Search Results (50)

Biomimetic chromatography is a powerful tool used in the pharmaceutical industry to characterize the physicochemical properties of molecules during early drug discovery. Some studies have indicated that biomimetic chromatography may also be useful for the evaluation of toxicologically relevant molecules. In this study, we evaluated the usefulness of the biomimetic chromatography approach for determining the lipophilicity, affinity to phospholipids, and bind to plasma proteins of selected organophosphate pesticides. Quantitative structure–retention relationship (QSRR) models were proposed to understand the structural features that influence the experimentally determined properties. ACD/labs, Chemicalize, and alvaDesc software were used to calculate theoretical descriptors. Multilinear regression was used as the regression type, and feature selection was supported by a genetic algorithm. The obtained QSRR models were validated internally and externally, and they demonstrated satisfactory performance with key statistical parameters ranged from 0.844 to 0.914 for R² and 0.696–0.898 for R²_ext, respectively, indicating good predictive ability. Full article

To achieve a scientific and objective evaluation of soil acidification, secondary salinization, and nutrient imbalance in old protected vegetable fields (OPVs) with over 30 years of cultivation history, a soil surface breeding vigorous moss was investigated. Here, quantitative laboratory analysis and mathematical statistics were employed to explore the spatial distribution of soil salinity and nutrients, as well as their relationships. The results revealed that OPVs exhibited slightly acidified values. The measured anions and cations in the soil salt composition constituted approximately 77% of the total ions. Among which, Ca²⁺ was the dominant cation, while SO₄²⁻ and NO₃⁻ were predominant anions. The total water-soluble salt (TDS) content of the surface soil reached 4.52 g kg⁻¹, exceeding the Chinese Saline Soils standard (1.0 g kg⁻¹) by 350%. In the OPVs, nitrate nitrogen was significantly higher than ammonium nitrogen, and available phosphorus and available potassium were generally abundant. Despite exhibited various soil health concerns, a field visit survey presented consistently high and stable yields in OPVs. We hypothesize that this seemingly contradictory finding may be attributable to several factors, including the abundance of divalent cations (Ca²⁺ and Mg²⁺), the soil fertility and water retention capacity of unsaturated salt-based suitable soil, as well as good soil aggregate structure. These factors had the potential to reduce the stresses on the soil. This study provided a foundational understanding of the nutrient and salinity status of soils in OPVs, offering valuable data and theoretical groundwork for future research endeavors. Full article

Background/Objectives: Higher education institutions must improve the attractiveness and retention of the nursing profession to address the widespread shortage. This pilot multimethod study aimed to preliminarily understand the relationship between motivations for enrolment and dropout among first-year undergraduate nursing students. Methods: A two-step approach was conducted among first-year nursing students from five Italian universities involving: (a) a baseline quantitative online survey collecting their characteristics and motivations for enrolment; (b) a follow-up semi-structured interview qualitative data collection among students who dropped out. Descriptive and inferential statistics were used to describe the motivations for enrolment and differences between universities. Dropout motivations emerged from inductive content analysis, with data categorisation according to Urwin’s framework. Results: A total of 759 students completed the online survey. Primary motivations for enrolment included the desire to be useful (88.8%), help suffering people (84.3%), and find employment (74.2%); 22.3% cited unsuccessful admission to another university as motivation for enrolment. Of the 141 students who discontinued, 31 were interviewed (22%). Eleven categories and three themes were identified. More than half of the participants dropped out due to interest in other courses and lack of aptitude, while a smaller number cited personal circumstances. Other motivations for dropout were related to negative learning environments or feelings and difficulties related to course characteristics. Conclusions: This study provides an initial insight into these complex phenomena that will be instrumental in understanding data from an Italian multicenter cohort study. The findings can inform recommendations and strategies to strengthen the future nursing workforce. Full article

