Mean Value Of R2 Research Articles

Development of Soil Fertility Index Using Machine Learning and Visible-Near-Infrared Spectroscopy

An accurate assessment of soil fertility is crucial for monitoring environmental dynamics, improving agricultural productivity, and achieving sustainable land management and utilization. The inherent complexity and spatiotemporal heterogeneity of soils result in significant challenges in soil fertility assessment. Therefore, this study focused on developing a rapid, economical, and precise approach to evaluate soil fertility through the application of visible-near-infrared spectroscopy (VNIR). To achieve this, we utilized the Land Use and Cover Area Frame Survey (LUCAS) dataset and employed a variety of prediction models, including partial least squares regression, support vector machines (SVMs), random forest, and convolutional neural networks, to estimate various soil properties and overall soil fertility. The results showed that the SVM model had the highest prediction accuracy, particularly for clay content (coefficient of determination (R2) = 0.79, ratio of performance to interquartile range (RPIQ) = 3.04), pH (R2 = 0.84, RPIQ = 4.54), total nitrogen (N) (R2 = 0.80, RPIQ = 2.40), and cation exchange capacity (CEC) (R2 = 0.83, RPIQ = 3.16). A soil fertility index (SFI) was developed based on factor analysis, integrating nine essential soil properties: clay content, silt content, sand content, pH, carbonate content, N, soluble phosphorus, soluble potassium, and CEC. We compared direct and indirect prediction models for estimating SFI and found that both models showed high accuracy (mean value of R2 = 0.80, mean value of RPIQ = 2.21). Additionally, SFI was classified into five classes to provide insights for precision agriculture. The kappa coefficient was 0.63, which indicated that the SFI evaluation results between VNIR and chemical analysis were relatively consistent. This study provides a theoretical foundation of real-time soil fertility monitoring for the optimization of agricultural practices.

Drying kinetics and heat mass transfer characteristics of thin layer pineapple during microwave vacuum drying

Microwave vacuum drying of pineapple slices was carried out to investigate the microwave power degree on the drying kinetics and heat mass transfer characteristics. Numerical simulation and bench test were used to study the variation characteristic of microwave volumetric heating and temperature and moisture during the drying process, and the best drying model was selected. The result shows that the drying process of the pineapple slices belonged to the falling rate period without constant drying stage; Among 6 common thin layer drying kinetic models, the mean values of R2 were maximum, χ2 and RMSE for the Two-term exponential model were minimum, which were 0.9993, 0.000107 and 0.00839, respectively. As the temperature rose to 40∼45∘C at the late stage of drying, the thermal runaway phenomenon appeared, and the internal temperature of pineapple slices rose sharply. Hot spots appeared at the center and edge of pineapple at the late stage of drying, and it generated by microwave volumetric heating focusing. The predicted values of moisture ratio (MR) obtained from COMSOL simulation showed good consistency with the Two-term exponential model and the experimental. In order to improve the drying quality of the pineapple, the microwave power should be varied and reasonable interval to make the drying temperature lower than the thermal runaway temperature range.

Data-driven approach for investigating and predicting rutting depth of asphalt concrete containing reclaimed asphalt pavement

