Partial Least Squares Regression Method Research Articles

Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

The precise measurement of Al, Mg, Ca, and Zn composition in copper slag is crucial for effective process control of copper pyrometallurgy. In this study, a remote laser-induced breakdown spectroscopy (LIBS) system was utilized for the spectral analysis of copper slag samples at a distance of 2.5 m. The composition of copper slag was then analyzed using both the calibration curve (CC) method and the partial least squares regression (PLSR) analysis method based on the characteristic spectral intensity ratio. The performance of the two analysis methods was gauged through the determination coefficient (R 2), average relative error (ARE), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). The results demonstrate that the PLSR method significantly improved both R 2 for the calibration and test sets while reducing ARE, RMSEC, and RMSEP by 50% compared to the CC method. The results suggest that the combination of LIBS and PLSR is a viable approach for effectively detecting the elemental concentration in copper slag and holds potential for online detection of the elemental composition of high-temperature molten copper slag.

Quantification of lung surfactant lipid (dipalmitoylphosphatidylcholine/sphingomyelin) ratio in binary liposomes using Raman spectroscopy

AbstractEarly diagnosis of neonatal respiratory distress syndrome (nRDS) is important in reducing the mortality of preterm babies. Knowledge of the ratio of two components of lung surfactant, dipalmitoylphosphatidylcholine (DPPC), and sphingomyelin (SM) can be used as biomarkers of lung maturity and inform treatment. Raman spectroscopy is a powerful tool to analyze vibrational spectra of organic molecules which requires only limited sample preparation steps and, unlike IR spectroscopy, is not masked by water absorption. In this paper, we explore the potential of using Raman spectroscopy as a tool to estimate the ratio of DPPC and SM from aqueous vesicles of binary mixture of DPPC and SM. We demonstrate that the ratio of DPPC and SM can be estimated by estimating the ratio of intensity of CO stretch of DPPC and CC stretch of SM as well as CO stretch of DPPC and amide I of SM. Further, we employ a partial least squares regression (PLSR) model to automate the estimation and demonstrate that PLSR method can predict the DPPC and SM ratio with an R2 value of 0.968.

Compositional analysis of copper and iron-based alloys using LIBS coupled with chemometric method.

The present manuscript deals with the utility of the calibration-free LIBS and calibration curve methods for the compositional study of different alloys using laser-induced breakdown spectroscopy (LIBS). In the process of alloying in the smelting industry, metal concentration in different alloys affects the physical and chemical properties of the final products. Therefore, LIBS can be used as an efficient quantitative analysis tool for online monitoring of the quality of the products. This is because LIBS can be performed online, in situ, without any pre-processing, and need no sample preparation for the compositional analysis of any type of materials present in any phase (solid, liquid, gas or even molten alloys in the industries). In the present study, four alloys (three copper and one iron-based alloy) consisting of Cu, Al, Zn, Ni, Fe, Cr and Mn as major and Sn and Si as minor elements were selected for the study using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) and calibration curve method i.e. partial least square regression (PLSR). For the CF-LIBS method, the temporal delay has been optimized in order to satisfy the optically thin and local thermal equilibrium (LTE) condition of the plasma. For the PLSR method, different regions of the strongest emission lines of constituents have been selected for quantitative analysis. The study of time-resolved LIBS spectra and the variation of plasma parameters with respect to the delay time is also discussed. The utility of the combined technique of CF-LIBS with the PLSR method for rapid monitoring and quality assessment of desired material/products without any sample pretreatment, thus reducing the cost of the analysis, is presented in this paper.

