C. Under these conditions, the ethylene selectivity was 98%, and both materials remained active with little to no deactivation for over 4 h. In comparison to a Pt–Sn reference, both Co–P materials showed vastly improved stability. Additionally, both Co–P materials showed no signs of sintering after EDH at 700 °C and maintained their respective Co 2 P and CoP phases. These results demonstrate the catalytic improvement with P incorporation and highlights the high stability of Co–P, and possibly other metal phosphides, as high temperature EDH catalysts. 

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