Multivariate Analysis Methods Research Articles

Distribution and morphological variation of tree ferns (Cyatheaceae) along an elevation gradient.

Knowing how species and communities respond to environmental change is fundamental in the context of climate change. The search for patterns of abundance and phenotypic variation along altitudinal gradients can provide evidence on adaptive limits. We evaluated the species abundance and the variation in morphometric and stomatal characters in five tree ferns species (Cyathea fulva, C. divergens, C. myosuroides, Alsophila firma and Gymnosphaera salvinii) distributed along an elevation gradient in a well-preserved Mexican cloud forest. Variation at the community and species level was assessed using exploratory and multivariate data analysis methods. We wanted to explore if the species abundance is environmentally determined, to determine the degree of variation along the elevation gradient, to test for differences between zones and associations with elevation, humidity and soil nutrients, and to assess contribution of the intra- and interspecific variation to the community response to elevation and soil nutrients. The studied fern community showed strong species turnover along the elevation gradient, with some influence of soil nutrient concentration, supporting environmental determinism. All measured characters displayed variation along the gradient. Stomatal characters (size and density) had significantly less variation than morphometric characters (trunk diameter, stipe length and blade length), but stomatal density also shows interesting intraspecific patterns. In general, patterns within the fern community suggest a strong influence of species identity, especially of species inhabiting the lower edge of the cloud forest, which showed the clearest morphometric and stomatal patterns, associated to contrasting environments rather than to changes in elevation. The coincidence between morphometric and stomatal patterns in this area suggest hydraulic adjustments in response to contrasting environments. Our results provide evidence that tree ferns species respond to environmental changes through adjustments of morphometric plasticity and stomatal density, which is relevant to predict possible responses to variation in environmental conditions resulting from climate change.

Service Quality Factors Affecting Interest in Repeat Outpatient Patients

Patient satisfaction levels are a good sign of how well a hospital provides services. The likelihood that a patient will return increases if they are satisfied with the hospital's services, which obviously benefits the facility. In this study, Royal Prima Medan outpatients' intentions to return to RSU were examined in relation to several service quality metrics. A cross-sectional design was used for this analytical survey research study. The study's population consisted of 4,011 average outpatients at the Pulmonary and Cardiac Polyclinic from July to August 2022. A sample of 98 respondents using an unintended sampling strategy was drawn from this cohort. Methods for univariate, bivariate, and multivariate data analysis were used in this study. The findings showed that the intention to return to the hospitalized patients road was influenced by the variable quality of infrastructure (sig-p = 0.004 0.05), the variable quality of human resources (sig-p = 0.025 0.05), and the variable quality of security (sig-p = 0.015 0.05), whereas the personal quality variables (sig-p = 0.054 > 0.05, gender = 0.253 > 0.05, education = 0.322 > 0.05, and occupation) were not.. According to the study's conclusions, it is thought that providing health staff training, reducing patient costs for less fortunate patients, and creating an organizational structure focused n duties and education will all enhance the hospital.

Adjuvant Hyperthermic Intravesical Chemotherapy in Intermediate- and High-Risk Non-muscle Invasive Bladder Cancer.

