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Within‐stand nutrient cycling is dependent on many factors, including primary productivity, nutrient‐use efficiency, nutrient resorption, sclerophylly, decomposition, nutritional quality of plant tissue, and allocation to defense. The efficiency of these plant‐mediated processes depends on nutrient availability in the environment and inherent functional properties of plants. However, little is known about how nutrient availability will affect these processes in forested wetlands in the tropics. In a factorial experiment we fertilized 48 dwarfed Rhizophora mangle (red mangrove) trees along tidal‐elevation and water‐depth gradients at Twin Cays, a range of intertidal, peat‐based offshore mangrove islands in Belize, Central America. Initial results indicated that phosphorus (P) deficiency is a major factor limiting primary productivity. Phosphorus‐fertilized trees had a significant decrease in P‐use efficiency and P‐resorption efficiency, but a significant increase in nitrogen (N)‐use efficiency and N‐resorption efficiency in their leaves compared with controls and N‐fertilized trees. Sclerophylly decreased dramatically in P‐fertilized trees, while the nutritional quality of the plant tissue increased. Phosphorus fertilization did not affect P leaching from green leaves. We found no fertilizer effect on the decomposition rates of leaf tissue, possibly due to higher phenolic concentrations in the P‐fertilized trees compared with controls and N‐fertilized trees. However, belowground decomposition of cotton strips increased in the substrate associated with P‐fertilized trees. Environmental conditions related to position along a tidal gradient may be as important as nutrients in controlling belowground decomposition.

ContextThe impact of landscape structure—often described by landscape composition and configuration—on ecological processes is well-known. Appropriately quantifying landscape structure that critically affect nutrient processes within watersheds remains challenging.ObjectiveA precipitation-weighted landscape structure model (LSM) was developed to predict nutrient concentrations at a large number of watersheds.MethodsThe LSM was developed based on the landscape location features including topography and precipitation within the watershed. The inequality function of Lorenz curve was used to quantify the spatial structure of different landscape types. The LSM was fitted and validated using the measurements of total nitrogen (TN) and total phosphorus (TP) in 132 watersheds. Regression models predicted the spatial patterns of TN and TP concentrations in 1578 watersheds of the Haihe River Basin, China.Results(1) Predictive models can explain 64 and 52% of the variation in total TN and TP, respectively. (2) Agricultural and residential lands served as nutrient sources. The contributions of agricultural land were 18 and 21% while those of residential land were 46 and 38% to TN and TP concentrations, respectively. (3) Grassland and forest land were nutrient sinks. Grassland had major contributions of 22 and 30% to TN and TP concentrations, respectively. The contributions of forest land were 7 and 11% to TN and TP concentrations, respectively.ConclusionsThe LSM focuses on the nutrient processes and is feasible to implement across a large number of watersheds. This study provides useful implications for quantifying landscape structure and predicting potential pollution under different landscape scenarios.

Reactive Transport Models (RTMs) are essential tools for understanding and predicting intertwined ecohydrological and biogeochemical processes on land and in rivers. While traditional RTMs have focused primarily on subsurface processes, recent watershed‐scale RTMs have integrated ecohydrological and biogeochemical interactions between surface and subsurface. These emergent, watershed‐scale RTMs are often spatially explicit and require extensive data, computational power, and computational expertise. There is however a pressing need to create parsimonious models that require minimal data and are accessible to scientists with limited computational background. To that end, we have developed BioRT‐HBV 1.0, a watershed‐scale, hydro‐biogeochemical RTM that builds upon the widely used, bucket‐type HBV model known for its simplicity and minimal data requirements. BioRT‐HBV uses the conceptual structure and hydrology output of HBV to simulate processes including advective solute transport and biogeochemical reactions that depend on reaction thermodynamics and kinetics. These reactions include, for example, chemical weathering, soil respiration, and nutrient transformation. The model uses time series of weather (air temperature, precipitation, and potential evapotranspiration) and initial biogeochemical conditions of subsurface water, soils, and rocks as input, and output times series of reaction rates and solute concentrations in subsurface waters and rivers. This paper presents the model structure and governing equations and demonstrates its utility with examples simulating carbon and nitrogen processes in a headwater catchment. As shown in the examples, BioRT‐HBV can be used to illuminate the dynamics of biogeochemical reactions in the invisible, arduous‐to‐measure subsurface, and their influence on the observed stream or river chemistry and solute export. With its parsimonious structure and easy‐to‐use graphical user interface, BioRT‐HBV can be a useful research tool for users without in‐depth computational training. It can additionally serve as an educational tool that promotes pollination of ideas across disciplines and foster a diverse, equal, and inclusive user community. Plain Language Summary Reactive Transport models (RTMs) are essential tools to understand the movement of water, nutrients and other elements from land to rivers and their interactions with each other. Recent watershed scale RTMs, unlike earlier ones that primarily focus on the subsurface processes, have integrated belowground processes and above‐ground dynamics and characteristics including changing weather and vegetation cover. However, these models require large amount of data and are challenging for users with limited computational background. Here we developed BioRT‐HBV 1.0, a parsimonious, watershed‐scale RTM with a graphical user interface that is comparatively easy to learn and use and requires minimal data. BioRT‐HBV can simulate a wide variety of processes like chemical weathering, carbon and nutrient transformation, soil organic carbon decomposition, among others. Here, we introduce the model structure, its governing equations, and examples that demonstrate the use of model in simulating carbon and nitrogen processes. We put forward this model as a potential research and educational tool that can be used by students and researchers from diverse disciplines. Key Points We introduce BioRT‐HBV, a watershed scale reactive transport model that is parsimonious, flexible with reaction network, easy to use and requires minimal data BioRT‐HBV can simulate a variety of user‐defined biogeochemical processes, including carbon and nitrogen processes BioRT‐HBV is open source for any researchers interested in ecohydrological and biogeochemical reactive transport processes