The aim of this paper is to investigate and predict the rutting depth of asphalt concrete containing Reclaimed Asphalt Pavement (RAP) content by the data-driven approach with aid of 6 six tree algorithm including Random Forest (RF), Gradient Boosting (GB), Adaptive Boosting (AdaB), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LightGB) and Categorial Gradient Boosting (CatB) using default hyperparameters. A database containing 396 data samples derived from Hamburg Wheel Tracking Test (HWTT) and consisting of 11 input variables such as Corrected Optimum Asphalt Content (COAC), Reclaimed Asphalt Pavement, Nominal Maximum Aggregate Size (NMAS), Flashpoint, m-Value, Mass loss, Specific gravity, Viscosity, Creep stiffness, Temperature, and Number of wheels and 1 output Rutting depth are created. 10 times of 10-Fold CV and four metrics such as R2, RMSE, MAE and MAPE are used to verify the robustness of ML models. XGB model has highest performance with the mean value of R2, RMSE, MAE and MAPE to be equal to 0.9792, 1.0767 mm, 0.7085 mm, 0.1034% respectively. Comparing with other factors like the number of load cycles (number of wheels) and mix design considerations, temperature has a dominant impact on the rutting depth of asphalt pavement (type of binder, NMAS, RAP content). The rutting depth value rises with increasing temperature and COAC. NMAS has a negligible impact on asphalt pavement's resistance to rutting. To increase the rutting resistance when building the asphalt pavement, the chosen asphalt binder has high Viscosity and Flashpoint values. The local interpretation clearly demonstrates each factor's contribution to the rutting depth value in each unique scenario. Engineers can design the rutting resistance of asphalt pavement in advance thanks to the mapping produced by the Partial Dependence Plot 2D under grid contour.

Investigation of the dehydration of ulexite ore with different parameters and modeling with artificial neural network (ANN) method.

Study experiments were conducted considering the temperature, time, and sample weight parameters in order to model the dehydration by applying dehydration processes to ulexite ores. Data obtained from the dehydration processes of ulexite ore were compared with TG analyzes. It was observed as the result of the heat treatment that the fastest water removal was provided in the temperature range of 150-250 °C and it was very low in the range of 400-750 °C. In order to design the ANN method, 4 different models were proposed with the same parameters in the dehydration experiments and the network structure was determined. The performance of the ANN model was assessed by means of error measurements i.e. absolute error (AE), absolute relative error (ARE) and coefficient of determination (R2). The mean value of R2 was 99%. It was found that the independent variables explained the dependent variable efficiently and the models were very successful. It was shown that the new models can be created using genetic algorithms or hybrid methods in the future studies requiring fewer experiments by following the same process in the present study.

Constructing air temperature and relative humidity-based hourly thermal comfort dataset for a high-density city using machine learning

Global warming causes new challenges for urban citizens and metropolitan governments in adapting to the changing thermal environment. However, fine-scale spatiotemporal mapping of urban thermal environments has been inadequate. Therefore, this study takes a typical high-density city, Hong Kong, as an example and utilises a machine learning algorithm, the random forest (RF), to carry out 100 m resolution hourly thermal environment mapping, including air temperature (Ta), relative humidity (RH) and the net effective temperature (NET), for the summer season (May to September) of 2008–2018, considering meteorological drivers, topography and local-climate-zone-based landscape drivers. The validation results show that the developed dataset achieves satisfactory accuracy. The mean values of R2, root mean square error (RMSE) and mean absolute error (MAE) for Ta achieve 0.8723, 1.1160 °C and 0.8227 °C, respectively, while those for RH reach 0.7970, 5.3816% and 3.8641%. In addition, the thermal comfort index, NET, reveals that people in built-up areas feel hotter than measured by Ta during the night due to the urban heat island effect. We believe this newly developed thermal comfort dataset can provide novel, reliable and fine-grained data support for urban climate research areas such as urban heat islands, heat exposure, heat-related health risk assessment, and urban energy consumption estimation.

A comprehensive survey on conventional and modern neural networks: application to river flow forecasting

This study appraises different types of conventional (e.g., GRNN, RBNN, & MLPNN) and modern neural networks (e.g., integrative, inclusive, hybrid, & recurrent) in forecasting daily flow in the Thames River located in the United Kingdom. The models are mathematically, statistically, and diagnostically compared based on the forecasted results for ten different time-series assortments. The results indicate that all the neural network models acceptably forecasted the daily flow rate, with mean values of R2 > 0.92 and RMSE 0.94 and RMSE < 15.3 m3/s), they were not as computationally effective as the other applied models.

