Wood trait preferences of Neotropical xylophagous beetles (Coleoptera: Cerambycidae).

Tree life history strategies are correlated with functional plant traits, such as wood density, moisture content, bark thickness, and nitrogen content; these traits affect the nutrients available to xylophagous insects. Cerambycid beetles feed on substrates that vary in these traits, but little is known about how they affect community composition. The goal of this project is to explore the community composition of two cerambycid subfamilies (Cerambycinae and Lamiinae) according to the wood traits in the wood they eat. In a salvage project conducted adjacent to the Panama Canal, trees were felled and exposed to Cerambycidae for oviposition. Disks from branches of differing thickness from the same plant individuals were used to calculate wood density, moisture content, and bark thickness in the field; nitrogen data were acquired offsite. Thick and thin branches tended to differ in wood trait values; therefore, data were analyzed separately in subsequent analyses. In thin branches, cerambycid abundance and species richness were higher in samples with less dense, moister wood, and thicker bark. Thick branches showed similar trends, but the wood traits accounted for little variability in beetle abundance or species richness. There were no significant regressions between beetle data and nitrogen. Cerambycines emerged more slowly, and from denser, drier wood, than lamiines. Cerambycines might be more drought-tolerant than lamiines, and therefore more resistant to the longer, more severe dry seasons that are predicted to occur due to climate change.

La historia de vida de los árboles se correlaciona con los rasgos funcionales de la planta, como la densidad de la madera, el contenido de humedad, el grosor de la corteza, y el contenido de nitrógeno; estos rasgos afectan los nutrientes disponibles para los insectos xilófagos. Los escarabajos cerambícidos se alimentan de sustratos que varían en estos rasgos, pero se sabe poco sobre cómo afectan la composición de la comunidad. El objetivo de este proyecto es explorarla composición comunitaria de dos subfamilias de cerambícidos (Cerambycinae y Lamiinae) según las características de la madera que consumen. En un proyecto de salvamento realizado junto al Canal de Panamá, se talaron árboles y se expusieron a Cerambycidae para la oviposición. Se usaron discos de ramas de diferente grosor de las mismas plantas para calcular la densidad de la madera, el contenido de humedad y el grosor de la corteza en el campo; los datos de nitrógeno fueron adquiridos fuera del sitio. Las ramas gruesas y delgadas tendieron a diferir en los valores de las características de la madera; por lo tanto, los datos se analizaron por separado en análisis posteriores. En ramas delgadas, la abundancia de cerambícidos y la riqueza de especies fueron mayores en muestras con madera menos densa, más húmeda y con corteza más gruesa. Las ramas gruesas mostraron tendencias similares, pero las características de la madera explicaron poca variabilidad en la abundancia de escarabajos o la riqueza de especies. No hubo regresión significativas entre los datos del escarabajo y el nitrógeno. Cerambycines surgieron más lentamente y de maderas más densas y secas que los lamiines. Cerambycines podrían ser más tolerantes a la sequía que lamiines y, por lo tanto, más resistentes a las estaciones secas más largas y severas que se prevé que ocurran debido al cambio climático.

Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data.

Despite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (Cab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March-April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35 µg/cm2. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R2 of 0.61-0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices.

Limiting factors for panicle photosynthesis at the anthesis and grain filling stages in rice (Oryza sativa L.).

Panicle photosynthesis is crucial for grain yield in cereal crops; however, the limiting factors for panicle photosynthesis are poorly understood, greatly impeding improvement in this trait. In the present study, pot experiments were conducted to investigate the limiting factors for panicle photosynthesis at the anthesis stage in seven rice genotypes and to examine the temporal variations in photosynthesis during the grain filling stage in the Liangyou 287 genotype. At the anthesis stage, leaf and panicle photosynthesis was positively correlated with stomatal conductance and maximum carboxylation rate, which were in turn associated with hydraulic conductance and nitrogen content, respectively. Panicle hydraulic conductance was positively correlated with the area of bundle sheaths in the panicle neck. During grain filling, leaf and panicle photosynthesis remained constant at the early stage but dramatically decreased from 8 to 9 days after anthesis. The trends of variations in panicle photosynthesis were consistent with those in stomatal conductance but not with those in maximum carboxylation rate. At first, the maximum carboxylation rate and respiration rate in the panicle increased, through elevated panicle nitrogen content, but then drastically decreased, as a result of dehydration. The present study systematically investigated the limiting factors for panicle photosynthesis, which are vital for improving photosynthesis and crop yield.

