Evaluation of lipophilicity and drug-likeness of donepezil-like compounds using reversed-phase thin-layer chromatography.

Fourteen donepezil-like acetylcholinesterase (AChE) inhibitors from our library were analyzed using reversed-phase thin-layer chromatography to assess their lipophilicity and blood-brain barrier permeability. Compounds possessed N-benzylpiperidine and N,N-diarylpiperazine moieties connected via a short carboxamide or amine linker. Retention parameters RM 0, b, and C0 were considered as the measures of lipophilicity. Besides, logD of the investigated compounds was determined chromatographically using standard compounds with known logPow and logD values at pH 11. Experimentally obtained lipophilicity parameters correlated well with in silico generated results, and the effect of the nature of the linker between two pharmacophores and substituents on the arylpiperazine part of the molecule was observed. As a result of drug-likeness analysis, both Lipinski's rule of five and Veber's rule parameters were determined, suggesting that examined compounds could be potential candidates for further drug development. Principal component analysis was performed to obtain an insight into a grouping of compounds based on calculated structural descriptors, experimentally obtained values of lipophilicity, and AChE inhibitory activity.

Screening Iranian endemic coriander genebank germplasm for drought tolerance using a new multivariate statistical method.

Drought is one of the serious abiotic factors influencing crop production such as coriander. Development of tolerant genotypes is prevented by the lack of effective selection criterion. Objectives of this study were evaluation of coriander accessions for water deficit stress and introduce a new multivariate method to select drought tolerant genotypes. For investigation of 19 traits, 16 Iranian endemic coriander genotypes were grown in a glasshouse under control and water deficit stress conditions. Shoot dry weight (SDW), fruit weight per plant (FWPP), fruit number per plant (FNPP) and umbel number per plant (UNPP) were decreased (Susceptibility Index>38%) under water deficit stress condition compared with the control condition. While the mean values of root dry weight (RDW) and root to shoot ratio (RTSR) were increased 1.49% and 97.33% under water stress condition, respectively. Because of high inheritance, high expected genetic gain, high genotypic correlation with together, well response to drought stress and high explanation of FWPP variation in regression model, the FWPP, branch number per plant (BNPP), FNPP and SPAD chlorophyll content in grain filing stage (SCCIGFS) traits were selected to screen coriander genotypes for drought tolerance in coriander. The principal component analysis mediated method (PCAMM) indicated as comprehensive criterion to screen drought tolerant genotypes. This method was highly heritabl, able to separate the Fernandez described A, B, C and D groups, no multicollinear and using multiple drought tolerance related traits. The PCAMM results showed that G13, G16, G2 and G12 genotypes belonged to Fernandez described A, B, C and D groups, respectively.

Evaluating the adoption of irrigation technology in a well-irrigated winter wheat－summer maize cropping system.

Determining suitable irrigation technology is of paramount for promoting water-saving agriculture, particularly for winter wheat-summer maize rotation system in well-irrigated regions. To optimize and assess the efficacy of various irrigation technologies (specifically, semi-fixed sprinkler irrigation, walking sprinkler, semi-automatic buried telescopic sprinkler irrigation, thin-soft spray tape irrigation, drip irrigation, self-driven winch sprinkler and manually moving spray gun irrigation, marked as A, B, C, D, E, F and G) applied in south central North China Plain, we first conducted an economic analysis for the winter wheat-summer maize rotation. Subsequently, employing a comprehensive set of 20 indicators spanning economic, societal, technological, ecological, and resource aspects, we employed a TOPSIS model with integrative weighting approach using "AHP + Entropy". We also employed principal component analysis and the Sankey diagram method to explore characteristics of different irrigation techniques and indexes. Irrigation mode E, conserving energy by 63.19% compared to mode B and offering labor savings five times greater than the mode D. The highest economic benefit for the rotation system was observed with the mode C, resulting in a 25.26% increase compared to the mode G. The top three irrigation modes based on scores were D, G, and E, with scores of 0.532, 0.490, and 0.474, respectively. The Sankey diagram revealed distinct preferences among different agricultural entities for specific irrigation modes. For specific stakeholders, we recommend irrigation modes D, G, F, and B for small farmers, large and specialized family businesses, family farms, and farmer cooperatives, respectively. In conclusion, our findings provide valuable scientific support and recommendations for the practical application of irrigation technology in agricultural production.