Non-muscle invasive bladder cancer (NMIBC) is a frequently diagnosed neoplasm, which is typically managed with transurethral resection of bladder tumor (TURBT) eventually followed by intravesical therapies. Bacillus Calmette-Guérin (BCG) is used as first-line adjuvant treatment in high- (HR) and intermediate-risk (IR) NMIBC, although, in the latter, mitomycin C (MMC) may also be used. Multiple limitations to the use of BCG encouraged the search for therapeutic alternatives. In this context, hyperthermic intravesical chemotherapy with MMC (HIVEC-MMC) emerged as a promising therapy in the adjuvant setting for NMIBC. The aim of our study was to evaluate the tolerability, compliance, and survival outcomes of HIVEC-MMC in patients with IR- and HR-NMIBC. This was a single-center retrospective analysis of IR- and HR- NMIBC patients who received HIVEC-MMC after TURBT between August 2018 and August 2022. Levels of risk stratification were defined using the European Association of Urology (EAU) criteria. The protocol consisted of four weekly HIVEC-MMC instillations (induction) followed by six monthly instillations (maintenance). The primary outcomes were to evaluate the tolerability and compliance with the HIVEC-MMC protocol and secondary outcomes were disease-free survival (DFS) and overall survival (OS). For the purpose of statistical analysis, methods of descriptive statistics, survival analysis (Kaplan-Meier estimation), and multivariate analysis (Cox regression, and binary logistic regression) were used. Fifty-seven patients were enrolled with a median age of 67.9 (34.4-83.5) years old. In this cohort, 40 patients (70.2%) had primary tumors. At the time of referral for HIVEC-MMC, the majority of the patients had IR-NMIBC (n= 33, 57.9%). A total of 41 patients (71.9%)completed the HIVEC-MMC protocol. Disease recurrence and adverse events (AEs) were the most common reasons to stop the protocol. After a median follow-up of 31 months (95% CI, 5.0-54.0), 32 patients (61.4%) were disease-free, 22 (38.6%) experienced recurrent disease and six patients (10.5%) died, although only one death was directly attributable to bladder cancer. The median DFS was 42 months (95% CI, 28.0-56.0). Completion of the HIVEC-MMC maintenance phase protocol stood as a predictive factor for DFS (44 months, 95% CI 29.1-58.9 vs. 14 months, 95% CI 0.0-29.6, p < 0.001; HR 4.48, 95% CI 1.65-12.15). The median OS was not reached; the 24- and 48-monthOS were 92.6% and 82.7%, respectively. EAU risk group, ECOG-PS, and completion of HIVEC protocol were found to be significant predictive factors of OS but lost their significance on multivariate analysis. However, if we exclude those who experienced recurrence during the maintenance phase protocol, treatment completion had a significant positive impact on OS (HR: 42.8, 95% CI 1.75-1045.072, p= 0.021). Our study suggests that HIVEC is a secure and well-tolerated treatment with promising efficacy data, making this therapeutic approach a feasible option in IR- and HR-NMIBC patients, mainly in those who cannot tolerate or have contraindications to BCG therapy, but also as an alternative during BCG shortages.

The Impact of The Development of Conversational AI on The Stock Indexes of Relevant Industries in The Chinese Stock Market

In recent years, the AI industry has been booming and has been favored by many investors. In 2023, generative AI has received a lot of attention due to the release of chat GPT, which has changed people's lives while also having a lot of impact on the stock market, In this paper, we will use the time series model and multivariate statistical analysis method to measure the impact on chat GPT on the stock price of related industries.

A Multivariate Method for Dynamic System Analysis: Multivariate Detrended Fluctuation Analysis Using Generalized Variance.

Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.

Comparative analysis of bile metabolic profile in patients with biliary obstruction complicated by Clonorchis sinensis infection.

Clonorchiasis is an important foodborne parasitic disease. However, eggs of Clonorchis sinensis (C. sinensis) cannot be detected in feces during biliary obstruction. Moreover, many diseases can cause biliary obstruction, such as gallstones, adenocarcinoma, cholangiocarcinoma and Ascaris lumbricoides infection. Therefore, it is of great significance to distinguish between patients of biliary obstruction and biliary obstruction with C. sinensis infection. A total of 48 biliary obstruction patients were enrolled, including 23 infected with C. sinensis (C. sinensis) (OB+C.s) and 25 non-infected subjects (OB). The bile samples were collected by endoscopic retrograde cholangiopancreatography and analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MS). Additionally, multivariate statistical analysis methods were employed to identify differential metabolites. Next, bile amino acid levels were determined by targeted metabolomics analysis. A total of 146 and 132 significant metabolites were identified in electrospray ionization (ESI)+ and ESI- modes, respectively. The levels of amino acids (asparagine, glutamate, ornithine) and polyamines (spermidine and spermine) were significantly changed. Targeted analysis showed that the levels of amino acids (such as L-arginine, L-glutamine, L-lysine, L-propionic, and L-tyrosine) were lower in OB+C.s patients compared to those in OB patients. Marked metabolic pathways were involved in "Glutathione metabolism", "Caffeine metabolism", "Alanine, aspartate and glutamate metabolism", "Arginine and proline metabolism", "Purine metabolism", "Beta-Alanine metabolism", and "D-glutamine and D-glutamate metabolism". These results show that there were significant differences between OB+C.s and OB patients, especially in amino acids. The metabolic signature and perturbations in metabolic pathways may help to better distinguish OB+C.s and OB patients.