New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain

The estimation of Reference Evapotranspiration (ET0) is crucial to estimate crop water requirements, especially in developing countries and areas with scarce water resources. In these regions, the impossibility of collecting all the required data to compute FAO-56 Penman–Monteith equation (FAO56-PM) makes scientists search new methodologies to accurately estimate ET0 with the minimum number of climatic parameters. In this work, several neural network approaches have been evaluated for estimating ET0 using datasets from five weather stations located in Southern Spain (semiarid region of Andalusia). The assessment of statistical performance (Root Mean Square Error -RMSE-, Mean Bias Error -MBE-, coefficient of determination -R2- and Nash-Sutcliffe model efficiency coefficient -NSE-) of models namely Multilayer perceptron (MLP), Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM), Support Vector Machines (SVM), Random Forest (RF) and XGBoost were carried out using different input variables configurations. Only temperature-based data were used as inputs; the calculation of new variables called EnergyT (the integral of the half hourly temperature values of a day) and Hourmin (the difference in hours between time sunset and the time when the maximum temperature occurs) had promising results for the most humid stations. The good results obtained with EnergyT when it is used as an input of the system demonstrated that the information contained on it gives detailed characterization of the daily thermic behavior at each location, resulting in a more efficient model than those using only daily maximum, minimum temperature and extraterrestrial radiation values. In general, the modeling results showed that no model firmly outperformed the others, although MLP and ELM were commonly the models that gave the best performances for all sites: mean values of R2 > 0.89, mean values of NSE > 0.88, mean values of RMSE < 0.67 mm/day and mean values of MBE ranging from −0.17 to 0.30 mm/day. Therefore, EnergyT and Hourmin can be used to estimate ET0 more accurately in stations where data acquisition is limited, like in developing countries or at low-cost weather stations that cannot collect all the required meteorological variables used in FAO56-PM. Overall, the use of ELM is recommended due to its high performance in terms of efficiency (NSE) for all the configurations and for all locations, especially using EnergyT as an input variable.

Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination

PurposeThe purpose of this study is to assess Blood oxygenation level dependent Magnetic Resonance Imaging (BOLD-MRI) and Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) in the differentiation of benign and malignant breast lesions. MethodsFifty-nine breast lesions (26 benign and 33 malignant lesions) pathologically proven in 59 patients were included in this retrospective study. As BOLD parameters were estimated basal signal S0 and the relaxation rate R2*, diffusion and perfusion parameters were derived by DWI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp) and tissue diffusivity (Dt)). Wilcoxon-Mann-Whitney U test and Receiver operating characteristic (ROC) analyses were calculated and area under ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis (LDA), support vector machine, k-nearest neighbours, decision tree) with least absolute shrinkage and selection operator (LASSO) method and leave one out cross validation approach were considered. ResultsA significant discrimination was obtained by the standard deviation value of S0, as BOLD parameter, that reached an AUC of 0.76 with a sensitivity of 65%, a specificity of 85% and an accuracy of 76%. No significant discrimination was obtained considering diffusion and perfusion parameters. Considering LASSO results, the features to use as predictors were all extracted parameters except that the mean value of R2* and the best result was obtained by a LDA that obtained an AUC = 0.83, with a sensitivity of 88%, a specificity of 77% and an accuracy of 83%. ConclusionsGood performance to discriminate benign and malignant lesions could be obtained using BOLD and DWI derived parameters with a LDA classification approach. However, these findings should be proven on larger and several dataset with different MR scanners.