The present study investigated the volatile constituents of Cymbopogon citratus (lemongrass) grown in a greenhouse environment in Serbia, marking the first commercial cultivation of the plant for essential oil production in the region. The essential oils and hydrolates obtained through steam distillation were analyzed via gas chromatography–mass spectrometry (GC-MS), and the resulting chemical data were further processed using chemometric methods. This study applied quantitative structure retention relationship (QSRR) analysis, employing molecular descriptors (MDs) and artificial neural networks (ANNs) to predict the retention indices (RIs) of the compounds. A genetic algorithm (GA) was used to select the most relevant MDs for this predictive modeling. A total of 29 compounds were annotated in the essential oils, with geranial and neral being the dominant components, while 37 compounds were detected in the hydrolates. The ANN models effectively predicted the RIs of both essential oils and hydrolates, demonstrating high statistical accuracy and low prediction errors. This research offers valuable insights into the chemical profile of lemongrass cultivated in temperate conditions and advances QSRR modeling for essential oil analysis. Full article

This study solved a set of equations to verify the dynamic optimal conditions of nonthermal plasma (NTP)-chemical conversion of solid polyolefin wastes into liquid petroleum hydrocarbons. Furthermore, a novel optimisation model was validated with non-linear experimental conditions to assess the quantitative relationship between the process variables responsible for the degradation rate of wastes. The central composite design (CCD) experimental design was developed based on the Response Surface Model (RSM) technique. These techniques significantly improved the model predictions because of the more-detailed electrochemical description. Experiments were conducted in an in-house-designed and -developed NTP system with advanced data acquisition schemes. Both experimental and the numerical findings exhibited a good agreement, and the results indicated that the electrical factors of NTP could significantly affect the conversion yield (Y_conv%) of solid polyolefin-derived wastes to liquid hydrocarbons. Additionally, the model investigation indicated that factors such as power discharge ($x_1$), voltage intensity ($x_2$), and reaction retention time (RTT) ($x_3$) significantly influenced the conversion yield. After optimisation, a maximum conversion percentage (Y_conv%) of ≈93% was achieved. The findings indicated that this recommended framework could be effectively employed for scaling the plasma synergistic pyrolysis technique for generating the maximal Y_conv% of plastic wastes to yield an oil. Thereafter, the analysis of variance (ANOVA) technique was applied to examine the accuracy of the developed structure in order to upgrade this laboratory-scale processes to an industrial-scale process with >95% effectiveness. The calorific value of the produced oil was seen to be from 43,570.5 J/g to 46,025.5 J/g due to changes of the arrangements of the process factors, which specified that the liquid hydrocarbons showed similar characteristics like commercial diesel in this respect. Full article

At present, stationary phases based on ionic liquids are a promising and widely used technique in gas chromatography, yet they remain poorly studied. Unfortunately, testing of “new” stationary phases is often carried out on a limited set of test compounds (about 10 compounds) of relatively simple structures. This study represents the first investigation into the physicochemical patterns of retention of substituted (including polysubstituted) aromatic alcohols on two stationary phases of different polarities: one based on pyridinium-based ionic liquids and the other on a standard polar phase. The retention order of the studied compounds on such stationary phases compared to the standard polar phase, polyethylene glycol (SH-Stabilwax), was compared and studied. It was shown that pyridinium-based ionic liquids stationary phase has a different selectivity compared to the SH-Stabilwax. Using a quantitative structure–retention relationships (QSRR) study, the differences in selectivity of the two stationary phases were interpreted. Using CHERESHNYA software, the importance of descriptors on different stationary phases was evaluated for the same data set. Different selectivity of the stationary phases correlates with different contributions of descriptors for the analytes under study. For the first time, we show that in-column dehydration is observed for some compounds (mostly substituted benzyl alcohols). This effect is worthy of further investigation and requires attention when analyzing complex mixtures. It suggests that when testing “new” stationary phases, it is necessary to conduct tests on a large set of different classes of compounds. This is because, in the case of using ionic liquids as an stationary phase, a reaction between the analyte and the stationary phase is possible. Full article