A Computational Study on the Structural Prediction of InhA Inhibitors as Antimycobacterial Agents

InhA is an enoyl acyl carrier reductase that catalyzes the chemo-selective reduction of its 2-trans-enoyl-ACP substrate. The pharmacological effects of the frontline medications used in the treatment of tuberculosis are elicited by inhibiting the enzyme InhA, which disrupts the mycolic acid biosynthesis pathway. The present study involves development of ligand-based pharmacophore model, docking studies, generation of 3D-QSAR model, and molecular dynamic simulation studies of virtually screened putative InhA inhibitors. The best field-based 3D-QSAR model was validated using partial least-square regression (PLS) method with high regression coefficient for training set (R 2) = 0.9256 and test set (R 2) = 0.7542, cross-validated coefficient (r cv 2) = 0.72 and R 2 pred = 0.9764. Also, virtual screening yielded compounds 4, 25, 1, and 30 exhibiting appreciable interactions (docking scores −5.972, −3.819, −3.801, and −3.701, respectively with InhA synthetase; PDB ID: 1BVR). Further, ADMET studies supported drug-like potential of compounds 25, 1, and 30 whereas compound 4 showed negligible human oral absorption. The molecular dynamic simulation studies of the top scored molecule 25 suggested stability of 25-1BVR complex over the course of simulation run. Therefore, this work can be helpful for future discovery of novel InhA inhibitors against drug-resistant tuberculosis.

UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation

Chlorophyll is an important indicator for monitoring crop growth and is vital for agricultural management. Therefore, rapid and accurate estimation of chlorophyll content is important for decision support in precision agriculture to accurately monitor the SPAD (Soil and Plant Analyzer Development) values of winter wheat. This study used winter wheat to obtain canopy reflectance based on UAV hyperspectral data and to calculate different vegetation indices and red-edge parameters. The best-performing vegetation indices and red-edge parameters were selected by Pearson correlation analysis and multiple stepwise regression (MSR). SPAD values were estimated using a combination of vegetation indices, vegetation indices and red-edge parameters as model factors, two types of machine learning (ML), a support vector machine (SVM), and a backward propagation neural network (BPNN), and partial least squares regression (PLSR) for four growth stages of winter wheat, and validated using independent samples. The results show that for the same data source, the best vegetation indices or red-edge parameters for estimating SPAD values differed at different growth stages and that combining vegetation indices with red-edge parameters gave better estimates than using only vegetation indices as an input factor for estimating SPAD values. There is no significant difference between PLSR, SVM, and BPNN methods in estimating SPAD values, with better stability of the estimated models using machine learning methods. Different growth stages have a large impact on winter wheat SPAD values estimates, with the accuracy of the four growth stage models increasing in the following order: booting < heading < filling < flowering. This study shows that using a combination of vegetation indices and red-edge parameters can improve SPAD values estimates compared to using vegetation indices alone. In the future, the choice of appropriate factors and methods will need to be considered when constructing models to estimate crop SPAD values.

Quartz-Enhanced Photoacoustic Spectroscopy Assisted by Partial Least-Squares Regression for Multi-Gas Measurements.

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photoacoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3), and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a mid-infrared (MIR) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra are a common challenge for gas spectroscopy. To mitigate this, we used a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts per million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here, in this proof-of-concept experiment, we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3, and CH4 and with an absolute sensitivity of approximately 300 (±50) ppm/V, 50 (±5) ppm/V, and 5 (±2) ppm/V for water, ammonia, and methane, respectively. This demonstrates that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.

Development of statistical regression and artificial neural network models for estimating nitrogen, phosphorus, COD, and suspended solid concentrations in eutrophic rivers using UV–Vis spectroscopy