Non‐invasive quality analysis of thawed tuna using near infrared spectroscopy with baseline correction

AbstractIn order to investigate the ability of NIR spectroscopy coupled with baseline correction to detect quality‐related changes of thawed tuna during the storage and the transportation. First, adaptive iteratively reweighted penalized least squares (airPLS) method was taken to eliminate the baseline and estimate spectra feature peaks of 168 samples (bigeye = 82, yellowfin = 86), and the Monte Carlo method was used to determine the optimal adjustment parameter λ. Second, predictive models based on the fitted features were established by the partial least squares regression (PLSR) to estimate docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), fat, and protein content. Compared with the standard normal variate (SNV), the proposed airPLS achieved good performance in a comparative experimental test. When the original spectra was preprocessed by airPLS, the number of feature peaks (nf) and the mean value of R2 () achieved 6 and 0.89 respectively, which is greater than that of the preprocessing algorithm SNV (nf = 4 and ). The proposed airPLS effectively reduces the interfering variability and reserves much more NIR features, which is a feasible and stable method for quality‐related analysis of the thawed tuna.Practical application: During the storage and the transportation, the rich nutrients in tuna are easily oxidized and degraded when they are exposed to the air. In order to develop effective methods to determine the quality of tuna and avoid selling thawed products as the fresh tuna, a quality‐related analysis method for thawed tuna based on the baseline correction is proposed to improve the prediction accuracy.

Determination of P-wave Velocity of Carbonate Building Stones During Freeze–Thaw Cycles

In this paper, laboratory investigations were conducted on 22 carbonate building stones (including 3 types of limestone, 12 types of travertine and 7 types of marble) from various quarries in Iran. Based on data obtained from these investigations, an empirical equation was developed to determine the P-wave velocity (VP) of stones during freeze–thaw (F–T) cycles. This equation was developed by combining two previously suggested F–T decay models and predicts VP of stones after each cycle of F–T based on properties of fresh stone. During developing phase, the performance of proposed equation was evaluated using three statistical indices including the coefficient of determination (R2), the root mean square error (RMSE) and the variance account for (VAF). The mean values of R2, NRMSE and VAF were obtained as 0.940, 0.027 and 93.921, respectively, which indicates good prediction capacity of proposed equation. Furthermore, the results obtained from the validating phase indicated that the equation provides reliable prediction of Vp with error percentage of less than 10%. Therefore, the output of equation can be applied to assess the deterioration of building stones during F–T action.

A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data

The modeling of Near-Surface Temperature Lapse Rate (NSTLR) is of great importance in various environmental applications. This study proposed a new approach for modeling the NSTLR based on the Normalized Land Surface Temperature (NLST). A set of remote sensing imagery including Landsat images, MODIS products, and ASTER Digital Elevation Model (DEM), land cover maps, and climatic data recorded in meteorological stations and self-deployed devices located in the three study area were used for modeling and evaluation of NSTLR. First, the Split Window (SW) and Single Channel (SC) algorithms were used to estimate LST, and the spectral indices were used to model surface biophysical characteristics. The solar local incident angle was obtained based on topographic and time conditions for different dates. In the second step, the NSTLR value was calculated based on the LST-DEM feature space at the regional scale. The LST was normalized relative to the surface characteristics based on Random Forest (RF) regression and the NSTLR was calculated based on the NLST-DEM feature space. Finally, the coefficient of determination (R2) and Root Mean Square Error (RMSE) between the modeled NSTLR and the observed NSTLR were calculated to evaluate the accuracy of the modeled NSTLR. The mean values of R2 between DEM and NLST were improved 0.3, 0.42 and 0.35, rather than between DEM and LST for the study area A, B and C, respectively. The R2 and RMSE between the observed NSTLR and the Landsat derived NSTLR based on NLST for the study area A (B, C) were improved 0.30 (0.26, 0.35) and 0.81 (0.80, 0.94) °C Km−1, respectively, rather than the Landsat derived NSTLR based on LST. Also, for the study area A, the R2 and RMSE between the observed NSTLR and the MODIS derived NSTLR based on NLST in spring, summer, autumn and winter were improved 0.17, 0.12, 0.10, and 0.22; and 0.51, 0.44, 0.27, and 0.51 °C Km−1, respectively, rather than the MODIS derived NSTLR based on LST. Model assessment results (R2 and RMSE) and comparing modeled NSTLR (all strategies) with observed NSTLR, for both Landsat and MODIS, showed that the use of NLST instead of LST, significantly improved the accuracy of the obtained NSTLR in the mountainous regions.