Image editing technology has brought about revolutionary changes in the field of architectural design, garnering significant attention in both the computer and architectural industries. However, architectural image editing is a challenging task due to the complex hierarchical structure of architectural images, which complicates the learning process for the high-dimensional features of architectural images. Some methods invert the images into the latent space of a pre-trained generative adversarial network (GAN) model, completing the editing process by manipulating this latent space. However, the task of striking a balance between reconstruction fidelity and editing efficacy through latent space mapping presents a formidable challenge. To address this issue, we propose a Residual Spatial Cross-Attention Network (RSCAN) for architectural image editing, which is an encoder model integrating multiple latent spaces. Specifically, we introduce the spatial feature extractor, which maps the image to the high-dimensional space F of the synthesis network, to enhance the spatial information retention and preserve the structural consistency of the architectural image. In addition, we propose the residual cross-attention to learn the mapping relationship between the low-dimensional space W and F space, generating modified features corresponding to the latent code and leveraging the benefits of multiple latent spaces to facilitate editing. Extensive experiments are performed on the LSUN Church dataset, and the experimental results indicate that our proposed RSCAN achieves significant improvements over the relevant methods in quantitative analysis metrics including the reconstruction quality, SSIM, FID, L2, LPIPS, PSNR, and editing effect $\Delta S$, with enhancements of 29.49%, 17.29%, 8.81%, 11.43%, 11.26%, and 47.8%, respectively, thereby enhancing the practicality of architectural image editing. Full article

The concept of Green Human Resource Management (GHRM) is regarded as a major turning point in managing human capital among firms. Sustainable practices, ecofriendly initiatives, and adequate management of employees (i.e., recruitment, training, performance, rewards, and involvement) are fundamental aspects of GHRM, which enable improvements in the performance of firms and enhanced competitiveness among their rivals. In this regard, the current study takes a quantitative approach towards analyzing GHRM practices and their effects on employee retention among hotels in the UAE. Furthermore, the indirect effect of green innovation is analyzed as a potential mediating variable that can better explain the GHRM–employee retention relationship. A total of 207 employees from five 5-star hotels were selected as participants to provide information regarding the factors under examination in this research. The collected data were analyzed using Smart-PLS v.3 and a partial least squares–structural equation modeling technique, which is a fitting technique for causal models. The perspective of employees on the outcome of GHRM initiatives and their willingness to remain in their firms can greatly contribute to the current understanding of GHRM and its effectiveness on employee retention in the context of the hotel industry of the UAE, and thus, aid practitioners and scholars alike. Full article

Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors. Lipophilicity indices were determined using computational and chromatographic approaches. In addition, the affinity to human serum albumin (HSA) and phospholipids was assessed using biomimetic chromatography protocols. Quantitative Structure–Retention Relationship (QSRR) methodologies were used to determine the impact of theoretical descriptors on experimentally determined properties. A multiple linear regression (MLR) model was calculated to identify the most important features, and genetic algorithms (GAs) were used to assist in the selection of features. The resultant models showed commendable predictive accuracy, minimal error, and good concordance correlation coefficient values of 0.876, 0.149, and 0.930 for the validation group, respectively. Full article

With the rapid advancement of the global economy, there has been a noticeable escalation in the level of inhalable particulate matter (PM) pollution in the atmosphere. The utilization of plants has been recognized as an effective means to mitigate the escalation in the atmospheric PM concentration through the capture and retention of this particulate matter on their leaves. This research focuses on investigating the PM retention capacity of nine commonly found greening plant species in Changsha, China, located in the country’s mid-subtropical region. In this study, we employed an air aerosol generator (QRJZFSQ-II) and a portable leaf area meter (LI-3000C) to systematically evaluate the PM retention in unit leaf area for different PM values. In addition, the leaf surface structure was observed via scanning electron microscopy, and the relationship between the leaf microstructure and the retained particles was quantitatively analyzed. The results showed that (1) there were significant differences in the retention of TSP, PM₁₀, and PM_2.5 per unit leaf area among the nine greening tree species analyzed. Rosa saturata was found to have the best retention effect regarding TSP and PM_2.5, and Rhododendron simsii was found to have the best retention effect regarding PM₁₀. (2) There were significant differences in the contents of TSP and PM_2.5 per leaf area among the different tree species with different life forms (p < 0.05), with the order of retention being shrub > arbor (needle leaves) > arbor (broad leaves). (3) Coniferous plants have a deep leaf surface texture, which is conducive to capturing more particles on their leaf surface, and (4) the long stomata diameter was significantly negatively correlated with PM retention, and the stomata density was significantly positively correlated with PM retention. However, the short diameter and small area of stomata demonstrated no significant correlation with PM retention (p < 0.05). Considering the selection of suitable tree species for greening in urban air pollution control, we suggest that Osmanthus fragrans, Pseudolarix amabilis, Rosa saturata, and Rhododendron simsii be used more frequently in urban areas affected by severe air pollution. Full article