Seasonal dynamics of photosynthetic nitrogen content and partitioning in deciduous forests.

Nitrogen allocated to the photosynthetic apparatus and its partitioning into different photosynthetic components is crucial for understanding plant carbon gain and plant productivity. It is known that photosynthetic nitrogen content and partitioning are controlled by both environmental and vegetation factors and have versatile and dynamic responses. However, such responses are greatly simplified in most current gas exchange models, in which only a prescribed relationship is commonly applied to describe the effect of nitrogen on photosynthesis and with limited model performance. While within-canopy variation at a specific time in leaf photosynthetic nitrogen content and partitioning has been studied previously, far less attention has been paid to the seasonal dynamics of photosynthetic nitrogen content and partitioning, which is especially critical to deciduous forests. In this study, we integrated long-term field observations in deciduous forests in Japan to determine seasonal patterns of photosynthetic nitrogen content and partitioning (rubisco, electron transport, and light capture) and to examine how photosynthetic nitrogen content and partitioning varied seasonally in deciduous forest canopies growing at different altitudes. The results demonstrated that there were remarkable seasonal variations in both photosynthetic nitrogen content and partitioning in deciduous forests along the altitudinal gradient. Moreover, photosynthetic nitrogen use efficiency was well explained by nitrogen partitioning rather than total leaf nitrogen. These results suggest that seasonal patterns of nitrogen partitioning should be integrated into ecosystem models to accurately project emergent properties of ecosystem productivity on local, regional, and global scales.

Stomatal, mesophyll and biochemical limitations to photosynthesis and their relationship with leaf structure over an elevation gradient in two conifers.

Photosynthetic responses across complex elevational gradients provides insight into fundamental processes driving responses of plant growth and net primary production to environmental change. Gas exchange of needles and twig water potential were measured in two widespread coniferous tree species, Pinus contorta and Picea engelmannii, over an 800-m elevation gradient in southeastern Wyoming, USA. We hypothesized that limitations to photosynthesis imposed by mesophyll conductance (gm) would be greatest at the highest elevation sites due to higher leaf mass per area (LMA) and that estimations of maximum rate of carboxylation (Vcmax) without including gm would obscure elevational patterns of photosynthetic capacity. We found that gm decreased with elevation for P. contorta and remained constant for P. engelmannii, but in general, limitation to photosynthesis by gm was small. Indeed, estimations of Vcmax when including gm were equivalent to those estimated without including gm and no correlation was found between gm and LMA nor between gm and leaf N. Stomatal conductance (gs) and biochemical demand for CO2 were by far the most limiting processes to photosynthesis at all sites along the elevation gradient. Photosynthetic capacity (A) and gs were influenced strongly by differences in soil water availability across the elevation transect, while gm was less responsive to water availability. Based on our analysis, variation in gm plays only a minor role in driving patterns of photosynthesis in P. contorta and P. engelmannii across complex elevational gradients in dry, continental environments of the Rocky Mountains and accurate modeling of photosynthesis, growth and net primary production in these forests may not require detailed estimation of this trait value.

Variation in leaf photosynthetic capacity within plant canopies: optimization, structural, and physiological constraints and inefficiencies.

Leaf photosynthetic capacity (light-saturated net assimilation rate, AA) increases from bottom to top of plant canopies as the most prominent acclimation response to the conspicuous within-canopy gradients in light availability. Light-dependent variation in AA through plant canopies is associated with changes in key leaf structural (leaf dry mass per unit leaf area), chemical (nitrogen (N) content per area and dry mass, N partitioning between components of photosynthetic machinery), and physiological (stomatal and mesophyll conductance) traits, whereas the contribution of different traits to within-canopy AA gradients varies across sites, species, and plant functional types. Optimality models maximizing canopy carbon gain for a given total canopy N content predict that AA should be proportionally related to canopy light availability. However, comparison of model expectations with experimental data of within-canopy photosynthetic trait variations in representative plant functional types indicates that such proportionality is not observed in real canopies, and AA vs. canopy light relationships are curvilinear. The factors responsible for deviations from full optimality include stronger stomatal and mesophyll diffusion limitations at higher light, reflecting greater water limitations and more robust foliage in higher light. In addition, limits on efficient packing of photosynthetic machinery within leaf structural scaffolding, high costs of N redistribution among leaves, and limited plasticity of N partitioning among components of photosynthesis machinery constrain AA plasticity. Overall, this review highlights that the variation of AA through plant canopies reflects a complex interplay between adjustments of leaf structure and function to multiple environmental drivers, and that AA plasticity is limited by inherent constraints on and trade-offs between structural, chemical, and physiological traits. I conclude that models trying to simulate photosynthesis gradients in plant canopies should consider co-variations among environmental drivers, and the limitation of functional trait variation by physical constraints and include the key trade-offs between structural, chemical, and physiological leaf characteristics.

