Search Results (116)

The comprehension of seasonal patterns of evapotranspiration (ET), as well as the interactive response to environmental factors, holds paramount importance for illuminating the intricate interaction within the carbon–water cycle of desert riparian forest ecosystems. Nonetheless, the driving mechanism behind ET changes is complex, and different components show significant differences in response to the same factor. Moreover, water resources are scarce in the region, and sustainable water resources management in arid regions usually aims to maximize transpiration (T) and minimize evaporation (E); therefore, reasonable calculation of ET components is urgent to effectively assess water resources consumption and improve water use efficiency. This discussion assessed the suitability and reliability of different methods for partitioning ET within the desert oasis in Northwestern China, calculated water use efficiency (WUE), and explored the differences in the response patterns of ET, transpiration (T), and WUE to environmental elements of constructive Populus euphratica forests in this region during the growing season. Continuous measurements of meteorological, soil, and vegetation factors were collected from 2014 to 2021 to facilitate this investigation. This study demonstrated that the underlying water use efficiency (uWUE) method effectively partitions ET into vegetation T and soil evaporation (E). Seasonal variations in ET and T were predominantly driven by temperature (Ta), radiation (Rn), soil moisture, and leaf area index (LAI). In addition, the exchange of water and carbon across different scales was governed by distinct regulatory mechanisms, where canopy-level WUE (WUEc) primarily depended on climatic conditions, while ecosystem-level WUE (WUEe) was more strongly influenced by vegetation structural characteristics. This study provided valuable insights into the ET characteristics, influencing factors, and water–carbon consumption mechanisms of desert vegetation in arid regions, and the conclusions of the discussion may provide theoretical insights for policymakers and ecosystem managers interested in preserving the ecological balance of arid regions. Full article

Accurately partitioning evapotranspiration (ET) of cropland into productive plant transpiration (T) and non-productive soil evaporation (E) is important for improving crop water use efficiency. Many methods, including machine learning methods, have been developed for ET partitioning. However, the applicability of machine learning models in cropland ET partitioning with diverse crop rotations is not clear. In this study, machine learning models are used to predict E, and T is obtained by calculating the difference between ET and E, leading to the derivation of the ratio of transpiration to evapotranspiration (T/ET). We evaluated six machine learning models (i.e., artificial neural networks (ANN), extremely randomized trees (ExtraTrees), gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost)) on partitioning ET at 16 cropland flux sites during the period from 2000 to 2020. The evaluation results showed that the XGBoost model had the best performance (R = 0.88, RMSE = 6.87 W/m², NSE = 0.77, and MAE = 3.41 W/m²) when considering the meteorological data, ecosystem sensible heat flux, ecosystem respiration, soil water content, and remote sensing vegetation indices as input variables. Due to the unavailability of observed E or T data at the 16 cropland sites, we used three other widely used ET partitioning methods to indirectly validate the accuracy of our ET partitioning results based on XGBoost. The results showed that our T estimation results were highly consistent with their T estimation results (R = 0.83–0.91). Moreover, based on the XGBoost model and the three other ET partitioning methods, we estimated the ratio of transpiration to evapotranspiration (T/ET) for different crops. On average, maize had the highest T/ET of 0.619 ± 0.119, followed by soybean (0.618 ± 0.085), winter wheat (0.614 ± 0.08), and sugar beet (0.611 ± 0.065). Lower T/ET was found for paddy rice (0.505 ± 0.055), winter barley (0.590 ± 0.058), potato (0.540 ± 0.088), and rapeseed (0.522 ± 0.107). These results suggest the machine learning models are easy and applicable for cropland T/ET estimation with different crop rotations and reveal obvious differences in water use among different crops, which is crucial for the sustainability of water resources and improvements in cropland water use efficiency. Full article