Applicability of Brassica juncea as a bioindicator for As contamination in soil near the abandoned mine area.

Soil monitoring in abandoned mine areas is important from the perspective of ecological and human health risk. Arsenic (As) is a predominant metalloid contaminant in abandoned mine area and its behavior has been influenced by various soil characteristics. Bioindicator can be a useful tool in terms of testing the extent to which they are uptaken by plants bioavailability. Eighteen soils near the mine tailings dam were collected to investigate the effect of As contamination on As absorption by Brassica juncea. The pH range of the experimental soils was between 4.90 and 8.55, and the total As concentrations were between 34 mg kg-1 and 3017 mg kg-1. The bioavailability of As was evaluated by Olsen method, and B. juncea was cultivated in eighteen soils for 3 weeks. Principal component analysis, correlation, and multiple regression analysis were performed to estimate a significant factor affecting As uptake by B. juncea. All statistical results indicated that As bioavailability in soil is the main factor affecting As uptake in root and shoot of B. juncea. Although translocation process, the amount of As in shoot was exponentially explained by As bioavailability in soil. This result suggests that the contamination and bioavailability of As can be confirmed only by analyzing the shoot of B. juncea, which is be easily found in environmental ecosystem, and implies the applicability of B. juncea as a bioindicator for the monitoring of As contamination and its behavior in soil ecosystem.

Assessment of the adaptive capacity of Morada Nova ewes with different coat coloration.

Coat color is a factor affecting heat tolerance in tropical ruminant and a particular coat color can determine which is more resilient to environmental changes. The aim of this study was to measure the level of adaptation of Morada Nova sheep with different coat color by using an Adaptability Index (AI). Adult ewes were used, including two different coat colors of Morada Nova sheep (red and white) with mean of body weight of 28.02 ± 5.70 kg and 31.47 ± 3.41 kg, respectively. Physiology parameters, hematology, electrolytes, acid-base status, mineral, renal functions, metabolites, enzymes, and proteins were measured. AI was designed using a multivariate approach (principal component analysis) to "weigh" the influence of each variable in the animal responses. The variables more important for adaptive aspects of Red Morada Nova were: haematology, electrolytes and acid-base status. The hemoglobin (HG), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), sodium (Na+), oxygen pressure (PO2), glucose (GLU) and albumin (ALB) were significantly higher in Red Morada Nova sheep and hydrogen carbonate (HCO3), base excess (BE), total carbon dioxide concentration (TCO2) and URE were significantly higher in the white phenotype. The variables more important for adaptive aspects of White Morada Nova sheep were: (K+), total protein (TP), PO2, HG, cholesterol (CHO), rectal temperature (RT) and glucose (GLU). Both phenotypes showed a high adaptation level, however, a higher value was generated for the Red Morada Nova sheep (81.97). This study concludes that both phenotypes of the Morada Nova sheep breed are well adapted to the climatic condition of the Brazilian tropical region using different adaptive mechanisms.

Neuronal population representation of human emotional memory.

Understanding how emotional processing modulates learning and memory is crucial for the treatment of neuropsychiatric disorders characterized by emotional memory dysfunction. We investigate how human medial temporal lobe (MTL) neurons support emotional memory by recording spiking activity from the hippocampus, amygdala, and entorhinal cortex during encoding and recognition sessions of an emotional memory task in patients with pharmaco-resistant epilepsy. Our findings reveal distinct representations for both remembered compared to forgotten and emotional compared to neutral scenes in single units and MTL population spiking activity. Additionally, we demonstrate that a distributed network of human MTL neurons exhibiting mixed selectivity on a single-unit level collectively processes emotion and memory as a network, with a small percentage of neurons responding conjointly to emotion and memory. Analyzing spiking activity enables a detailed understanding of the neurophysiological mechanisms underlying emotional memory and could provide insights into how emotion alters memory during healthy and maladaptive learning.