Photoacoustic spectroscopy and multivariate techniques: New applications to simplify the analysis of raw propolis

This paper investigated the application of photoacoustic spectroscopy to estimate total phenolics content in raw propolis collected in the state of Paraná, Brazil. The absorption spectrum from 260 nm to 650 nm indicated three main bands: the first around 270 nm related to phenolic acids and flavonoids; the second around 480 nm is associated with anthocyanins, and the third around 630 nm may be related to chlorophyll. The whole spectral range was analyzed using multivariate analysis methods and principal component analysis (PCA) was applied to determine the sample similarities and their relationship to geographic localization. Pre-processing methods, normalization, generalized least squares weighting - GLSW (classes based – color and region), mean center, and without GLSW, were compared. GLSW allowed propolis discrimination related to geographical origin and the corresponding photoacoustic signals to be verified. Propolis samples from southern and eastern Paraná showed photoacoustic signals of mean intensity, while samples from the western showed extreme minimum values.Interval partial least-squares-regression (iPLS) of the photoacoustic spectra and phenolic content had determination coefficients equal to R2C = 0.959 (calibration) and R2P = 0.897 (prediction), root mean square errors RMSEC = 0.787% and RMSEP = 0.516% and residual prediction deviations RPDC = 5.10 and RPDP = 3.42, in the concentration range of 2.89 – 15.02 % (w/w). The results indicated that the spectrum obtained by photoacoustic spectroscopy can be successfully used to estimate the level of phenolics in raw propolis. This approach has the advantages of avoiding the step of propolis extraction and the need to carry out classical analytical protocols which are time-consuming and produce chemical residues.

Application of machine learning to spectrum and image data

Machine learning is a useful tool when extracting hidden information from complex measurement data obtained via surface analysis, as in secondary ion mass spectrometry. Flexible learning methods often require significant effort to adjust parameters, as these parameters may have a significant effect on results. However, machine learning methods enable the extraction of new information that cannot be found by manual analysis. This paper presents some examples of complex data analyses using conventional multivariate analysis methods based on linear combinations (principal component analysis and multivariate curve resolution), an unsupervised learning method based on artificial neural networks (sparse autoencoder), and a supervised learning method based on decision trees (random forest). To obtain reproducible and useful results from machine learning applications to surface analysis data, the preparation of data sets—including the selection of variables and the raw data conversion process—is crucial. Moreover, sufficient information representing analytical purposes, such as the chemical structures of unknown samples, material types, and physical or chemical properties of particular materials, must be contained in the data set for supervised learning.

Discrimination as a relational matrix of analysis: race, gender, age, social class, immigration, religion, politics, and civil identification: a case study of Rio de Janeiro, Brazil

The general objective of this research is to analyze how and to what extent racist, patriarchal, classist and political arguments are interlocked and mutually reinforced in multiple residual forms today. This article adopts Foucault’s concepts of biopower and normalization to analyze the persistence of racism surrounding unequal and discriminatory power relations and the oppression of racialized, gender, and social inequalities (Foucault 1994; Koopman 2013). A representative survey was applied to 759 urban inhabitants of Rio de Janeiro, Brazil, who answered a questionnaire asking for their socio-demographic characteristics, perceptions, expectations, attitudes, practices, and intergroup relationships regarding racism, patriarchalism, sexism, homophobia, xenophobia, class discrimination and political-ideological self-identification. The multivariate method of Principal Component Analysis (PCA) was applied, and the results show the predominance of patriarchal values against women, LGBTQ+, Black, youth and poor persons, through explicit racism and the legitimation of the use of violence for both, men and women. Women primarily reject providing sex education at school, agree with their domestic role as wives and with the idea that females’ behavior causes their own rapes, reject LGBTQ marriage, and discriminate against youths, while men rank first the domestic role of women, explicit racism and criminalization of Black people.

Relationship between vertical facial morphology and dental arch measurements in class II malocclusion: a retrospective study.