The estimation of water requirements constitutes a critical prerequisite for delineating water scarcity hotspots and mitigating intersectoral competition, particularly in endorheic basins in arid or semi-arid regions where hydrological closure exacerbates resource allocation conflicts. Under conditions of water scarcity, water supplied locally by precipitation and shallow groundwater bodies should be taken into account to estimate the net water requirements to be met with water conveyed from off-site sources. This concept is embodied in the distinction of blue ET (BET) and green ET (GET). In this study, the Budyko hypothesis (BH) method was optimized to partition the total ET into GET and BET during 2001–2018 in the Heihe River Basin. In this region, a better knowledge of net water requirements is even more important due to water allocation policies which reduced water supply to irrigated lands in the last 15 years. This study proposes a modified BH method based on a new vegetation-specific parameter (${\omega }_v$) which was optimized for different vegetation types using precipitation and actual ET data obtained from remote sensing observations. The results show that the BH method partitioned GET and BET reasonably well, with a percent bias of 23.8% and 37.4% and a root mean square error of 84.8 mm/a and 113.6 mm/a, respectively, when compared with reported data, which are superior to that of the precipitation deficit and soil water balance methods. A sensitivity experiment showed that the BH method exhibits a low sensitivity to uncertainties of input data. The results documented differences in the contribution of GET and BET to total ET across different land cover types in the Heihe River Basin. As expected, rainfed forest and grassland ecosystems are predominantly governed by GET, with 81.3% and 87.2% of total ET, respectively. In contrast, croplands and shrublands are primarily regulated by BET, with contributions of 61.5% and 84.3% to total ET. The improved BH method developed in this study paves the way for further analyses of the net water requirements in arid and semi-arid regions. Full article

Gynura divaricata (L.) DC is a long-used medicinal and edible plant in China folk. Its hyperglycemic effects have garnered increasing public attention in recent years. This study revealed that the ethyl acetate (EtOAc) and butanol (BuOH) partition fractions of G. divaricata crude extract exhibited significantly higher α-glucosidase inhibition activity and enhanced glucose uptake ability compared to other fractions. Guided by the hypoglycemic bioassay, these two fractions were subjected to isolation of active compounds using high-speed countercurrent chromatography (HSCCC). A two-phase solvent system composed of hexane-methyl tert-butyl ether (MtBE)-methanol-0.1% TFA water was employed for the separation of the EtOAc fraction by conventional HSCCC through a gradient elution strategy. Five major compounds were obtained and identified as chlorogenic acid (1), 3,4-dicaffeoylquinic acid (2), 3,5-dicaffeoylquinic acid (3), 4,5-dicaffeoylquinic acid (4), and kaempferol-3-O-β-D-glucopyranoside (5) by ESI-MS, ¹HNMR, and ¹³CNMR. The chlorogenic acid and the three dicaffeoylquinic acids were found to display higher inhibitory activities against α-glucosidase compared to the flavonoid. Considering their acidic nature, pH-zone-refining CCC (PHZCCC) was then applied for further scale-up separation using a solvent system MtBE: n-butanol: acetonitrile: water with trifluoroacetic acid (TFA) as a retainer and ammonium hydroxide (NH₄OH) as an eluter. A significantly higher yield of chlorogenic acid was obtained from the BuOH fraction by PZRCCC. Molecular docking between the caffeoylquinic acids and α-glucosidase confirmed their hypoglycemic activities. This study demonstrates that CCC is a powerful tool for preparative separation of active constituents in natural products. This research presents a novel and effective method for the preparative isolation of hypoglycemic compounds from Gynura divaricata. Full article