Identification of spermatogenesis in individual seminiferous tubules and testicular tissue of adult normal and busulfan-treated mice employing Raman spectroscopy and principal component analysis.

The present study aims to identify spermatogenesis in testicular seminiferous tubules (ST) and testicular tissue of adult normal and busulfan-treated mice utilizing PCA and Raman spectroscopy. Raman measurements were conducted on single tubules and testes samples from adult and immature mice, comparing them with those from busulfan-treated adult mice, with validation through histological examination. The analysis revealed a higher signal variability (30 %-40 % at the peaks), prompting scrutiny of individual Raman spectra as a means of spermatogenesis measurement. However, principal component analysis (PCA) demonstrated significant cluster separation between the ST of mature and immature mice. Similar investigations were performed to compare ST from normal mature mice and those from busulfan-treated (BS-treated) mature mice, revealing substantial separation along PC1 and PC2 for all comparison sets. Additionally, comparing testicular samples from mature and immature mice revealed distinct separation in PCA. The study concludes that the combined approach of PCA and Raman spectroscopy proves to be a noninvasive and potentially valuable method for identifying spermatogenesis in seminiferous tubules and testicular samples.

An introductory review on the application of principal component analysis in the data exploration of the chemical analysis of food samples.

Principal component analysis (PCA) is currently one of the most used multivariate data analysis techniques for evaluating information from food analysis. In this review, a brief introduction to the theoretical principles that underlie PCA will be given, in addition to presenting the most commonly used computer programs. An example from the literature was discussed to illustrate the use of this chemometric tool and interpretation of graphs and parameters obtained. A list of recently published articles will also be presented, in order to show the applicability and potential of the technique in the food analysis field.

Grain nutritional and antioxidant profiling of diverse lentil (Lens culinaris Medikus) genetic resources revealed genotypes with high nutritional value.

The lentil (Lens culinaris Medikus ssp. Culinaris) is a self-pollinating, diploid (2n = 2X = 14) crop with a genome size of 4 Gbp. The present study was conducted to provide a database for the evaluation of lentil antioxidant capacity, nutritional quality, and biochemical attributes. For these purposes, lentil germplasm, including 100 exotic and local genotypes from different agro-climatic zones of Pakistan, was collected. Significant variation (p < 0.05) was found among the genotypes under investigation using the Tukey HSD test. Ascorbate peroxidase was highest in ALTINOPARK (2,465 Units/g s. wt.), catalase in LPP 12110 (5,595 Units/g s. wt.), superoxide dismutase in LPP 12105 (296.75 Units/g s. wt.), and peroxidase in NIAB Masoor 2002 (3,170 Units/g s. wt.). Furthermore, NLM 15016 had a maximum total antioxidant capacity of 15.763 mg/g s. wt. The maximum values of total soluble sugars (83.93 mg/g. s. wt.) and non-reducing sugars (74.79 mg/g. s. wt.) were noticed in NLM 15015. The highest reducing sugars were detected in ILL 8006 (45.68 mg/g. s. wt.) ascorbic acid in LPP 12182 (706 µg/g s. wt.), total phenolic content in NLI 17003 (54,600 µM/g s. wt.), and tannins in NLI 17057 (24,563 µM/g s. wt.). The highest chlorophyll a (236.12 µg/g s. wt.), chlorophyll b (317 µg/g s. wt.), total chlorophyll (552.58 µg/g s. wt.), and lycopene (10.881 µg/g s. wt.) were found in NLH 12097. Maximum total carotenoids were revealed in the local approved variety Markaz 2009 (17.89 µg/g s. wt.). Principal component analysis (PCA), correlation analysis (Pearson's test), and agglomerative hierarchical clustering (AHC) were performed to detect the extent of variation in genotypes. In cluster analysis, all genotypes were categorized into three clusters. Cluster II genotypes showed remarkable divergence with cluster III. According to PCA, the contribution of PC-I regarding tested nutritional parameters toward variability was the highest (39.75%) and indicated positive factor loading for the tested nutritional and biochemical parameters. In conclusion, genotype X 2011S 33-34-32 can be used by the food industry in making pasta, multigrain bread, and snacking foods due to its high protein content for meat alternative seekers. Identified genotypes with high nutritional attributes can be utilized to improve quality parameters in the respective lentil breeding lines.

