Total Variance In Data Research Articles

Dimensions of emotional distress among Brazilian workers in a COVID-19 reference hospital: A factor analytical study.

BACKGROUNDThe coronavirus disease 2019 (COVID-19) pandemic is an unprecedented challenge for public health and has caused the loss of millions of lives worldwide. Hospital workers play a key role in averting the collapse of the health system, but the mental health of many has deteriorated during the pandemic. Few studies have been devoted to identifying the needs of workers on frontline duty.AIMTo investigate dimensions of common emotional symptoms and associated predictors among Brazilian workers in a COVID-19 reference hospital.METHODSThis is an observational study of the mental health of professionals in a COVID-19 hospital in the city of São Paulo. We invited all hospital employees to respond to an online survey between July and August 2020, during the first peak of the pandemic. Data of 1000 participants who completed the survey were analyzed (83.9% were women and 34.3% were aged 30 to 40). Hospital workers self-reported the presence of symptoms of depression, anxiety, trauma-related stress, and burnout through the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Impact of Event Scale-Revised and the Mini-Z Burnout Assessment respectively. Responses were assembled and subjected to exploratory factor analysis to reveal workers’ core emotional distress. Multiple linear regression models were subsequently carried out to estimate the likelihood of dimensions of distress using questions on personal motivation, threatening events, and institutional support.RESULTSAround one in three participants in our sample scored above the threshold of depression, anxiety, post-traumatic stress disorder, and burnout. The factor analysis revealed a three-factor structure that explained 58% of the total data variance. Core distressing emotional domains were avoidance and re-experience, depression-anxiety, and sleep changes. Regression analysis revealed that institutional support was a significant protective factor for each of these dimensions (β range = -0.41 to -0.20, P < 0.001). However, participants’ personal motivation to work in healthcare service was not associated with these emotional domains. Moreover, the likelihood of presenting the avoidance and re-experience dimension was associated with having a family member or close friend be hospitalized or die due to COVID-19 and having faced an ethical conflict.CONCLUSIONDistressing emotional domains among hospital workers were avoidance and re-experience, depression and anxiety, and sleep changes. Improving working conditions through institutional support could protect hospital workers' mental health during devastating public health crises.

Ecological determinants of algal communities of different types of ecosystems

The results of studies of the environmental regularities of the formation of algal communities in soils of various ecosystems in the south of Ukraine are presented. 26 forest, 11 steppe and 3 saline ecosystems were investigated. The research has established the species richness of algae in each ecosystem and produced a multidimensional ordination of algal communities based on the analysis of the main components to clarify the factors that determine the composition of algal communities. The research has established the species richness of algae in each ecosystem and produced a multidimensional ordination of algal communities based on the analysis of the main components to clarify the factors that determine the composition of algal communities. Predictors determined by edaphic conditions and phytocenotic interactions associated primarily with edificators of ecosystems were used for the analysis. The ecological space of the studied ecosystems was formed by four main factors with eigenvalues greater than one, which explained 81.4% of the total variance. PC1 (34.82% of variance) is associated with the type of ecosystem and such edaphic parameters as pH, the availability of moisture in the habitat, and soil mineralization. PC2 (21.98%) reflects changes in the gradients of trophicity (humus content) and granulometric composition of soils. PC3 and PC4 additionally explain 16.04% and 9.27% of the total data variance, respectively. Their values mainly depend on the mineralization, trophicity, and moisture supply of edaphotopes, which, at the level of the composition of algae, is obviously associated with the heterogeneity of the ecological preferences of the algal species themselves, as well as the variability of ecological niches of ecosystems, due to which there are species more typical for other types of ecosystems in the communities. The use of the factor rotation procedure by the Varimax normalized method made it possible to concretize the taxa most associated with the main components: PC1 indicates the various Cyanobacteria species, PC2 – Chlorophyta, Streptophyta and Eustigmatophyceae, PC3 – Xanthophyceae and Euglenozoa, PC4 – Bacillariophyceae.

Grouping the Potential Local Feed Ingredients for Ornamental Fish Feed based on their Nutrient Composition, Cost, and Availability