Mosasaurid teeth are abundant in the fossil record and often diagnostic to low taxonomic levels, allowing to document the taxonomic diversity and ecological disparity through time and with fewer biases than in other marine reptiles. The upper Maastrichtian Phosphates of Morocco, with at least fifteen coeval species representing a wide range of sizes and morphologies, undoubtedly represent the richest outcrop in the world for this clade of iconic Mesozoic squamates and one of the richest known marine tetrapod assemblages. Until now, the methods used to link tooth morphology to diets in marine amniotes were mainly qualitative in nature. Here, using the dental morphology of mosasaurids from Morocco, we combine two complementary approaches—a thorough comparative anatomical description and 2D/3D geometric morphometry—to quantitatively categorize the main functions of these teeth during feeding processes and infer diet preferences and niche-partitioning of these apex predators. Our results from combining these two approaches show the following: (1) Mosasaurids from the upper Maastrichtian Phosphates of Morocco occupy the majority of dental guilds ever colonized by Mesozoic marine reptiles. (2) As seen elsewhere in the Maastrichtian, mosasaurines dominate the regional mosasaurid assemblage, exhibiting the greatest taxonomic diversity (two-thirds of the species) and the largest range of morphologies, body sizes (2 m to more than 10 m) and ecological disparities (participating in nearly all predatory ecological guilds); strikingly, mosasaurines did not developed flesh piercers and, conversely, are the only ones to include durophagous species. (3) Halisaurines, though known by species of very different sizes (small versus large) and cranial morphologies (gracile versus robust), maintain a single tooth shape (piercer). (4) Plioplatecarpines were medium-size cutters and piercers, known by very morphologically diverging species. (5) Tylosaurines currently remain scarce, represented by a very large generalist species; they were largely replaced by mosasaurines as apex predators over the course of the Maastrichtian, as observed elsewhere. Also, when comparing tooth shapes with body sizes, the largest taxa (>8 m long) occupied a restricted area of tooth shapes (generalist, durophagous), whereas small and medium-sized species (<8 m long) range across all of them (generalists, durophagous, cutters, piercers). In other words, and probably related to the specificities and advantages of biomechanical resistance, apex predators are never dedicated piercers, micro-predators are conversely never generalists, and meso-predators show the widest range of dental adaptations. These diversities and disparities strongly suggest that Tethyan mosasaurids evolved strong niche-partitioning in the shallow marine environment of the upper Maastrichtian Phosphates of Morocco. Such a high diversity sensu lato just prior to the K/Pg biological crisis suggests that their extinction was rather sudden, though the exact causes of their extinction remain unknown. Finally, Gavialimimus Strong et al., 2020 is systematically reassigned to Gavialimimus ptychodon (Arambourg, 1952), and an emended diagnosis (for teeth and dentition) is proposed for this species. Full article

Quantifying the water budget in the Great Salt Lake (GSL) basin is a nontrivial task, especially under a changing climate that contributes to increasing temperatures and a shift towards more rainfall and less snowfall. This study examines the potential impacts of temperature thresholds on the water budget in the GSL, emphasizing the influence on snowmelt, evapotranspiration (ET), and runoff under varying climate warming scenarios. Current hydrological models such as the Variable Infiltration Capacity (VIC) model use a universal temperature threshold to partition snowfall and rainfall across different regions. Previous studies have argued that there is a wide range of thresholds for partitioning rainfall and snowfall across the globe. However, there is a clear knowledge gap in quantifying water budget components in the Great Salt Lake (GSL) basin corresponding to varying temperature thresholds for separating rainfall and snowfall under the present and future climates. To address this gap, the study applied temperature thresholds derived from observation-based data available from National Center for Environmental Prediction (NCEP) to the VIC model. We also performed a suite of hydrological experiments to quantify the water budget of the Great Salt Lake basin by perturbing temperature thresholds and climate forcing. The results indicate that higher temperature thresholds contribute to earlier snowmelt, reduced snowpack, and lower peak runoff values in the early spring that are likely due to increased ET before peak runoff periods. The results show that the GSL undergoes higher snow water equivalent (SWE) values during cold seasons due to snow accumulation and lower values during warm seasons as increased temperatures intensify ET. Projected climate warming may result in further reductions in SWE (~71%), increased atmospheric water demand, and significant impacts on water availability (i.e., runoff reduced by ~20%) in the GSL basin. These findings underscore the potential challenges that rising temperatures pose to regional water availability. Full article