To evaluate the relationship between dental arch measurements and the vertical facial pattern determined in skeletal Class II untreated patients. Lateral cephalograms and plaster models were obtained from 124 untreated female adults (average age: 17.6 ± 3.8 years). Class I (CI), Class II Division 1 (CII/1) and Class II Division 2 (CII/2) malocclusions were divided into three subgroups according to their vertical morphology as hypodivergent, normodivergent and hyperdivergent. The multivariate variance analysis (MANOVA) method was used in the comparison of measurement values according to vertical and sagittal morphology. The relationship between both A point-Nasion-B point (ANB) and Frankfurt-mandibular plane (FMA) angles and dental arch measurements was examined by Pearson correlation analysis. The significance level was received as p < 0.05. While vertical morphology has a statistically significant effect on mandibular arch length, sagittal morphology affects maxillary arch depth. The parameters influenced by both morphologies are maxillary and mandibular arch length, as well as maxillary intermolar width. The mandibular arch length was significantly shorter in hyperdivergent-CII-2 malocclusion (50.5 ± 7.4 mm). Larger values were obtained in both mandibular arch length and maxillary arch depth measurements in CII-1 malocclusion compared to CII-2 malocclusion. The maxillary intermolar width was significantly shorter in hypodivergent-CII-1 malocclusion (46.8 ± 3.4 mm), while it was higher in hypodivergent-CI malocclusion (51.1 ± 3.4 mm). The maxillary arch length was the lowest in hyperdivergent-CI malocclusion (63.1 ± 13.3 mm) and the highest in hypodivergent-CI malocclusion (72.8 ± 7.6 mm). Additionally, a positive but weak correlation was found between ANB and FMA angles. Dental arch measurements have been found to be affected by both vertical facial morphology and skeletal sagittal relationship. A positive correlation was found between ANB and FMA angles.

Mediating effect of e-learning quality on learning outcomes through student satisfaction in nursing education

Abstract Objective This study aimed to determine the relationships between e-learning quality, student satisfaction, and learning outcomes and the mediating effect of student satisfaction. Methods A cross-sectional quantitative correlational study using a predictive design and multivariate analysis method was employed in this study. A sample of 241 nursing students were recruited through an online survey based on a stratified random sampling technique. The variance-based Partial Least Squares Structural Modeling analysis method was used to test the possible relationship and mediating effect among the variables. Results The findings revealed statistically significant relationships between e-learning quality, student satisfaction, and learning outcomes. A mediating effect of 37.2% is predicted for e-learning quality on learning outcomes through student satisfaction. Conclusions This study emphasizes the learning needs of working nurses and the impact on their learning outcomes in e-learning nursing undergraduate programs. Advances in e-learning education have assisted nurses to be more self-sufficient in their pursuit of lifelong learning.

Application of self-organizing maps to coal elemental data

Understanding the modes of occurrence of elements in coal is important, not only to help properly evaluate the impacts of potentially toxic elements on the environment and human health but also to provide technical guidance for recovering critical elements from coal ash. Statistical and multivariate data analysis methods have widely been used, together with physical and chemical methods, to determine the modes of occurrence of elements in coal. However, some of the statistical methods, e.g., average linkage hierarchical clustering algorithm have some disadvantages (e.g., statistical errors and poor visualization). A self-organizing map is an unsupervised artificial neural network, and it is known for its high data mining capability and excellent data visualization. In contrast to the average linkage hierarchical clustering algorithm that is commonly used for analyzing the modes of occurrence of elements in coal, the self-organizing map algorithm can provide a topological relationship among elements instead of merely providing the groups to which the elements belong. This paper focuses on the application of self-organizing map to coal elemental data for analyzing the modes of occurrence of elements in coal. Samples used in this study are from the Adaohai, Hailiushu, and Datanhao mines, all located in the Daqingshan Coalfield, Inner Mongolia, China. The results obtained from the self-organizing map algorithm are compared with those produced by average linkage hierarchical clustering algorithm. Based on the previous investigations (mainly direct methods) and further analysis, it can be concluded that the results from the self-organizing map algorithm in this investigation are more consistent with the geochemical nature and previous investigations by direct methods than those from average linkage hierarchical clustering algorithm. Consequently, the self-organizing map algorithm is a new reliable and intuitive method for analyzing the modes of occurrence of elements in coal.

A method for temporal-spatial multivariate genomic analysis of acute wound healing via tissue stratification: a porcine negative pressure therapy pilot study.