Gibberellins (GAs) play a pivotal role in modulating plant growth and development. Glucose–conjugated gibberellins (Glc–GAs), a prevalent conjugated form of GAs, regulate intracellular GA levels by the coupling and decoupling of glucose groups. However, the diversity of Glc–GAs identified within individual species remains limited, hinting at a multitude of yet undiscovered gibberellin metabolites. This lacuna poses considerable impediments to research efforts dedicated to comprehensively delineating the GA metabolic pathway. In this study, we developed a structure–oriented screening and identification method for Glc–GAs in plant species by employing LC–MS/MS coupled with chemical derivatization. Through the application of chemical derivatization technique, carboxyl groups on Glc–GAs were labeled which effectively enhanced the sensitivity and selectivity of mass spectrometry detection for these compounds. Concurrently, the integration of mass spectrometry fragmentation and chromatographic retention behavior facilitated the efficient screening and identification of potential Glc–GAs. With this strategy, we screened and identified 12 potential Glc–GAs from six plant species. These findings expand the Glc–GA diversity in plants and contribute to understanding GA metabolic pathways. Full article

A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods. Full article

The n-octanol–water partition coefficient (logP) is an important physicochemical parameter which describes the behavior of organic compounds. In this work, the apparent n-octanol/water partition coefficients (logD) of basic compounds were determined using ion-suppression reversed-phase liquid chromatography (IS-RPLC) on a silica-based C18 column. The quantitative structure–retention relationship (QSRR) models between logD and logk_w (logarithm of retention factor corresponding to 100% aqueous fraction of mobile phase) were established at pH 7.0–10.0. It was found that logD had a poor linear correlation with logk_w at pH 7.0 and pH 8.0 when strongly ionized compounds were included in the model compounds. However, the linearity of the QSRR model was significantly improved, especially at pH 7.0, when molecular structure parameters such as electrostatic charge n_e and hydrogen bonding parameters A and B were introduced. External validation experiments further confirmed that the multi-parameter models could accurately predict the logD value of basic compounds not only under strong alkaline conditions, but also under weak alkaline and even neutral conditions. The logD values of basic sample compounds were predicted based on the multi-parameter QSRR models. Compared with previous work, the findings of this study extended the pH range for the determination of the logD values of basic compounds, providing an optional mild pH for IS-RPLC experiments. Full article

The soil-water characteristic curve (SWCC) is an essential tool to determine hydraulic and mechanical properties of unsaturated soils. As an inherent influencing factor, soil texture controls the characteristics of SWCCs. Fractal theory can quantitatively describe the physical characteristics of soil. This study used particle size distribution data and water content data contained in the UNSODA2.0 database to explore the fractal characteristics of 12 soil types with different textures under different matrix suctions. The SWCC fractal model was adopted to characterize the hydraulic properties of soil with various soil textures. The findings revealed that the mass fractal dimensions of particles from these 12 different soil types significantly differed and were closely related to the clay content. Fractal dimension increased with increasing clay content. The fractal dimension established a good relationship between soil structure and hydraulic properties. Fractal analysis can be used to determine the connection between physical properties and soil hydraulic parameters. The estimated results of the SWCC fractal model indicated that it had a good performance regarding the description of SWCCs for the 12 soil textures. The soil structure could be described through fractal dimensions, which can effectively indicate soil hydraulic characteristics. The estimated fractal dimension of this model could be obtained by particle size distribution. Furthermore, using the SWCC fractal model, we found that the SWCC of coarse textured soil changed sharply in the low suction stage and its residual water content was small, and the SWCC of fine textured soil changed gently with a large residual water content. The water retention capacity followed the order clay > silty clay > sandy clay > clay loam > silty clay loam > sandy clay loam > loam > silt loam > sandy loam > silt > loamy sand > sand. Full article

Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules. Full article