Physicochemical properties of starches from sweet potato root tubers grown in natural high and low temperature soils.

Three sweet potato varieties grew in natural high temperature (HT) and low temperature (LT) field soils. Their starch physicochemical properties were affected similarly by HT and LT soils. Compared with LT soil, HT soil induced the increases of granule size D[4,3] from 18.0-18.7 to 19.9-21.8 µm and amylopectin average branch-chain length from 21.9-23.1 to 24.1-24.7 DP. Starches from root tubers grown in HT and LT soils exhibited CA- and CC-type XRD pattern, respectively. Starches from root tubers grown in HT soil exhibited stronger lamellar peak intensities (366.8-432.0) and higher gelatinization peak temperature (72.0-76.8 °C) than those (176.2-260.5, 56.4-63.4 °C) in LT soil. Native starches from root tubers grown in LT soil were hydrolyzed more easily (hydrolysis rate coefficient 0.227-0.282 h-1) by amylase than those (0.120-0.163 h-1) in HT soil. The principal component analysis exhibited that starches from root tubers grown in HT and LT soils had significantly different physicochemical properties.

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit.

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.

Physicochemical and functional properties of short-chain fatty acid starch modified with different acyl groups and levels of modification.

Rice and quinoa starches are modified with short-chain fatty acids (SCFA) with different SCFA acyl chain lengths and levels of modification. This work is aimed to investigate the impact of modifying rice and quinoa starches with short-chain fatty acids (SCFAs) on various physicochemical properties, including particle size, protein and amylose content, thermal behavior, pasting characteristics, and in vitro digestibility. Both native and SCFA-starches showed comparable particle sizes, with rice starches ranging from 1.58 to 2.22 µm and quinoa starches from 5.18 to 5.72 µm. SCFA modification led to lower protein content in both rice (0.218-0.255 %) and quinoa starches (0.537-0.619 %) compared to their native counterparts. Esterification led to the reduction of gelatinization and pasting temperatures as well as the hardness of the paste of SCFA-starches were reduced while paste clarity increased. The highest level of modification in SCFA-starch was associated with the highest amount of resistant starch fraction. Principal component analysis revealed that modification levels exerted a greater influence on starch properties than the types of SCFA used (acetyl, propionyl, and butyryl). These findings is importance in considering the degree of substitution or level of modification when tailoring starch properties through SCFA modification, with implications for various applications in food applications.

Clusterization of Behavioral and Psychological Symptoms of Dementia as Assessed by Neuropsychiatric Inventory: A Case Against the Use of Principal Component Analysis.

Background: The term Behavioral and Psychological Symptoms of Dementia (BPSD) covers a group of phenomenologically and medically distinct symptoms that rarely occur in isolation. Their therapy represents a major unmet medical need across dementias of different types, including Alzheimer's disease. Understanding of the symptom occurrence and their clusterization can inform clinical drug development and use of existing and future BPSD treatments. Objective: The primary aim of the present study was to investigate the ability of a commonly used principal component analysis to identify BPSD patterns as assessed by Neuropsychiatric Inventory (NPI). Methods: NPI scores from the Aging, Demographics, and Memory Study (ADAMS) were used to characterize reported occurrence of individual symptoms and their combinations. Based on this information, we have designed and conducted a simulation experiment to compare Principal Component analysis (PCA) and zero-inflated PCA (ZI PCA) by their ability to reveal true symptom associations. Results: Exploratory analysis of the ADAMS database revealed overlapping multivariate distributions of NPI symptom scores. Simulation experiments have indicated that PCA and ZI PCA cannot handle data with multiple overlapping patterns. Although the principal component analysis approach is commonly applied to NPI scores, it is at risk to reveal BPSD clusters that are a statistical phenomenon rather than symptom associations occurring in clinical practice. Conclusions: We recommend the thorough characterization of multivariate distributions before subjecting any dataset to Principal Component Analysis.

Analysis of intra-specific variations in the venom of individual snakes based on Raman spectroscopy.