The dynamically evolving science of pharmacology requires AI technology to advance a new path for drug development. The author proposes generative AI for future drugs, identifying suitable drug molecules, uncharacteristically to previous generations of medicines, incorporating the wisdom, experience, and intuit of traditional materia medica and the respective traditional medicine practitioners. This paper describes the guiding principles of the new drug development, springing from the tradition and practice of Tibetan medicine, defined as the Interactive Nutrient Process (INP). The INP provides traditional knowledge and practitioner's experience, contextualizing and teaching the new drug therapy. An illustrative example of the outcome of the INP is a potential small molecule drug, 6-Shogaol and related shogaol derivatives, from ginger roots (Zingiber officinalis fam. Zingiberaceae) evaluated clinically for 12 months for biological markers of iron homeostasis in patients with the myelodysplastic syndromes (MDS). The study's preliminary results indicate that 6-Shogaol and related shogaols may improve iron homeostasis in low-risk/intermediate-1 MDS patients without objective or subjective side effects.

Nutrients in exotic species and invaded communities play a key role in determining the dynamics of invaders and the invasibility of a receipt community. This study focused on the effects of the native holoparasite Cuscuta campestris (for short Cuscuta) on nutrients in the exotic invasive Mikania micrantha (for short Mikania) and stands invaded by Mikania. We conducted a set of field investigations on Mikania with Cuscuta parasitism for 1-4 years, and measured soil properties, community composition, and the growth and nutrient content of Mikania and Cuscuta in two types of sub-communities (i.e. with Mikania only, or with Mikania and Cuscuta). Cuscuta dramatically reduced the cover, biomass, and nutrients (i.e. N, P, and K content) of Mikania, significantly enhanced soil water, pH and nutrient content (i.e. organic matter, total N and P, available P and K), and greatly increased the cover and species richness of native plants. In addition, N and K of Cuscuta were positively correlated with N of Mikania, which was negatively associated with soil total N, available P and K. These findings suggest that Cuscuta may be an effective measure against Mikania and be beneficial to the restoration of invaded communities.

The main objective of wastewater treatment plant is to release safe effluent not only to human health but also to the natural environment. An aerobic granular sludge technology is used for nutrient removal of wastewater treatment process using sequential batch reactor system. The nature of the process is highly complex and nonlinear makes the prediction of biological treatment is difficult to achieve. To study the nonlinear dynamic of aerobic granular sludge, high temperature real data at 40˚C were used to model sequential batch reactor using artificial neural network. In this work, the radial basis function neural network for modelling of nutrient removal process was studied. The network was optimized with self-organizing radial basis function neural network which adjusted the network structure size during learning phase. Performance of both network were evaluated and compared and the simulation results showed that the best prediction of the model was given by self-organizing radial basis function neural network.

One of the main parameters involved in the yeast-to-hypha transition in Candida albicans is temperature, and this change is involved in its pathogenicity. A complete switch between yeast and hypha can be achieved by changing the temperature from 28°C to 37°C in Lee medium supplemented with serum. To compare the early transcriptional response of C. albicans to temperature, we have carried out a genome-wide analysis of the C. albicans response to temperature after a 5-min exposure at 37°C. Using a cDNA microarray method, we found changes in 1,635 genes, suggesting that the key time for controlling the dimorphic change occurs very early. The overrepresented categories of up-regulated genes consisted of transporters, transcription factor and translation initiation factors, ribosomal proteins, DNA-directed RNA polymerase, cell cycle and cell polarity, RNA helicase and genes encoding polyamine biosynthesis. The main categories of down-regulated genes included: carbohydrate metabolism, actin filament organization, electron transport and ATP biosynthesis, respiration, histone assembly, and ergosterol biosynthesis. Collectively, these results demonstrate that much of the gene regulation observed is during the early stage of yeast-to-hypha transition.

A new and more appropriate continuous recycled system for aBiological Nutrient Removal process has been developed based on a sequencing batch reactor. This system comprises a Continuously Stirred Tank Reactor, a surge tank and a settling tank, from which a fraction of treated water is recycled back to the reactor. To design the control system for the whole plant, step tests have been conducted and Relative Gain Array analysis performed. Six control loops with the Process Variables including dissolved oxygen and nitrate concentrations, and volume holdups have been formed. Two designed control strategies Proportional Integral controllers and Generic Model Control have been implemented. The simulated results will be presented for comparison.

The activity of H⁺-ATPases of plant and fungi generates an electrochemical gradient of H⁺ across the cell plasma membrane that drives a number of secondary transport systems, including those responsible for the translocation of cations, anions, amino acids and sugars. During the last years, several studies have been aimed at elucidating the role of plasma membrane H⁺-ATPases in the nutrient exchange processes taking place between the plant and the fungus in arbuscular mycorrhizal (AM) symbiosis. This paper reviews present knowledge about plasma membrane H⁺-ATPases and experimental evidence supporting the involvement of H⁺-ATPases of both organisms in the bidirectional transport of nutrients between partners. Molecular strategies that will provide further information on the function and regulation of plasma membrane H⁺-ATPases in AM symbiosis are presented and discussed.