In the ornamental fish industry, there is a growing concern about the higher freight cost of feed ingredients. The present study aimed to cluster the locally available ingredients based on their nutrient contents, price, and availability. A total of 15 ingredients; fish meal (FM), by-catches (BC), soybean seeds (Soy), maize (Ma), duckweed (DW), Azolla pinnata (Az), rice bran (RB), shrimp shells (SS), palmyrah fruit pulp (PFP), red seaweed (RS), wheat flour (WF), cassava flour (CF), cornflour (CoF), coconut copra waste (CCC), and groundnut cake (GNC) were selected as locally available ingredients. Principal Component Analysis (PCA) was performed by grouping the ingredients based on their nutrient contents, price, and availability using Minitab 16. In PCA, the two first components accounted for 57.50% of the total variance in the initial data. Protein, calcium, phosphorus, and price contribute positively with component 1, whereas carbohydrate content is related negatively to this component. Calcium and ash content correlated positively with component 2 and fat, gross energy, and availability were linked negatively to it. The PCA allowed the clustering the ingredients into four groups. Ingredients in Group 1, including Az, SS, RB, DW, and RS showed a positive correlation with component 2 and they contain more fiber although, they are not accessible throughout the year. Ingredients of Group 2, PFP, WF, CoF, and CF are excellent sources of starch but contained less protein content (0.94%, 11.00%, 0.00%, and 2.80% respectively). CCC, Ma, GNC, and SB pertain to group 3 correlating poorly with component 2. They are an excellent source of protein with the advantage that they are relatively affordable and easy to available than the other groups. Ingredients in group 4 incorporated BC and FB. Although they are high in protein, they are expensive and very poor in carbohydrates. Therefore, it is recommended that the ingredients in group 3 are the best choice for feed formulation with balanced nutrition at a low cost for fish feed preparation.

Nitrogen fertilizer alleviates root-knot nematode stress in beetroot by suppressing the pathogen while modulating the antioxidant defense system and cell viability of the host

The ongoing removal of environmentally harmful nematicides from the global market necessitates the development of new nematode management strategies. Therefore, alternate approaches that are eco-friendly are preferable to manage the menace of root-knot disease in agroecosystems. The current study sought to evaluate and determine the optimum level of nitrogen fertilizer with respect to its plant growth promoting and nematicidal activities. Among the different concentrations of nitrogen fertilizer supplemented to the soil, U150Mi (150 kg/h N) was found most optimum to considerably (P ≤ 0.05) improve the growth and yield attributes of beetroot inoculated with Meloidogyne incognita. Scanning electron microscopy demonstrated the improvement in stomatal attributes of beetroot at U150Mi (150 kg/h N) as compared to the untreated inoculated control. Confocal laser microscopy confirmed the dose-dependent nematicidal properties of nitrogen fertilizer under laboratory conditions. The cell viability of beetroot was also markedly improved at U150Mi (150 kg/h N) nitrogen fertilizer level. The formation of galls and egg masses was suppressed significantly with the application of nitrogen fertilizer at U150Mi (150 kg/h N) level. The PCA (principal component analysis) accounted for 99.61% for the total-data variability in plants inoculated with M. incognita and treated with different nitrogen fertilizer levels.

Influence of cobalt chloride and ferric citrate on purple non-sulfur bacteria Rhodopseudomonas yavorovii

Heavy metals that enter the environment due to natural processes or industrial activities, when accumulated, have a negative impact on organisms, including microorganisms. Microorganisms have developed various adaptations to heavy metal compounds. The aim of our work was to investigate the influence of ferric citrate and cobalt (II) chloride on biomass accumulation, indicators of free radical damage and activity of enzymes of the antioxidant defense system of bacteria Rhodopseudomonas yavorovii IMV B-7620, that were isolated from the water of Yavorivske Lake (Ukraine, Lviv region), which was formed as a result of flooding of a sulfur quarry. We used cultural, photometric methods, and statistical processing of the results was performed using two-way ANOVA and factor analysis. It was found that ferric citrate at a concentration of 1–12 mM causes inhibition of the accumulation of biomass of bacteria Rh. yavorovii IMV B-7620 up to 44.7%, and cobalt (II) chloride at a concentration of 1–15 mM – up to 70.4%, compared with the control. The studied concentrations of ferric citrate and cobalt (II) chloride cause free radical damage to lipids and proteins of Rh. yavorovii IMV B-7620. As a result of two-way ANOVA we found that under the influence of ferric citrate statistically significant changes in biomass accumulation, lipid hydroperoxides and thiobarbiturate reactive species content, superoxide dismutase activity were predetermined by increasing the concentration of metal salts as well as increasing the duration of cultivation of bacteria, while the content of diene conjugates and catalase activity changed with increasing duration of cultivation. Under the influence of cobalt (II) chloride, statistically significant changes in all studied indicators were found both due to the increase in the concentration of metal salts and with increasing duration of bacterial cultivation. The studied parameters of Rh. yavorovii IMV B-7620 cells under the influence of ferric citrate and cobalt (II) chloride are combined into two factors, that explain 95.4% and 99.2% of the total data variance, respectively. Under the influence of ferric citrate, the first latent factor included diene conjugates, thiobarbiturate reactive species, carbonyl groups in proteins, which are closely linked by a direct bond and inversely related to the content of lipid hydroperoxides and catalase activity. The second latent factor included duration of cultivation of bacteria, biomass accumulation, and superoxide dismutase activity, which are inversely related to lipid hydroperoxide content and catalase activity. Under the influence of cobalt (II) chloride, the first latent factor included the content of lipid hydroperoxides, carbonyl groups in proteins, as well as catalase and superoxide dismutase activities, which are inversely related to bacterial biomass.