River water quality monitoring is crucial for understanding water dynamics and formulating policies to conserve the water environment. In situ ultraviolet–visible (UV–Vis) spectrometry holds great potential for real-time monitoring of multiple water quality parameters. However, establishing a reliable methodology to link absorption spectra to specific water quality parameters remains challenging, particularly for eutrophic rivers under various flow and water quality conditions. To address this, a framework integrating desktop and in situ UV–Vis spectrometers was developed to establish reliable conversion models. The absorption spectra obtained from a desktop spectrometer were utilized to create models for estimating nitrate-nitrogen (NO3-N), total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and suspended solids (SS). We validated these models using the absorption spectra obtained from an in situ spectrometer. Partial least squares regression (PLSR) employing selected wavelengths and principal component regression (PCR) employing all wavelengths demonstrated high accuracy in estimating NO3-N and COD, respectively. The artificial neural network (ANN) was proved suitable for predicting TN in stream water with low NH4-N concentration using all wavelengths. Due to the dominance of photo-responsive phosphorus species adsorbed onto suspended solids, PLSR and PCR methods utilizing all wavelengths effectively estimated TP and SS, respectively. The determination coefficients (R2) of all the calibrated models exceeded 0.6, and most of the normalized root mean square errors (NRMSEs) were within 0.4. Our approach shows excellent efficiency and potential in establishing reliable models monitoring nitrogen, phosphorus, COD, and SS simultaneously. This approach eliminates the need for time-consuming and uncertain in situ absorption spectrum measurements during model setup, which may be affected by fluctuating natural and anthropogenic environmental conditions.

Screening anti-fatigue components of American ginseng saponin by analyzing spectrum-effect relationship coupled with UPLC-Q-TOF-MS.

American ginseng has an obvious anti-fatigue effect, but the effective material basis is still unclear. The spectrum-effect relationship is a scientific method that studies the correlations between chemical spectra and pharmacological effect. To reveal the real bioactive compounds in American ginseng saponin (AGS) based on a study of the underlying correlations between these compounds' occurrence in rat serum after their intake of AGS and the anti-fatigue effect of AGS. We utilized ultra-performance liquid chromatography (UPLC) with quadrupole and time-of-flight mass spectrometry (Q-TOF-MS) to analyze the extract of AGS and its constituents in serum after oral administration in rats. The anti-fatigue effect of AGS in rats was measured using the time weight-bearing swimming technique, the content of blood urea nitrogen, hepatic glycogen, and blood lactic acid. The relationship between the peak area values in fingerprints from rat serum and pharmacodynamic parameters of AGS was established using correlation analysis with partial least square regression (PLSR) method and gray correlation method. We detected and identified 22 compounds from extract, and 8 prototype components from serum. Through PLSR and gray correlation method, it was found that the ginsenosides Re, Rb1, and Rb2 were significantly positively related to the pharmacodynamic data. Based on the spectrum-effect relationship, PLSR and gray correlation method can be used to screen for the anti-fatigue components available in AGS. Such an approach is of practical significance as it provides an effective means for exploring the material basis for the efficacy of American ginseng, particularly as an anti-fatigue agent.

Selection of principal variables through a modified Gram–Schmidt process with and without supervision

AbstractIn various situations requiring empirical model building from highly multivariate measurements, modelling based on partial least squares regression (PLSR) may often provide efficient low‐dimensional model solutions. In unsupervised situations, the same may be true for principal component analysis (PCA). In both cases, however, it is also of interest to identify subsets of the measured variables useful for obtaining sparser but still comparable models without significant loss of information and performance. In the present paper, we propose a voting approach for sparse overall maximisation of variance analogous to PCA and a similar alternative for deriving sparse regression models influenced closely related to the PLSR method. Both cases yield pivoting strategies for a modified Gram–Schmidt process and its corresponding (partial) QR‐factorisation of the underlying data matrix to manage the variable selection process. The proposed methods include score and loading plot possibilities that are acknowledged for providing efficient interpretations of the related PCA and PLS models in chemometric applications.

Research into Heat Stress Behavior Recognition and Evaluation Index for Yellow-Feathered Broilers, Based on Improved Cascade Region-Based Convolutional Neural Network

The heat stress response of broilers will adversely affect the large-scale and welfare of the breeding of broilers. In order to detect the heat stress state of broilers in time, make reasonable adjustments, and reduce losses, this paper proposed an improved Cascade R-CNN (Region-based Convolutional Neural Networks) model based on visual technology to identify the behavior of yellow-feathered broilers. The improvement of the model solved the problem of the behavior recognition not being accurate enough when broilers were gathered. The influence of different iterations on the model recognition effect was compared, and the optimal model was selected. The final average accuracy reached 88.4%. The behavioral image data with temperature and humidity data were combined, and the heat stress evaluation model was optimized using the PLSR (partial least squares regression) method. The behavior recognition results and optimization equations were verified, and the test accuracy reached 85.8%. This proves the feasibility of the heat stress evaluation optimization equation, which can be used for reasonably regulating the broiler chamber.