Response of Soil Water and Wheat Yield to Rainfall and Temperature Change on the Loess Plateau, China

Understanding the influences of rainfall and temperature on soil water and the grain production of winter wheat (Triticum aestivum L.), is of great importance to ensure the sustainability of food production on the Loess Plateau of China. Based on calibration and evaluation, the Environmental Policy Integrated Climate (EPIC) model was employed to determine the response of soil water and winter wheat to rainfall and temperature changing over the last 30 years in different regions. Results showed that (1) the EPIC model simulated soil water content well in 0–2 m soil, with a relative root mean square error (RRMSE) value of 6.0~14.0%, and the mean value of R2 was 0.824, which was similar to the value of ME (0.815); (2) rainfall decreased 13.6–24.9% more from 2001 to 2010 than it did during 1961–2000, while its minimum and maximum temperature increased 1.00–1.55 °C and 0.30–0.84 °C respectively, in comparison with 1961–2000; (3) both the increase of maximum temperature and the decrease of rainfall were harmful to the production of winter wheat. Contrarily, the increase of minimum temperature was beneficial to the production of winter wheat on the Loess Plateau of China. Furthermore, due to rainfall decreasing, the winter wheat yield of Luochuan, Changwu, Yuncheng, and Yan’an decreased by 8.5%, 7.6%, 11.7%, and 12.3%, respectively. Because of the rising of the maximum temperature, winter wheat yield decreased 6.4%, 6.8%, 7.2%, and −3.0%, respectively. On the other hand, the increase of the minimum temperature raised the winter wheat yield of 8.8%, 10.2%, 1.5%, and 12.0%, respectively. Climate change, either precipitation reduction or temperature increase, decreased soil water in the dry land winter wheat field. Therefore, more water-saving technologies are needed to adapt to climate change, to store and use water sources more effectively in semi-arid regions. Though precipitation reduction and maximum temperature increase produced negative impacts on winter wheat yield, the uptrend in minimum temperature is better for increasing the winter wheat yield, which can be used by farmers and governments to adapt to climate change, by adjusting planting time properly.

Relationships between the three components of air temperature and remotely sensed land surface temperature of agricultural areas in Morocco

ABSTRACTIn this article, correlations to derive minimum (Tmin), mean (Tmean), and maximum (Tmax) air temperatures from land surface temperature (Ts) in Morocco are studied. Land surface temperature was derived from 349 decadal cloud-free images acquired by the Advanced Very High Resolution Radiometer sensor, onboard the National Oceanic and Atmospheric Administration’s satellites. Ts was first compared with Tmin, Tmean, and Tmax, at the level of nine meteorological stations between 1995 and 2012. This first step showed the existence of good linear correlations between Ts and air temperatures. The coefficient of determination, R2, varied between 0.59 and 0.79 for Ts versus Tmin, between 0.60 and 0.76 for Ts versus Tmax, and between 0.67 and 0.79 for Ts versus Tmean. The root mean square error (RMSE) varied between 2.4°C and 3.9°C for Ts versus Tmin, between 2.5°C and 4.6°C for Ts versus Tmax, and between 2.2°C and 3.8°C for Ts versus Tmean. The mean absolute error (MAE) varied between 2°C and 3.1°C for Ts versus Tmin, between 1.9°C and 3.6°C for Ts versus Tmax, and between 1.8°C and 3°C for Ts versus Tmean. Second, Ts was compared with Tmin, Tmax, and Tmean gridded at the level of agricultural areas of 46 provinces in Morocco. For 42 provinces, the mean value of R2 was 0.71 for both Tmin versus Ts and Tmax versus Ts, and 0.76 for Tmean versus Ts. The mean values of the RMSE and the MAE were 3.1°C and 2.4°C for Ts versus Tmin, 3.6°C and 2.8°C for Tmax versus Ts, and 3°C and 2.3°C for Tmean versus Ts, respectively. Finally, the accuracy of the regression models between air and surface temperatures was tested using a k-fold cross-validation method that showed high stability of these models. The relationships obtained in this work could be very useful for further monitoring and modelling agriculture and meteorological parameters.