Utilising the Nanozymatic Activity of Copper-Functionalised Mesoporous C3 N5 for Sensing Biomolecules.

Designing unique nanomaterials for the selective sensing of biomolecules is of significant interest in the field of nanobiotechnology. In this work, we demonstrated the synthesis of ordered Cu nanoparticle-functionalised mesoporous C3 N5 that has unique peroxidase-like nanozymatic activity for the ultrasensitive and selective detection of glucose and glutathione. A nano hard-templating technique together with the in-situ polymerisation and self-assembly of Cu and high N-containing CN precursor was adopted to introduce mesoporosity as well as high N and Cu content in mesoporous C3 N5 . Due to the ordered structure and highly dispersed Cu in the mesoporous C3 N5 , a large enhancement of the peroxidase mimetic activity in the oxidation of a redox dye in the presence of hydrogen peroxide could be obtained. Additionally, the optimised Cu-functionalised mesoporous C3 N5 exhibited excellent sensitivity to glutathione with a low detection limit of 2.0 ppm. The strong peroxidase activity of the Cu-functionalised mesoporous C3 N5 was also effectively used for the sensing of glucose with a detection limit of 0.4 mM through glucose oxidation with glucose oxidase. This unique Cu-functionalised mesoporous C3 N5 has the potential for detecting various molecules in the environment as well as for next-generation glucose and glutathione diagnostic devices.

Physio-biochemical, agronomical, and gene expression analysis reveals different responsive approach to low nitrogen in contrasting rice cultivars for nitrogen use efficiency.

BACKGROUND: Nitrogen (N) is an essential macronutrient for plant growth and development as it is an essential constituent of biomolecules. Its availability directly impacts crop yield. Increased N application in crop fields has caused environmental and health problems, and decreasing nitrogen inputs are in demand to maintain crop production sustainability. Understanding the molecular mechanism of N utilization could play a crucial role in improving the nitrogen use efficiency (NUE) of crop plants. METHODS AND RESULTS: In the present study, the effect of low N supply on plant growth, physio-biochemical, chlorophyll fluorescence attributes, yield components, and gene expression analysis were measured at six developmental stages in rice cultivars. Two rice cultivars were grown with a supply of optimium (120 kg ha-1) and low N (60 kg ha-1). Cultivar Vikramarya excelled Aditya at low N supply, and exhibits enhanced plant growth, physiological efficiency, agronomic efficiency, and improved NUE due to higher N uptake and utilization at low N treatment. Moreover, plant biomass, leaf area, and photosynthetic rate were significantly higher in cv. Vikramarya than cv. Aditya at different growth stages, under low N treatment. In addition, enzymatic activities in cultivar Vikramarya were higher than cultivar Aditya under low nitrogen, indicating its greater potential for N metabolism. Gene expression analysis was carried out for the most important nitrogen assimilatory enzymes, such as nitrate reductase (NR), nitrite reductase (NiR), glutamine synthetase (GS), and glutamate synthase (GOGAT). Expression levels of these genes at different growth stages were significantly higher in cv. Vikramarya compared to cv. Aditya at low N supply. Our findings suggest that improving NUE needs specific revision in N metabolism and physiological assimilation. CONCLUSION: Overall differences in plant growth, physiological efficiency, biochemical activities, and expression levels of N metabolism genes in N-efficient and N-inefficient rice cultivars need a specific adaptation to N metabolism. Regulatory genes may separately or in conjunction, enhance the NUE. These results provide a platform for selecting crop cultivars for nitrogen utilization efficiency at low N treatment.