Accurately forecasting sales for fast-moving consumer goods (FMCG) remains a significant challenge due to the volatile and multi-faceted nature of sales data. Existing methods often struggle to capture intricate patterns driven by seasonal trends, external factors, and consumer behavior, hindering effective inventory management and strategic decision-making. To overcome these challenges, we propose STL-DCSInformer-ETS, a hybrid model that integrates three complementary components: STL decomposition, an enhanced DCSInformer model, and the ETS model. The model uses monthly sales data from a FMCG company, with key features including sales volume, product prices, promotional activities, and regulatory factors such as holidays, geographical information, consumer behavior, product factors, etc. STL decomposition partitions time-series data into trend, seasonal, and residual components, reducing data complexity and enabling more targeted forecasting. The enhanced DCSInformer employs dilated causal convolution and a multi-scale feature extraction mechanism to capture long-term dependencies and short-term variations effectively. Meanwhile, the ETS model specializes in modeling seasonal patterns, further refining forecasting precision. To further improve predictive performance, the Random Forest-based Recursive Feature Elimination (RF-RFE) method is applied to optimize feature selection. RF-RFE identifies key predictive factors from multiple dimensions, such as time, geography, and economy, which significantly influence forecasting accuracy. Through numerical experiments, the method demonstrates excellent performance by achieving a 35.9% reduction in Mean Squared Error and a 21.4% decrease in Mean Absolute Percentage Error, significantly outperforming traditional methods. Furthermore, the model effectively captures both medium- and long-term sales trends while addressing short-term fluctuations, leading to more accurate forecasting and improved decision-making for fast-moving consumer goods. This research provides new theoretical insights into hybrid forecasting models and practical solutions for optimizing inventory management and strategic planning in the FMCG industry. Full article

Cisplatin is still a first-line agent in cancer treatment due to its effectiveness. Despite the large body of research concerning this drug, the role of explicit water molecules in its mechanism remains uncertain. We addressed the addition of cisplatin with the nitrogenous DNA bases adenine and guanine, with an emphasis on the impact of explicit microsolvation on every step of the action pathway of this pharmaceutical. We used electronic structure calculations to explore the energetics of the key reactions of this mechanism. We also exploited state-of-the-art methods of wave function analyses, namely, the Quantum Theory of Atoms in Molecules and the Interacting Quantum Atoms partition, to explore the chemical bonding throughout such chemical reactions. Our results reveal that microsolvation significantly differently affects electronic and Gibbs free activation energies, as previously reported (F.P. Cossio et al. ChemPhysChem, 17, 3932, 2016). The changes in activation energies are consistent with Hammond’s postulate in terms of the changes in the chemical bonding scenario between reactants and transition states. Overall, we provide an in-depth description of the importance of the surrounding water molecules of cisplatin, which aids in understanding the mechanism of pharmaceuticals in the pursuit of more effective cancer treatments. Full article

Traditional Japanese medicines, i.e., Kampo medicines, consist of crude drugs (mostly plants) that have empirical pharmacological functions (‘Yakuno’ in Japanese), such as clearing heat. Crude drugs with cold properties, such as Phellodendron bark, have the empirical function of clearing heat as they cool the body. Because we found that anti-inflammatory compounds were present in several crude drugs for clearing heat, it is speculated that the empirical function of clearing heat may be linked to anti-inflammatory activities. When 10 typical crude drugs were selected from 22 herbal crude drugs for clearing heat, we identified anti-inflammatory compounds in five crude drugs, including Phellodendron bark. In this study, the other crude drugs were extracted and partitioned with ethyl acetate (EtOAc) and n-butanol to obtain three crude fractions. All the EtOAc-soluble fractions, except that from Forsythia fruits, inhibited interleukin (IL)-1β-induced nitric oxide (NO) production in primary-cultured rat hepatocytes. Anti-inflammatory compounds were identified from these EtOAc-soluble fractions: baicalein from Scutellaria roots, (−)-nyasol from Anemarrhena rhizomes, and loniflavone from Lonicera leaves and stems. (+)-Phillygenin was purified from Forsythia fruits by removing cytotoxic oleanolic and betulinic acids. These compounds suppressed the production of NO and cytokines in hepatocytes. Anti-inflammatory compounds were not purified from the EtOAc-soluble fraction of Rehmannia roots because of their low abundance. Collectively, these findings indicate that anti-inflammatory compounds are present in all 10 crude drugs for clearing heat, confirming that these anti-inflammatory compounds in crude drugs provide the empirical functions for clearing heat. Other empirical functions of Kampo medicine can also be explained by modern pharmacological activities. Full article