Prediction of 13C NMR chemical shifts by artificial neural network. I. Partial charge model as atomic descriptor

Mulliken population analysis (MPA), Hirshfeld population analysis (HPA), Charge Model 5 (CM5) and Hu Lu Yang charge fitting method (HLY) were considered in order to reveal influence of atomic partial charges on the 13C NMR chemical shifts. The test set included seven classes of organic molecules. Partial charges of carbon atoms were obtained from quantum-chemical calculations at DFT/HISS level. Linear regressions were constructed as estimators of accuracy of each model. The best approach was shown by multivariate regression with MPA, HPA, and CM5 charges as predictors in a linear model with mean value of R2=0.8917.

Linkage Disequilibrium Estimation of Chinese Beef Simmental Cattle Using High-density SNP Panels.

Linkage disequilibrium (LD) plays an important role in genomic selection and mapping quantitative trait loci (QTL). In this study, the pattern of LD and effective population size (Ne) were investigated in Chinese beef Simmental cattle. A total of 640 bulls were genotyped with IlluminaBovinSNP50BeadChip and IlluminaBovinHDBeadChip. We estimated LD for each autosomal chromosome at the distance between two random SNPs of <0 to 25 kb, 25 to 50 kb, 50 to 100 kb, 100 to 500 kb, 0.5 to 1 Mb, 1 to 5 Mb and 5 to 10 Mb. The mean values of r2 were 0.30, 0.16 and 0.08, when the separation between SNPs ranged from 0 to 25 kb to 50 to 100 kb and then to 0.5 to 1 Mb, respectively. The LD estimates decreased as the distance increased in SNP pairs, and increased with the increase of minor allelic frequency (MAF) and with the decrease of sample sizes. Estimates of effective population size for Chinese beef Simmental cattle decreased in the past generations and Ne was 73 at five generations ago.

Hydrogenic Impurity States in a Quantum Pseudodot: Spin-Orbit Interaction, Relativistic Correction, and Diamagnetic Susceptibility

We have studied three characteristics of a hydrogenic impurity located in the center of a quantum pseudodot within the effective mass approximation. These characteristics are the diamagnetic susceptibility, the spin-orbit interaction (SOI), and the relativistic correction (RC). First, we have solved analytically the Shrodinger equation without impurity by using the Laplace transformation. Then, we have applied the variational procedure to obtain energy levels and wave functions with a hydrogenic impurity in the center of quantum pseudodot. According to the results obtained from the present work reveals that (i) the diamagnetic susceptibility increases with increasing the pseudodot size, (ii) The mean value of r 2 increases when the pseudodot size increases, (iii) the SOI and RC increase by increasing the potential V 0, (iv) the RC and SOI approach to zero when the pseudodot increases, (v) the splitting between j=1/2 and 3/2 due to the SOI decreases by increasing pseudodot size.

BOLD imaging: a potential predictive biomarker of renal functional outcome following revascularization in atheromatous renovascular disease