Cropland abandonment alleviates soil carbon emissions in the North China Plain.

Land use change could profoundly influence the terrestrial ecosystem carbon (C) cycle. However, the effects of agricultural expansion and cropland abandonment on soil microbial respiration remain controversial, and the underlying mechanisms of the land use change effect are lacking. In this study, we conducted a comprehensive survey in four land use types (grassland, cropland, orchard, and old-field grassland) of North China Plain with eight replicates to explore the responses of soil microbial respiration to agricultural expansion and cropland abandonment. We collected surface soil (0-10 cm in depth) in each land use type to measure soil physicochemical property and microbial analysis. Our results showed that soil microbial respiration was significantly increased by 15.10 mg CO2 kg-1 day-1 and 20.06 mg CO2 kg-1 day-1 due to the conversion of grassland to cropland and orchard, respectively. It confirmed that agricultural expansion might exacerbate soil C emissions. On the contrary, the returning of cropland and orchard to old-field grassland significantly decreased soil microbial respiration by 16.51 mg CO2 kg-1 day-1 and 21.47 mg CO2 kg-1 day-1, respectively. Effects of land use change on soil microbial respiration were predominately determined by soil organic and inorganic nitrogen contents, implying that nitrogen fertilizer plays an essential role in soil C loss. These findings highlight that cropland abandonment can effectively mitigate soil CO2 emissions, which should be implemented in agricultural lands with low grain production and high C emissions. Our results improve mechanistic understanding on the response of soil C emission to land use changes.

Maize/peanut intercropping improves nutrient uptake of side-row maize and system microbial community diversity.

BACKGROUND: Intercropping, a diversified planting pattern, increases land use efficiency and farmland ecological diversity. We explored the changes in soil physicochemical properties, nutrient uptake and utilization, and microbial community composition in wide-strip intercropping of maize and peanut. RESULTS: The results from three treatments, sole maize, sole peanut and intercropping of maize and peanut, showed that intercropped maize had a marginal advantage and that the nutrient content of roots, stems and grains in side-row maize was better than that in the middle row of intercropped maize and sole maize. The yield of intercropped maize was higher than that of sole cropping. The interaction between crops significantly increased soil peroxidase activity, and significantly decreased protease and dehydrogenase activities in intercropped maize and intercropped peanut. The diversity and richness of bacteria and fungi decreased in intercropped maize rhizosphere soil, whereas the richness of fungi increased intercropped peanut. RB41, Candidatus-udaeobacter, Stropharia, Fusarium and Penicillium were positively correlated with soil peroxidase activity, and negatively correlated with soil protease and dehydrogenase activities. In addition, intercropping enriched the functional diversity of the bacterial community and reduced pathogenic fungi. CONCLUSION: Intercropping changed the composition and diversity of the bacterial and fungal communities in rhizosphere soil, enriched beneficial microbes, increased the nitrogen content of intercropped maize and provided a scientific basis for promoting intercropping in northeastern China.

Can evolutionary history predict plant plastic responses to climate change?

Plant plastic responses are critical to the adaptation and survival of species under climate change, but whether they are constrained by evolutionary history (phylogeny) is largely unclear. Plant leaf traits are key in determining plants' performance in different environments, and if these traits and their variation are phylogenetically dependent, predictions could be made to identify species vulnerable to climate change. We compiled data on three leaf traits (photosynthetic rate, specific leaf area, and leaf nitrogen content) and their variation under four environmental change scenarios (warming, drought, elevated CO2 , or nitrogen addition) for 434 species, from 210 manipulation experiments. We found phylogenetic signal in the three traits but not in their variation under the four scenarios. This indicates that closely related species show similar traits but that their plastic responses could not be predicted from species relatedness under environmental change. Meanwhile, phylogeny weakened the slopes but did not change the directions of conventional pairwise trait relationships, suggesting that co-evolved leaf trait pairs have consistent responses under contrasting environmental conditions. Phylogeny can identify lineages rich in species showing similar traits and predict their relationships under climate change, but the degree of plant phenotypic variation does not vary consistently across evolutionary clades.

Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance.

Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25 ) and the maximum electron transport at a reference temperature (Jmax25 ), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of Vcmax25 and Jmax25 ; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field-grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25 , based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57-0.65 and 0.48-0.58 for Vcmax25 and Jmax25 , respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity.

Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery.

The recently launched and upcoming hyperspectral satellite missions, featuring contiguous visible-to-shortwave infrared spectral information, are opening unprecedented opportunities for the retrieval of a broad set of vegetation traits with enhanced accuracy through novel retrieval schemes. In this framework, we exploited hyperspectral data cubes collected by the new-generation PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency to develop and test a hybrid retrieval workflow for crop trait mapping. Crop traits were mapped over an agricultural area in north-east Italy (Jolanda di Savoia, FE) using PRISMA images collected during the 2020 and 2021 vegetative seasons. Leaf chlorophyll content, leaf nitrogen content, leaf water content and the corresponding canopy level traits scaled through leaf area index were estimated using a hybrid retrieval scheme based on PROSAIL-PRO radiative transfer simulations coupled with a Gaussian processes regression algorithm. Active learning algorithms were used to optimise the initial set of simulated data by extracting only the most informative samples. The accuracy of the proposed retrieval scheme was evaluated against a broad ground dataset collected in 2020 in correspondence of three PRISMA overpasses. The results obtained were positive for all the investigated variables. At the leaf level, the highest accuracy was obtained for leaf nitrogen content (LNC: r2=0.87, nRMSE=7.5%), while slightly worse results were achieved for leaf chlorophyll content (LCC: r2=0.67, nRMSE=11.7%) and leaf water content (LWC: r2=0.63, nRMSE=17.1%). At the canopy level, a significantly higher accuracy was observed for nitrogen content (CNC: r2=0.92, nRMSE=5.5%) and chlorophyll content (CCC: r2=0.82, nRMSE=10.2%), whereas comparable results were obtained for water content (CWC: r2=0.61, nRMSE=16%). The developed models were additionally tested against an independent dataset collected in 2021 to evaluate their robustness and exportability. The results obtained (i. e., LCC: r2=0.62, nRMSE=27.9%; LNC: r2=0.35, nRMSE=28.4%; LWC: r2=0.74, nRMSE=20.4%; LAI: r2=0.84, nRMSE=14.5%; CCC: r2=0.79, nRMSE=18.5%; CNC: r2=0.62, nRMSE=23.7%; CWC: r2=0.92, nRMSE=16.6%) evidence the transferability of the hybrid approach optimised through active learning for most of the investigated traits. The developed models were then used to map the spatial and temporal variability of the crop traits from the PRISMA images. The high accuracy and consistency of the results demonstrates the potential of spaceborne imaging spectroscopy for crop monitoring, paving the path towards routine retrievals of multiple crop traits over large areas that could drive more effective and sustainable agricultural practices worldwide.

Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors.

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy.

Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R2C is 0.921, the RMSEC is 0.115, the test set R2P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.

The Influence of Hop Phenolic Compounds on Dry Hopping Beer Quality.

BACKGROUND: The article considers the phenolic hop compounds' effect on the quality indicators of finished beer. The topic under consideration is relevant since it touches on the beer matrix colloidal stability when compounds with potential destabilizing activity are introduced into it from the outside. METHODS: The industrial beer samples' quality was assessed by industry-accepted methods and using instrumental analysis methods (high-performance liquid chromatography methods-HPLC). The obtained statistical data were processed by the Statistics program (Microsoft Corporation, Redmond, WA, USA, 2006). RESULTS: The study made it possible to make assumptions about the functional dependence of the iso-α-bitter resins and isoxanthohumol content in beer samples. Mathematical analysis indicate interactions between protein molecules and different malted grain and hop compounds are involved in beer structure, in contrast to dry hopped beer, where iso-a-bitter resins, protein, and coloring compounds were significant, with a lower coefficient of determination. The main role of rutin in the descriptor hop bitterness has been established in kettle beer hopping technology, and catechin in dry beer hopping technology, respectively. The important role of soluble nitrogen and ß-glucan dextrins in the perception of sensory descriptors of various technologies' beers, as well as phenolic compounds in relation to the formation of bitterness and astringency of beer of classical technology and cold hopping, has been shown. CONCLUSIONS: The obtained mathematical relationships allow predicting the resulting beer quality and also make it possible to create the desired flavor profiles.