Comparing Machine Learning Algorithms for Soil Salinity Mapping Using Topographic Factors and Sentinel-1/2 Data: A Case Study in the Yellow River Delta of China

Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R2 = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction.

Estimation and mapping of pasture biomass in Mongolia using machine learning methods

Mongolian pasture plays an essential role in the national economy. Reliable pasture biomass estimation is indispensable to support the agricultural sector and also sustainable livelihood in the country. The aim of this study is to determine an appropriate method to estimate and map pasture biomass in a forest-steppe area of Mongolia. For this purpose, machine learning methods such as random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) were compared. As data sources, spectral indices derived from Sentinel-2B image of 2019 and field-measured biomass sample datasets were used. To determine the optimal spectral predictor variables, initially, 20 spectral indices were evaluated using the PLSR. Of these, five indices (i.e. ATSAVI2, EVI, GRVI, IPVI and MSR) with the highest correlation coefficients (r ≥ 0.94) were considered for further analysis. These indices were also examined and validated by a variable importance analysis. Then, the RF, SVM, and PLSR models were applied to predict and map pasture biomass using the selected five indices. The PLSR method demonstrated the highest accuracy with coefficient of determination (R2) =0.899 and root mean square error (RMSE)=10.560 g/m2. The SVM technique showed the second highest accuracy with R2=0.837 and RMSE = 12.881 g/m2. The RF model gave the lowest accuracy with R2=0.823 and RMSE = 13.430g/m2. Our research showed that different machine learning models might be applied (because in all cases R2>0.82) for a pasture biomass estimation and mapping in the selected test site, but the best result could be achieved by the use of the PLSR.

The Allocation Change of Rural Land Consolidation Type Structure under the Influence Factors of Different Geographical and Economic Development of China.

Land consolidation structure reflects land consolidation function, and studies about its spatio-temporal change and driving mechanism can serve for regional management and control land consolidation. At present, the analysis of regional differences, time changes, and driving factors of land consolidation type structure change is relatively lacking. Based on the data of provincial acceptance projects from 2000 to 2014, this paper analyzes the spatio-temporal change of rural land consolidation type structure in China, discusses the impact of relevant policies, and identifies the socio-economic driving factors in key regions by employing correlation analysis and the PLSR (partial least squares regression) method. The results showed that from 2000 to 2014, the proportional increase of land arrangement in China was significantly correlated with the proportional decrease of land reclamation (R2 = 0.93), and the proportional decrease of land development (R2 = 0.99) showed an obvious co-evolution pattern of increase and decrease; TILC (The area of land development/The area of land arrangement) decreased from 2.14 to 0.91 in 2002-2003. Since 2003, the dominant type of land consolidation in China has gradually changed from land development to land arrangement. However, the proportion of land development in QT (Qinghai-Tibet), JY (Jin-Yu), and FGH (Fujian-Guangdong-Hainan) areas is still more than 40%; the change of land consolidation type structure was influenced by policies, social and economic factors, such as urbanization rate, fixed assets investment, industrial proportion, and population density, and the regional difference was significant: the eastern section (JZS, Jiangsu-Zhejiang-Shanghai) is the industry proportion, the central area (HHAJ, Hunan-Hubei-Anhui-Jiangxi) is grain production and fixed assets investment, the western region (NW, Northwest China) has the urbanization rate, grain production, population density, and fixed assets investment. Land consolidation structure should be configured differentially in each region based on the identification of regional function orientation and comprehensive consideration of regional resource endowment and development needs and directions to improve the efficiency of land consolidation.