Introduction: Wound therapies are capable of modulating the complex molecular signaling profile of tissue regeneration. However traditional, bulk tissue analysis results in nonspecific expressional profiles and diluted signaling that lacks temporal-spatial information. Methods: An acute incisional porcine wound model was developed in the context of negative pressure wound therapy (NPWT). Dressing materials were inserted into wounds with or without NPWT exposure and evaluated over 8-hours. Upon wound explantation, tissue was stratified and dissected into the epidermis, dermis, or subcutaneous layer, or left undissected as a bulk sample and all groups processed for RNAseq. RNAseq of stratified layers provided spatial localization of expressional changes within defined tissue regions, including angiogenesis, inflammation, and matrix remodeling. Results: Different expressional profiles were observed between individual tissue layers relative to each other within a single wound group and between each individual layer relative to bulk analysis. Tissue stratification identified unique differentially expressed genes within specific layers of tissue that were hidden during bulk analysis, as well as amplification of weak signals and/or inversion of signaling between two layers of the same wound, suggesting that two layers of skin can cancel out signaling within bulk analytical approaches. Discussion: The unique wound stratification and spatial RNAseq approach in this study provides a new methodology to observe expressional patterns more precisely within tissue that may have otherwise not been detectable. Together these experimental data offer novel insight into early expressional patterns and genomic profiles, within and between tissue layers, in wound healing pathways that could potentially help guide clinical decisions and improve wound outcomes.

Learning capacity and effectiveness in the management of organizational projects

Best practices in project management have been positioned in the organizational field as a suitable alternative to achieve the planned objectives. Learning capacity is one of the most important advantages of any organization, linked to human talent and business culture. This research sought to establish the relationship between organizational learning capacity and effectiveness in the management of the projects executed. In this study, 192 project managers were surveyed. The data were processed through a multivariate factor analysis method with the purpose of specify the relationship between the proposed variables. This research allowed us to understand learning capacity as a multidimensional variable that positively affects project management.

Formation and evolution of soil salinization based on multivariate statistical methods in Ningxia Plain, China

The Ningxia Plain, situated in the arid zone of northwest China, is a typical dryland plain that faces significant challenges to sustainable agricultural development due to soil salinization. In this study, we employed multivariate analysis and geostatistical methods to investigate the degree and distribution types of soil salinization and the hydrochemical characteristics of shallow groundwater. We also examined the relationship between soil salinization and hydrogeochemical characteristics by analyzing the sources of groundwater ions. This study developed a hydrogeochemical model to describe the soil salinization process in the Ningxia Plain. The results indicate that the majority of surface soils are alkaline type soils, followed by chloride-sulfate type soils. The groundwater is mainly fresh water and brackish water, with a hydrochemical type of SO4·HCO3–Ca·Mg or SO4·Cl–Ca·Mg. Saline water and salt water are represented by Cl–Na·Mg or Cl·SO4–Na·Mg. We also observed spatial trends in groundwater depth and total dissolved solids (TDS) concentrations that were opposite to soil salinity, which suggests a certain degree of second-order trend effect. Furthermore, the degree of soil salinization increased and then decreased from the pre-mountain alluvial plain to the Yellow River alluvial plain, while the groundwater chemistry ranged from simple to complex. The most severe area of soil salinization was found to be concentrated between Hongguang and Yaofu, which is also the area where shallow groundwater salinity accumulation is mainly influenced by continental salinization. In summary, this study provides valuable insights into the hydrogeochemical characteristics of the Ningxia Plain, which can inform strategies for mitigating soil salinization and promoting sustainable agriculture development in arid regions.

Impact of Gender on Mortality After Transcatheter Edge-to-Edge Repair for Functional Mitral Regurgitation

Recent studies suggested short-term mortality after transcatheter edge-to-edge repair (TEER) was comparable between men and women. However, the gender-specific prognostic difference in the long-term follow-up after TEER is still unknown. To evaluate the impact of gender on long-term mortality after TEER for functional mitral regurgitation (FMR) using multicenter registry data. We retrospectively analyzed 1,233 patients (male 60.3%) who underwent TEER for FMR at 24 centers. The impact of gender on all-cause death and hospitalization for heart failure (HF) after TEER was evaluated using multivariate regression analysis and propensity score (PS) matching methods. During the 2-year follow-up, 207 all-cause death and 263 hospitalizations for HF were observed after TEER for FMR. Men had a significantly higher incidence of all-cause death than women (18.6% vs 14.1%, log-rank p=0.03). After adjustment by multivariate Cox regression and PS matching, the male gender was significantly associated with a higher incidence of all-cause mortality after TEER than the female gender (hazard ratio 2.11, 95% confidence interval 1.42 to 3.14 in multivariate Cox regression; hazard ratio 1.89, 95% confidence interval 1.03 to 3.48 in PS matching). The gender-specific prognostic difference was even more pronounced after 1-year of TEER. On the contrary, there was no gender-related difference in hospitalization for HF after TEER. In conclusion, women with FMR had a better prognosis after TEER than men, whereas this was not observed in hospitalization for HF. This result might indicate that women with FMR are more likely to benefit from TEER.