Prediction of photosynthetic light-response curves using traits of the leaf economics spectrum for 75 woody species: effects of leaf habit and sun-shade dichotomy.

PREMISE: Photosynthetic light-response (PLR) curves for leaves are important components of models related to carbon fixation in forest ecosystems, linking the Mitscherlich equation and Michaelis-Menten equation to traits of the leaf economics spectrum (LES). However, models do not consider changes in leaf habits (i.e., evergreen and deciduous) and within-canopy shading variation in these PLR curves. METHODS: Here, we measured the PLR curves in sun and shade leaves of 44 evergreen and 31 deciduous species to examine the relationships between variables of the Mitscherlich equation and Michaelis-Menten equation, leaf nitrogen (N) and phosphorus (P) content, and leaf mass per area (LMA). RESULTS: Small changes were caused by different leaf habits and shade variations in relationships linking variables of the two equations to leaf N and P content and LMA. Values of the scaling exponents for PLR curve parameters did not differ regardless of canopy position and leaf habit (P > 0.05). The PLR curves in species with different leaf habits (i.e., evergreen and deciduous) at different canopy positions could be predicted using the general allometric relations between leaf traits and PLR parameters in the two equations. For photosynthetic photon flux densities from 0 to 2000 µmol m-2 s-1 , approximately 71% (Mitscherlich equation) and 70% (Michaelis-Menten equation) of the net assimilation rates could be predicted. CONCLUSIONS: These findings indicate that leaf net assimilation rates can be predicted through the large available data for LES traits. Incorporation of values for these traits available in the LES databases into ecosystem models of forest productivity and carbon fixation warrants further investigation.

Mapping landscape canopy nitrogen content from space using PRISMA data.

Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and upcoming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is expected to globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. Thanks to the provision of contiguous visible-to-shortwave infrared spectral data, hyperspectral missions open enhanced opportunities for the development of new-generation retrieval models of multiple vegetation traits. Among these, canopy nitrogen content (CNC) is one of the most promising variables given its importance for agricultural monitoring applications. This work presents the first hybrid CNC retrieval model for the operational delivery from spaceborne imaging spectroscopy data. To achieve this, physically-based models were combined with machine learning regression algorithms and active learning (AL). The key concepts involve: (1) coupling the radiative transfer models PROSPECT-PRO and SAIL for the generation of a wide range of vegetation states as training data, (2) using dimensionality reduction to deal with collinearity, (3) applying an AL technique in combination with Gaussian process regression (GPR) for fine-tuning the training dataset on in field collected data, and (4) adding non-vegetated spectra to enable the model to deal with spectral heterogeneity in the image. The final CNC model was successfully validated against field data achieving a low root mean square error (RMSE) of 3.4 g/m2 and coefficient of determination (R 2) of 0.7. The model was applied to a PRISMA image acquired over agricultural areas in the North of Munich, Germany. Mapping aboveground CNC yielded reliable estimates over the whole landscape and meaningful associated uncertainties. These promising results demonstrate the feasibility of routinely quantifying CNC from space, such as in an operational context as part of the future CHIME mission.

Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery.

Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.

4,5-Dicyano-1,2,3-Triazole-A Promising Precursor for a New Family of Energetic Compounds and Its Nitrogen-Rich Derivatives: Synthesis and Crystal Structures.

The nitrogen-rich compounds and intermediates with structure of monocyclic, bicyclic, and fused rings based on 1,2,3-triazole were synthesized and prepared by using a promising precursor named 4,5-dicyano-1,2,3-triazole, which was obtained by the cyclization reaction of diaminomaleonitrile. Their structure and configurational integrity were assessed by Fourier transform-infrared spectroscopy (FT-IR), mass spectrometry (MS), and elemental analysis (EA). Additionally, fourteen compounds were further confirmed by X-ray single crystal diffraction. Meanwhile, the physical properties of four selected compounds (3·H2O, 6·H2O, 10·H2O, and 16) including thermal stability, detonation parameters, and sensitivity were also estimated. All these compounds could be considered to construct more abundant 1,2,3-triazole-based neutral energetic molecules, salts, and complex compounds, which need to continue study in the future in the field of energetic materials.