The knowledge of variations in the composition of venoms from different snakes is important from both theoretical and practical points of view, in particular, at developing and selecting an antivenom. Many studies on this topic are conducted with pooled venoms, while the existence and significance of variations in the composition of venoms between individual snakes of the same species are emphasized by many authors. It is important to study both inter- and intra-specific, including intra-population, venom variations, because intra-specific variations in the venom composition may affect the effectiveness of antivenoms as strongly as inter-specific. In this work, based on venom Raman spectroscopy with principal component analysis, we assessed the variations in venoms of individual snakes of the Vipera nikolskii species from two populations and compared these intra-specific variations with inter-specific variations (with regard to the other related species). We demonstrated intra-specific (inter- and intra-population) differences in venom compositions which are smaller than inter-specific variations. We also assessed the compositions of V. nikolskii venoms from two populations to explain inter-population differences. The method used is rapid and requires virtually no preparation of samples, used in extremely small quantities, allowing the venoms of individual snakes to be analyzed. In addition, the method is informative and capable of detecting fairly subtle differences in the composition of venoms.

Fluorescent multichannel sensor array based on three carbon dots derived from Tibetan medicine waste for the quantification and discrimination of multiple heavy metal ions in water.

A fluorescent multichannel sensor array has been established based on three carbon dots derived from Tibetan medicine waste for rapid quantification and discrimination of six heavy metal ions. Due to the chelation between metal ions and carbon dots (CDs), this fluorescence "turn off" mode sensing array can quantify six metal ions as low as "µM" level. Moreover, the six heavy metal ions display varying quenching effects on these three CDs owing to diverse chelating abilities between each other, producing differential fluorescent signals for three sensing channels, which can be plotted as specific fingerprints and converted into intuitive identification profiles via principal component analysis (PCA) and hierarchical cluster analysis (HCA) technologies to accurately distinguish Cu2+, Fe3+, Mn2+, Ag+, Ce4+, and Ni2+ with the minimum differentiated concentration of 5 µM. Valuably, this sensing array unveils good sensitivity, exceptional selectivity, ideal stability, and excellent anti-interference ability for both mixed standards and actual samples. Our contribution provides a novel approach for simultaneous determination of multiple heavy metal ions in environmental samples, and it will inspire the development of other advanced optical sensing array for simultaneous quantification and discrimination of multiple targets.

Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks.

Soil erodibility (K) is an essential component in estimating soil loss indicating the soil's susceptibility to detach and transport. Data Computing and processing methods, such as artificial neural networks (ANNs) and multiple linear regression (MLR), have proven to be helpful in the development of predictive models for natural hazards. The present case study aims to assess the efficiency of MLR and ANN models to forecast soil erodibility in Peninsular Malaysia. A total of 103 samples were collected from various sites and K values were calculated using the Tew equation developed for Malaysian soil. From several extracted parameters, the outcomes of correlation and principal component analysis (PCA) revealed the influencing factors to be used in the development of ANN and MLR models. Based on the correlation and PCA results, two sets of influencing factors were employed to develop predictive models. Two MLR (MLR-1 and MLR-2) models and four neural networks (NN-1, NN-2, NN-3, and NN-4) optimized using Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) were developed and evaluated. The model performance validation was conducted using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE). The analysis showed that ANN models outperformed MLR models. The R2 values of 0.446 (MLR-1), 0.430 (MLR-2), 0.894 (NN-1), 0.855 (NN-2), 0.940 (NN-3), and 0.826 (NN-4); MSE values of 0.0000306 (MLR-1), 0.0000315 (MLR-2), 0.0000158 (NN-1), 0.0000261 (NN-2), 0.0000318 (NN-3), and 0.0000216 (NN-4) suggested the higher accuracy and lower modelling error of ANN models as compared with MLR. This study could provide an empirical basis and methodological support for K factor estimation in the region.

Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation.

Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.

Yield, Quality, and Nitrogen Leaching of Open-Field Tomato in Response to Different Nitrogen Application Measures in Northwestern China.