Stenting of the stenosed renal artery is commonly employed in atheromatous renovascular disease (ARVD) in order to revascularize the affected kidney. However, it is still far from clear which patient subgroups should be revascularized as stenting carries small but significant risks. We have previously demonstrated that the ratio of magnetic resonance-measured renal volume to isotopic single kidney glomerular filtration rate (isoSK-GFR) is higher in kidneys which show functional improvement after revascularization. Blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) does not require contrast administration and is sensitive to changes in tissue concentration of deoxyhaemoglobin. In this study, we test the hypothesis that baseline BOLD R2* map signal and R2*:isoSK-GFR ratio will provide an additional independent predictive biomarker of response to revascularization. Studies were performed in 28 subjects (16 ARVD and 12 controls). All subjects had R2* mapping and isoSK-GFR measured at baseline and at 4-month follow-up. MRI data were collected on a 3 T whole-body MRI scanner using a coronal dual-echo, 2D gradient-echo breath-hold acquisition. Parenchymal regions of interest (ROIs) were drawn on a representative slice through the middle of the kidney. Parametric maps of R2* were generated and mean values of R2* were calculated for every ROI. The ratio of R2*:isoSK-GFR at baseline was significantly greater in kidneys where renal function improved (5.91 ± 6.51) versus stable (1.78 ± 1.11), deteriorated (2.15 ± 1.79) or controls (1.5 ± 0.91), P = 0.003. R2*:isoSK-GFR ratio that was greater than 95% confidence interval of the control kidneys was 66.7% sensitive, but 85.7% specific in predicting a positive renal functional outcome. These pilot data show that BOLD R2* imaging, presumably by detecting intra-renal deoxyhaemoglobin in still viable 'hibernating' parenchyma, coupled with isoSK-GFR may provide an effective predictive biomarker for positive renal functional response to revascularization. R2* imaging is non-invasive, quick to perform and could provide further insight into reversible parenchymal changes in ARVD kidneys.

Quantitative HPTLC analysis of palmitoyl hexapeptide

A sensitive and precise quantitative densitometric HPTLC method has been established for analysis of palmitoyl hexapeptide both as the bulk drug and in formulations,. Silica gel 60 F254 plates were used with toluene-ethanol 9:1 as mobile phase. This system gave compact spots for palmitoyl hexapeptide (RF 0.33 ± 0.02). Densitometric quantification was performed in absorbance mode at 221 nm and well resolved bands were obtained. Regression analysis of the calibration data revealed a good linear relationship (mean value of r2 = 0.99910 ± 0.003) between response and amount of palmitoyl hexapeptide in the range 10–30 ng per band. The mean slope and intercept were 114.5 ± 0.002 and 311.1 ± 1.32, respectively. The method was validated for precision, recovery, robustness, ruggedness, and specificity. The limits of detection and quantification were 3 and 9 ng per band, respectively. The method is selective and specific with potential application in cosmetics and pharmaceutical analysis.

Statistical Reconstruction of the Antarctic Oscillation Index Based on Multiple Proxies

AbstractBased on multiple proxies from the Southern Hemisphere, an austral summer (December-January-February: DJF) Antarctic Oscillation Index (AAO) since 1500 A.D. was reconstructed with a focus on interannual to interdecadal variability (<50 a). By applying a multivariate regression method, the observational AAO-proxy relations were calibrated and cross-validated for the period of 1957–89. The regressions were employed to compute the DJF-AAO index for 1500–1956. To verify the results, the authors checked the explained variance (r2), the reduction of error (RE), and the standard error (SE). Cross-validation was performed by applying a leave-one-out validation method. Over the entire reconstruction period, the mean values of r2, RE, and SE are 59.9%, 0.47, and 0.67, respectively. These statistics indicate that the DJF-AAO reconstruction is relatively skillful and reliable for the last ∼460 years. The reconstructed AAO variations on the interannual and interdecadal time-scales compare favorably with those ...

Efficacy of Dual Lumen Jugular Venous Catheter Hemodialysis when Venous Lumen Is Used as Arterial Lumen

In 12 patients who underwent hemodialysis by a dual lumen jugular venous catheter, urea recirculation rate was measured during conventional treatment under normal conditions (R1), as well as when the venous lumen of the catheter was used as arterial lumen and the arterial as venous lumen (R2). Although the mean value of R2 was, as expected, higher than R1 (8.25 +/- 2.7 versus 4.25 +/- 1.00; p = 0.0004), it was still within the acceptable rate. We conclude that during hemodialysis by a dual lumen jugular venous catheter, efficient treatment can be provided when the venous is used as arterial lumen.