Rice straw is used as livestock feed and compost. Ferimzone and tricyclazole, common fungicides for rice blast control, can be found in high concentrations in rice straw after unmanned aerial vehicle (UAV) spraying, potentially affecting livestock and human health through pesticide residues. In this study, an optimized method for the analysis of the two fungicides in rice straw was developed using the improved QuEChERS method. After the optimization of water and solvent volume, extraction conditions including ethyl acetate (EtOAc), acetonitrile (MeCN), a mixed solvent, and MeCN containing 1% acetic acid were compared. Different salts, including unbuffered sodium chloride, citrate, and acetate buffer salts, were compared for partitioning. Among the preparation methods, the MeCN/EtOAc mixture with unbuffered salts showed the highest recovery rates (88.1–97.9%, RSD ≤ 5.1%). To address the severe matrix effect (%ME) of rice straw, which is characterized by low moisture content and cellulose-based complex matrices, samples were purified using 25 mg each of primary–secondary amine (PSA) and octadecylsilane (C₁₈), without pesticide loss. The developed method was validated with a limit of quantification (LOQ) of 0.005 mg/kg for target pesticides, and recovery rates at levels of 0.01, 0.1, and 2 mg/kg met the permissible range (82.3–98.9%, RSD ≤ 8.3%). The %ME ranged from −17.6% to −0.3%, indicating a negligible effect. This optimized method was subsequently applied to residue studies following multi-rotor spraying. Fungicides from all fields and treatment groups during harvest season did not exceed the maximum residue limits (MRLs) for livestock feed. This confirms that UAV spraying can be safely managed without causing excessive residues. Full article

Accurate evapotranspiration (ET) estimation is crucial for understanding ecosystem dynamics and managing water resources. Existing methodologies, including traditional techniques like the Penman–Monteith model, remote sensing approaches utilizing Solar-Induced Fluorescence (SIF), and machine learning algorithms, have demonstrated varying levels of effectiveness in ET estimation. However, these methods often face significant challenges, such as reliance on empirical coefficients, inadequate representation of canopy dynamics, and limitations due to cloud cover and sensor constraints. These issues can lead to inaccuracies in capturing ET’s spatial and temporal variability, highlighting the need for improved estimation techniques. This study introduces a novel approach to enhance ET estimation by integrating SIF partitioning with Photosynthetically Active Radiation (PAR) and leaf area index (LAI) data, utilizing the TL-LUE model (Two-Leaf Light Use Efficiency). Partitioning SIF data into sunlit and shaded components allows for a more detailed representation of the canopy’s functional dynamics, significantly improving ET modelling. Our analysis reveals significant advancements in ET modelling through SIF partitioning. At Xiaotangshan Station, the correlation between modelled ET and SIFsu is 0.71, while the correlation between modelled ET and SIFsh is 0.65. The overall correlation (R²) between the modelled ET and the combined SIF partitioning (SIF(P)) is 0.69, indicating a strong positive relationship at Xiaotangshan Station. The correlations between SIFsh and SIFsu with modelled ET show notable patterns, with R² values of 0.89 and 0.88 at Heihe Daman, respectively. These findings highlight the effectiveness of SIF partitioning in capturing canopy dynamics and its impact on ET estimation. Comparing modelled ET with observed ET and the Penman–Monteith model (PM model) demonstrates substantial improvements. R² values for modelled ET against observed ET were 0.68, 0.76, and 0.88 across HuaiLai, Shangqiu, and Yunxiao Stations. Modelled ET correlations to the PM model were 0.75, 0.73, and 0.90, respectively, at three stations. These results underscore the model’s capability to enhance ET estimations by integrating physiological and remote sensing data. This innovative SIF-partitioning approach offers a more nuanced perspective on canopy photosynthesis, providing a more accurate and comprehensive method for understanding and managing ecosystem water dynamics across diverse environments. Full article