A deep learning-based bridge damage detection and localization method

Existing studies have utilized highly efficient partial least-squares regression (PLSR) to estimate nodal loads of the entire bridge using a small number of bridge sensors, and when the structure is damaged, the estimated nodal loads include damage information. Based on the ability of convolutional neural networks (CNNs) that can learn the PLSR method to estimate nodal loads, this paper proposes a bridge damage detection and localization method using inclination or deflection measurements. First, this study develops a method for estimating excessive nodal loads and establishes a framework for bridge damage detection and localization utilizing the change in the deviation of excessive nodal loads estimated by a CNN and the PLSR method before and after structural damage. Then, a CNN model is designed in this study, and the CNN model establishes a mathematical relationship between the monitoring point response as input and the estimated excessive nodal load as output through training. Finally, the detection and localization of bridge damage are realized using the proposed calculation method of damage indicator. The proposed method avoids costly finite element modeling and does not require difficult-to-obtain real structural damage information to train network models, and can achieve real-time detection and localization of bridge damage with a small number of sensors installed. Numerical simulations show that the proposed method can detect and locate damage very accurately and reliably in the presence of unknown loads, multi-damage, and measurement errors, revealing its potential in the field of bridge damage detection.

Development of a PLSR-BRT Model for Predicting the Performance of Tunnel Boring Machines

Scientific prediction of the penetration rate (PR) of tunnel boring machines (TBMs) is of great significance to the selection of tunnel construction methods, the prediction of construction progress, and the evaluation of benefits. In view of the high nonlinearity, fuzziness, and complexity of the TBM excavation process, this paper attempts to present a new algorithm that integrates partial least squares regression (PLSR) with boosted regression trees (BRT) for predicting the PR of TBMs. For this purpose, 150 datasets were obtained from the Lanzhou water conveyance tunnel project in China. Six impact factors were selected as the input layers of the proposed model by single-factor correlation analysis. To develop the PLSR-BRT model, two principal components of the influencing parameters were extracted via the PLSR method, and the BRT algorithm was employed to establish the predictive model. In addition, other models such as PLSR back propagation neural network (BPNN), BRT, PLSR, support vector regression (SVR), and artificial neural network (ANN) were adapted for use in this problem to verify the predictive accuracy of the proposed model. The results demonstrated that the PLSR-BRT model achieved the best predictive performance compared with the other models, with coefficient of determination value (R2) and root mean square error (RMSE) of 0.96 and 1.78, respectively, for the testing set. The PLSR-BRT model can maximally avoid both overfitting and inadequate fitting. In view of engineering applications, the complexity and redundancy of the field dataset can be solved via reducing the dimensionality of input parameters, and the training samples could be expanded by a boosted method so that the discontinuously acquired geological parameters are unable to restrain the model performance. This method serves as an effective approach for TBM PR prediction and has excellent potential for a variety of scientific and engineering applications.

Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases

One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity estimations. The PlanetScope satellite array provides high-precision mapping for land surface monitoring through its 3-m spatial resolution and near-daily revisiting frequency. This study’s use of the PlanetScope satellite array is a new attempt to estimate soil salinity in inland drylands. We hypothesized that field observations, PlanetScope data, and spectral indices derived from the PlanetScope data using the partial least-squares regression (PLSR) method would produce reasonably accurate regional salinity maps based on 84 ground-truth soil salinity data and various spectral parameters, like satellite band reflectance, and published satellite salinity indices. The results showed that using the newly constructed red-edge salinity and yellow band salinity indices, we were able to develop several inversion models to produce regional salinity maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), and Extreme Gradient Boosting algorithm (XGBoost), were applied for variable selection. The newly constructed yellow salinity indices (YRNDSI and YRNDVI) had the best Pearson correlations of 0.78 and −0.78. We also found that the proportions of the newly constructed yellow and red-edge bands accounted for a large proportion of the essential strategies of the three algorithms, with Boruta feature preference at 80%, RF at 80%, and XGBoost at 60%, indicating that these two band indices contributed more to the soil salinity estimation results. The best PLSR model estimation for different strategies is the XGBoost-PLSR model with coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) values of 0.832, 12.050, and 2.442, respectively. These results suggest that PlanetScope data has the potential to significantly advance the field of soil salinity research by providing a wealth of fine-scale salinity information.

