Associative Nitrogen Fixation Linked With Three Perennial Bioenergy Grasses In Field and Greenhouse Experiments

© 2020 The Authors. Associative nitrogen (N2)‐fixation (ANF) by bacteria in the root‐zone of perennial bioenergy grasses has the potential to replace or supplement N fertilizer and support sustainable production of biomass, but its application in marginal ecosystems requires further evaluation. In this study, we first combined both greenhouse and field experiments, to explore the N2 fixation effects of three temperate feedstocks Miscanthus × giganteus (giant miscanthus, Freedom), Panicum virgatum (switchgrass, Alamo), and Saccharum sp. (energycane, Ho 02‐147). In field studies across three growing seasons, plant and soil pools of candidate feedstocks were partially composed of N derived from the atmosphere (Ndfa). Energycane, giant miscanthus, and switchgrass were estimated to derive \u3e30%, %Ndfa. Greenhouse studies were also performed to trace isotopically labeled 15N2 into plant biomass and soil pools. Evidence for Ndfa was detected in all three feedstock grasses (using reference 15N of soil, chicory, and sorghum, δ15N~+7.0). Isotopically labeled 15N2 was traced into biomass (during grass elongation stage) and soil pools. Extrapolation of rates during the 24 hr labeling period to 50 days estimated 30%–55% of plant Ndfa, with the greatest Ndfa for energycane. The findings of the field natural abundance and greenhouse 15N2 feeding experiments provided complementary evidence that perennial bioenergy grasses have the potential to support relatively high rates of ANF, and accumulate diazotroph‐derived N into biomass when grown on non‐fertilized soil

In Silico Discovery of JMJD6 Inhibitors for Cancer Treatment.

The 2-oxoglutarate (2OG)-dependent oxygenase JMJD6 is emerging as a potential anticancer target, but its inhibitors have not been reported so far. In this study, we reported an in silico protocol to discover JMJD6 inhibitors targeting the druggable 2OG-binding site. Following this protocol, one compound, which we named as WL12, was found to be able to inhibit JMJD6 enzymatic activity and JMJD6-dependent cell proliferation. To our best knowledge, this is the first case in drug discovery targeting JMJD6

Terrestrial-derived soil protein in coastal water: metal sequestration mechanism and ecological function.

Terrestrial fungi, especially arbuscular mycorrhizal (AM) fungi, enhance heavy metal sequestration and promote ecosystem restoration. However, their ecological functions were historically overlooked in discussions regarding water quality. As an AM fungi-derived stable soil protein fraction, glomalin-related soil protein (GRSP) may provide insights into the ecological functions of AM fungi associated with water quality in coastal ecosystems. Here, we first assessed the metal-loading dynamics and ecological functions of GRSP transported into aquatic ecosystems, characterized the composition characteristics, and revealed the mechanisms underlying Cu and Cd sequestration. Combining in situ sampling and in vitro cultures, we found that the composition characteristics of GRSP were significantly affected by the element and mineral composition of sediments. In situ, GRSP-bound Cu and Cd contributed 18.91-22.03% of the total Cu and 2.27-6.37% of the total Cd. Functional group ligands and ion exchange were the principal mechanisms of Cu binding by GRSP, while Cd binding was dominated by functional group ligands. During the in vitro experiment, GRSP sequestered large amounts of Cu and Cd and formed stable complexes, while further dialysis only released 25.74 ± 3.85% and 33.53 ± 3.62% of GRSP-bound Cu and Cd, respectively

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey

Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables. The emergence of generative Large Language Models (LLMs) has significantly impacted various NLP domains, particularly through their advanced reasoning capabilities. This survey focuses on evaluating and improving LLMs from a causal view in the following areas: understanding and improving the LLMs' reasoning capacity, addressing fairness and safety issues in LLMs, complementing LLMs with explanations, and handling multimodality. Meanwhile, LLMs' strong reasoning capacities can in turn contribute to the field of causal inference by aiding causal relationship discovery and causal effect estimations. This review explores the interplay between causal inference frameworks and LLMs from both perspectives, emphasizing their collective potential to further the development of more advanced and equitable artificial intelligence systems

Terrestrial-derived soil protein in coastal water: Metal sequestration mechanism and ecological function

Abstract(#br)Terrestrial fungi, especially arbuscular mycorrhizal (AM) fungi, enhance heavy metal sequestration and promote ecosystem restoration. However, their ecological functions were historically overlooked in discussions regarding water quality. As an AM fungi-derived stable soil protein fraction, glomalin-related soil protein (GRSP) may provide insights into the ecological functions of AM fungi associated with water quality in coastal ecosystems. Here, we first assessed the metal-loading dynamics and ecological functions of GRSP transported into aquatic ecosystems, characterized the composition characteristics, and revealed the mechanisms underlying Cu and Cd sequestration. Combining in situ sampling and in vitro cultures, we found that the composition characteristics of GRSP were significantly affected by the element and mineral composition of sediments. In situ , GRSP-bound Cu and Cd contributed 18.91–22.03% of the total Cu and 2.27–6.37% of the total Cd. Functional group ligands and ion exchange were the principal mechanisms of Cu binding by GRSP, while Cd binding was dominated by functional group ligands. During the in vitro experiment, GRSP sequestered large amounts of Cu and Cd and formed stable complexes, while further dialysis only released 25.74 ± 3.85% and 33.53 ± 3.62% of GRSP-bound Cu and Cd, respectively

Comparison of the WRF-FDDA-Based Radar Reflectivity and Lightning Data Assimilation for Short-Term Precipitation and Lightning Forecasts of Severe Convection

This work evaluates and compares the performance of the radar reflectivity and lightning data assimilation schemes implemented in weather research and forecasting-four-dimensional data assimilation (WRF-FDDA) for short-term precipitation and lightning forecasts. All six mesoscale convective systems (MCSs) with a duration greater than seven hours that occurred in the Guangdong Province of China during June 2020 were included in the experiments. The results show that both the radar reflectivity data assimilation and lightning data assimilation improved the analyses and short-term forecasts of the precipitation and lightning of the MCSs. On average, for precipitation forecasts, the experiments with radar reflectivity data assimilation performed better than those with lightning data assimilation; however, for lightning forecasts, the experiments with lightning data assimilation performed better in the analysis period and 1 h forecast, and for some cases, the superiority lasted to three forecast hours. This highlights the potential of lightning data assimilation in short-term lightning forecasting

Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon “3060” plan for the future of China