Moderator Effect of Chronic Disease on The Relationship Between Marriage Adjustment and Satisfaction in Married Couples

Objective: The purpose of our study is to show how the relationship between marital adjustment and satisfaction will change in cases of chronic disease in either or both spouses of married couples using a multivariate statistical analysis method. Methods: Marriage adjustment ve marriage satisfaction scales were used. A structural equation modeling - multiple group analysis method was used in the study, which was designed as a relational screening model. Results: In the study, which included 898 participants, 56.6% of the participants were female and 43.4% were male. The mean age of the participants was 36.94 ± 8.72 standard deviations. First, the relationship between marital adjustment and satisfaction was analyzed using structural equation modeling, and the relationship between the scales was found to be statistically significant (p=0.001). In the model, which was significant and sufficient, the variable of chronic disease was coded on the arrow representing the regression coefficient between the scales, and multiple group analysis was applied. The relationship between marital adjustment and satisfaction was found to be weak among individuals with chronic diseases. The rate of marriage satisfaction explaining marriage adjustment was lower in individuals without chronic disease (R2=0.16) than in those without chronic disease (R2=0.10). While ego scores were not significant in individuals without chronic disease (p=0.237), they were statistically significant in individuals with chronic disease (p=0.017). Conclusion: Chronic diseases has been found to have a significant impact on the relationship between spouses. Many studies have examined the effects of chronic diseases on marriage. However, our study differs from other studies because of the analytical methods used. In the scales, it was determined whether the chronic disease showed a change in the relationship between the scales, not the scores in the chronic disease state.

An integrated multi-criteria decision-making and multivariate analysis towards sustainable procurement with application in automotive industry

Green Supply Chain Management (GSCM) has emerged as a paramount issue in modern business organizations striving to become environmentally sustainable. Suppliers are pivotal in building a green supply chain. Green supplier selection (GSS) is a complex task involving several steps, from evaluation to final selection. This research aims to select spare parts suppliers of an automotive company based on their GSCM practices. Fourteen critical criteria are extracted from extant literature and refined through a Delphi study. The data was collected through interviews with industry experts using structured questionnaires. This study proposes integrated multi-criteria decision-making (MCDM) and multivariate analysis method with internal consistency checks. The Principal Component Analysis (PCA) is used to calculate criteria weights. A Simple Additive Weighting (SAW) method ranks the suppliers based on weighted criteria. The result shows that “collaboration with suppliers for green purchasing” is the most influential parameter for GSS. The outcome of this research may aid managers in selecting the most suitable green suppliers in the automotive industry by attaining sustainability. The proposed framework can be replicated to select suppliers in other industries.

Primary Metabolite Screening Shows Significant Differences between Embryogenic and Non-Embryogenic Callus of Tamarillo (Solanum betaceum Cav.).

Tamarillo is a solanaceous tree that has been extensively studied in terms of in vitro clonal propagation, namely somatic embryogenesis. In this work, a protocol of indirect somatic embryogenesis was applied to obtain embryogenic and non-embryogenic callus from leaf segments. Nuclear magnetic resonance spectroscopy was used to analyze the primary metabolome of these distinct calli to elucidate possible differentiation mechanisms from the common genetic background callus. Standard multivariate analysis methods were then applied, and were complemented by univariate statistical methods to identify differentially expressed primary metabolites and related metabolic pathways. The results showed carbohydrate and lipid metabolism to be the most relevant in all the calli assayed, with most discriminant metabolites being fructose, glucose and to a lesser extent choline. The glycolytic rate was higher in embryogenic calli, which shows, overall, a higher rate of sugar catabolism and a different profile of phospholipids with a choline/ethanolamine analysis. In general, our results show that a distinct primary metabolome between embryogenic and non-embryogenic calli occurs and that intracellular levels of fructose and sucrose and the glucose to sucrose ratio seem to be good candidates as biochemical biomarkers of embryogenic competence.

Multivariate analysis of refractive state in eyes with keratoconus

ObjectiveTo demonstrate a multivariate method of analysis of the short-term variation of refractive state in keratoconus (KC) patients.Methods and analysisIn this observational study, 19 eyes with KC and 19 healthy...