Application of ensemble neural-network method to integrated sugar content prediction model for citrus fruit using Vis/NIR spectroscopy.

As consumers’ preference for sweetness in food products is increasing, most agricultural product processing complexes currently operate sugar-screening machines. However, every season, the entire prediction models have to be modified to fit the fruit species cultivated that season using a long-drawn process, because separate models are required for each species. Therefore, in this study, species-integrated prediction models were examined based on three citrus species. The species-specific and species-integrated prediction performance of classical partial least squares regression (PLSR)-based, neural-network-based, and ensemble-based models were evaluated. Four different types of ensemble models were proposed depending on the combination method of layers and classification features. The analytical results indicated that the Ensemble Type-4 model exhibited the best performance on both species-specific and species-integrated data, with average 9.9% and 22.1% reduction in RMSETest compared with that of conventional PLSR methods. Furthermore, based on the structural advantages of modularity in ensemble models, effective model maintenance is expected to be possible for future applications in the field.

Dust source clay content and salinity estimation using VNIR spectrometry

The spectral behavior of soil will change through degradation, which makes it difficult to retrieve soil properties using previously developed models. This study aims to use linear [including partial least squares regression (PLSR) and ratio soil index (RSI)] and nonlinear [including partial least squares-backpropagation neural network (PLS-BPNN) and partial least squares-random forest (PLS-RF)] models to estimate soil electrical conductivity (EC) and clay content in dust sources. For this purpose, 142 soil samples were collected in Khuzestan province. After laboratory spectroscopic analysis, the area and depth of diagnostic absorption features (AFs) of continuum removed (CR) spectra were calculated. The results revealed that with increasing clay content, the depth of AFs at 1400, 1900, and 2200 nm will increase. Meanwhile, an increase in the soil salinity will increase the depth and area of AFs in 1450 and 1915 nm and decrease the depth and area of AF in 2200 nm. Spectral ranges of 2100–2300 and 1400–1600 nm were identified as the most important portions of the visible and near-infrared spectrum for analyzing clay content and EC, respectively. The RSI method performed poorly in soil salinity and clay content estimation. PLSR and PLS-RF methods overestimated clay content and salinity in low values. The PLS-BPNN model had the best performance for estimating clay content (RPIQ = 4.51) and EC (RPIQ = 4.76). Considering the expected non-linear relationship between soil properties and corresponding spectral reflectance, the results of this study were acceptable.

Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential.

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power (Q2EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

How do medication errors occur in the nursing communication process? Investigating the relationship between error types and error factors.

Human error types and error factors are two important elements of error analysis. Understanding the relationship between them can contribute to new case analyses, the tendency of error occurrence statistics, error factor identification, and prevention of error recurrence. To provide evidence and guidance for the prevention and improvement of medication communication errors by quantitatively exploring the relationship between error types and error factors. Data were collected on self-reported errors in the medication administration process by nurses in all departments of three cooperative medical institutions, and an error sheet of specified style was adopted. Error types were determined by the systematic human error reduction and prediction approach method and human cognition processes. Error factors were extracted using the root cause analysis combined with Berlo's communication model, and the relationship between error types and error factors was quantitatively studied using the partial least-squares regression method. After a one-by-one analysis of 303 error cases, the communication errors occurring in the nursing medication process could be explained by six error types and 12 error factors. In addition, 20 correlation patterns between the error types and error factors were quantitatively obtained, and their path coefficient distributions ranged from 0.088 to 0.467. The results of this study may provide reference to understand errors and establish countermeasures from the statistics of error occurrence trends, extract error factors related to error types and determine key error factors.