The Ogallala aquifer, underlying eight states from South Dakota to Texas, is practically non-recharging south of Nebraska, and groundwater withdrawals for irrigation have lowered the aquifer in western Kansas. Subsequent well-yield declines encourage deficit irrigation, greater reliance on precipitation, and producing profitable drought-tolerant crops like upland cotton (Gossypium hirsutum (L.)). Our objective was to evaluate deficit irrigated cotton growth, yield, and water productivity (CWP) in northwest, west-central, and southwest Kansas in relation to El Niño southern oscillation (ENSO) phase effects on precipitation and growing season cumulative thermal energy (CGDD). Using the GOSSYM crop growth simulator with actual 1961–2000 location weather records partitioned by the ENSO phase, we modeled crop growth, yield, and evapotranspiration (ET) for irrigation capacities of 2.5, 3.75, and 5.0 mmd⁻¹ and periods of 4, 6, and 8 weeks. Regardless of location, the ENSO phase did not influence CGDD, but precipitation and lint yield decreased significantly in southwest Kansas during La Niña compared with the Neutral and El Niño phases. Simulated lint yields, ET, CWP, and leaf area index (LAI) increased with increasing irrigation capacity despite application duration. Southwestern Kansas producers may use ENSO phase information with deficit irrigation to reduce groundwater withdrawals while preserving desirable cotton yields. Full article

Primarily owing to the pronounced fluorescence exhibited by its reduced form, resazurin (also known as alamarBlue^®) is widely employed as a redox sensor to assess cell viability in in vitrostudies. In an effort to broaden its applicability for in vivo studies, molecular adjustments are necessary to align optical properties with the near-infrared imaging window while preserving redox properties. This study delves into the theoretical characterisation of a set of fluorinated resazurin derivatives proposed by Kachur et al., 2015 examining the influence of fluorination on structural and electrochemical properties. Assuming that the conductor-like polarisable continuum model mimics the solvent effect, the density functional level of theory combining M06-2X/6-311G* was used to calculate the redox potentials. Furthermore, (TD-)DFT calculations were performed with PBE0/def2-TZVP to evaluate nucleophilic characteristics, transition states for fluorination, relative energies, and fluorescence spectra. With the aim of exploring the potential of resazurin fluorinated derivatives as redox sensors tailored for in vivo applications, acid–base properties and partition coefficients were calculated. The theoretical characterisation has demonstrated its potential for designing novel molecules based on fundamental principles. Full article

Qinghai spruce forests, found in the Qilian mountains, are a typical type of water conservation forest and play an important role in regulating the regional water balance and quantifying the changes and controlling factors for evapotranspiration (ET) and its components, namely, transpiration (T), evaporation (E_s) and canopy interceptions (E_i), of the Qinghai spruce, which may provide rich information for improving water resource management. In this study, we partitioned ET based on the assumption that total ET equals the sum of T, E_s and E_i, and then we analyzed the environmental controls on ET, T and E_s. The results show that, during the main growing seasons of the Qinghai spruce (from May to September) in the Qilian mountains, the total ET values were 353.7 and 325.1 mm in 2019 and 2020, respectively. The monthly dynamics in the daily variations in T/ET and E_s/ET showed that T/ET increased until July and gradually decreased afterwards, while E_s/ET showed opposite trends and was mainly controlled by the amount of precipitation. Among all the ET components, T always occupied the largest part, while the contribution of E_s to ET was minimal. Meanwhile, E_i must be considered when partitioning ET, as it accounts for a certain percentage (greater than one-third) of the total ET values. Combining Pearson’s correlation analysis and the boosted regression trees method, we concluded that net radiation (R_n), soil temperature (T_s) and soil water content (SWC) were the main controlling factors for ET. T was mainly determined by the radiation and soil hydrothermic factors (R_n, photosynthetic active radiation (PAR) and T_S30), while E_s was mostly controlled by the vapor pressure deficit (VPD), atmospheric precipitation (P_a), throughfall (P_t) and air temperature (T_a). Our study may provide further theoretical support to improve our understanding of the responses of ET and its components to surrounding environments. Full article