The overuse of fertilizers in open-field tomato leads to soil deterioration through nutrient leaching and increases the risk of agricultural non-point source contamination. Currently, the combined effects of different fertilization methods on soil nitrogen leaching and tomato production are still unclear. Therefore, the most effective fertilization method for open-field tomato should be discovered by examining how different fertilization methods affected tomato yield and quality, nitrogen use efficiency (NUE), and soil nitrogen leaching. Compared with CK (no fertilization), fertilization significantly increased the yield, total sugar (TS), total soluble solids (TSS), and vitamin C (vC) contents of fruits (p < 0.05), and OPT (optimal fertilization, controlled release nitrogen application, 240 kg ha-1) had the largest effect on increasing yield, quality, and net profit. However, when the fertilizer application rate reached 375 kg ha-1, these indices decreased. Nitrogen leaching concentrations, leaching amount, and titratable acids (TAs) increased with increased nitrogen application rates. Compared with other treatments, OPT reduced the total leaching amounts of total nitrogen (TN), nitrate nitrogen (NO3--N), and ammonia nitrogen (NH4+-N) by 30.09-51.79%, 24.89-50.03%, and 30-65%, respectively. Principal component analysis (PCA) showed that OPT achieved the highest overall score in terms of yield, quality, and nitrogen leaching conditions. The partial least squares path modeling (PLS-PM) further reveals that applications of high amounts of nitorigen have a positive effect on soil nitrogen leaching. The amount of nitrogen leaching vegetatively affects tomato yield and quality, while plant uptake of nitrogen positively affects tomato production. These findings confirm the importance of using controlled-release fertilizers and reducing nitrogen inputs to control nitrogen leaching and enhance open-field tomato yields.

Screening of Rice (Oryza sativa L.) Genotypes for Salinity Tolerance and Dissecting Determinants of Tolerance Mechanism.

Soil salinity imposes osmotic, ionic, and oxidative stresses on plants, resulting in growth inhibition, developmental changes, metabolic adaptations, and ion sequestration or exclusion. Identifying salinity-tolerant resources and understanding physiological and molecular mechanisms of salinity tolerance could lay a foundation for the improvement of salinity tolerance in rice. In this study, a series of salinity-tolerance-related morphological and physiological traits were investigated in 46 rice genotypes, including Sea Rice 86, to reveal the main strategies of rice in responding to salinity stress at the seedling stage. No genotypes showed the same tolerance level as the two landraces Pokkali and Nona Bokra, which remain the donors for improving the salinity tolerance of rice. However, due to undesirable agronomic traits of these donors, alternative cultivars such as JC118S and R1 are recommended as novel source of salinity tolerance. Correlation and principal component analyses revealed that the salinity tolerance of rice seedlings is not only controlled by growth vigor but also regulated by ion transport pathways such as long-distance Na+ transport, root Na+ sequestration, and root K+ retention. Therefore, such key traits should be targeted in future breeding programs as the strategy of obtaining better Na+ exclusion is still the bottleneck for improving salinity tolerance in rice.

Egg vs. Oil in the Cookbook of Plasters: Differentiation of Lipid Binders in Wall Paintings Using Gas Chromatography-Mass Spectrometry and Principal Component Analysis.

Wall paintings are integral to cultural heritage and offer rich insights into historical and religious beliefs. There exist various wall painting techniques that pose challenges in binder and pigment identification, especially in the case of egg/oil-based binders. GC-MS identification of lipidic binders relies routinely on parameters like the ratios of fatty acids within the plaster. However, the reliability of these ratios for binder identification is severely limited, as demonstrated in this manuscript. Therefore, a more reliable tool for effective differentiation between egg and oil binders based on a combination of diagnostic values, specific markers (cholesterol oxidation products), and PCA is presented in this study. Reference samples of wall paintings with egg and linseed oil binders with six different pigments were subjected to modern artificial ageing methods and subsequently analysed using two GC-MS instruments. A statistically significant difference (at a 95% confidence level) between the egg and oil binders and between the results from two GC-MS instruments was observed. These discrepancies between the results from the two GC-MS instruments are likely attributed to the heterogeneity of the samples with egg and oil binders. This study highlights the complexities in identifying wall painting binders and the need for innovative and revised analytical methods in conservation efforts.