Eco-friendly dual-edged management of fly ash and its antagonistic interplay with Meloidogyne incognita on beetroot (Beta vulgaris L.)

Fly ash (FA) management is a key concern of ecologists around the world, so its potential as a nutritional supplement for agro-ecosystems needs to be explored. Therefore, alternate techniques that are eco-friendly to manage this emerging dual-edged waste are preferable in this field. The current study sought to determine the soil-modifying, crop yield improvement, and nematicidal properties of FA. In this study, beetroot seeds were sown in pots comprising field soil amended with differing proportions of FA (w/w) revealed the bio-fold properties of FA. Biomineralization and mapping of elements revealed that increased nutritional elements in soil supplemented with 15% FA induced growth-performance and yield of beetroot. Molecularly and morphologically characterized Meloidogyne incognita was used as nematode in this study for optimization of nematicidal properties FA. Plant growth performance, photosynthetic pigments, and yield of beetroot were significantly reduced owing to M. incognita as compared to control (un-treated and un-inoculated), and 15% FA reversed the negative effect of M. incognita significantly (P < 0.05) as compared to control plants. Confocal laser microscopy confirmed that 15% FA augmented in soil reduced nematode-juvenile invasion in beetroot as compared with control. The PCA (principal component analysis) accounted for 98.63% and 98.8% for the total-data variability in plants without nematodes and total data variability in treated plants (M. incognita + FA) respectively, which showed fit for a significant correlation between the various studied parameters in present study.

A neural network regularization method to address variance inflation in autoencoders

There exist various machine learning techniques which can be used to reduce the dimensionality of original data while minimizing the information loss. Principal component analysis (PCA) is one of the most well known such techniques, which transforms the original correlated variables into uncorrelated variables called principal components. Although PCA is known to preserve the total variance of the original data during the transformation, there are some cases with a potential of variance inflation, where the total variance of principal components becomes much larger than that of original variables. It is important to prevent variance inflation, as it can negatively affect the performance of other application systems (e.g. process monitoring systems) which are designed on the basis of principal component with inflated variances. Variance inflation also has a high potential to occur during the training of autoencoder, a special type of neural network performing nonlinear version of PCA. Although there are several neural network regularization methods available to alleviate the problem of variance inflation, none of them is tailored to do such task. To this end, in this work, an alternative neural network regularization method is proposed, which can strongly regulate the total variance in the feature space. Using the Tennessee Eastman process as an illustrative example, the proposed regularization method is compared with the existing ones in terms of neural network overfitting, variance inflation, and training time.

Soil metal concentrations after five years of pasture-tosugarcane conversion

Extensive pastures are commonly converted to sugarcane areas in Brazil. In soils cultivated with sugarcane, tillage and fertilization are management practices commonly carried out. Soil management practices alter the physical and chemical properties increasing or decreasing metal availability. The purpose of this study was to quantify soil metal concentrations during pasture-to-sugarcane conversion correlating these changes with physical and chemical properties. The results showed that the concentration of metals in soil samples occurred according to the following order Fe > Mn > Cr > Zn > Cu > Pb > Cd. Significant increases in soil metal concentrations due the sugarcane cultivation were observed to Cd and Cu. The soil physical and chemical properties presented correlations with soil metal concentrations. There was a strong relationship on inorganic fertilization to change in soil metal concentrations. The principal component analysis (PCA) explained 55.4% of the total data variance, separating factors in two groups that pointed to an influence of fertilization on metal grouping. Inorganic fertilizer application may input 1.06 g·ha–1·year–1 Cd. The Cd concentration ranged from 0.15 to 1.07 mg·kg–1, representing addictions of 9.54 mg·ha–1·year–1 Cd in soil. The metal concentrations observed after five years of pasture-to-sugarcane conversion did not represent environmental risks since the concentrations remained below the Brazilian official determinations.

Climatology and modeling of ionospheric irregularities over Greenland based on empirical orthogonal function method

This paper addresses the long-term climatology (over two solar cycles) of total electron content (TEC) irregularities from a polar cap station (Thule) using the rate of change of the TEC index (ROTI). The climatology reveals variabilities over different time scales, i.e., solar cycle, seasonal, and diurnal variations. These variations in different time scales can be explained by different drivers/contributors. The solar activity (represented by the solar radiation index F10.7P) dominates the longest time scale variations. The seasonal variations are controlled by the interplay of the energy input into the polar cap ionosphere and the solar illumination that damps the amplitude of ionospheric irregularities. The diurnal variations (with respect to local time) are controlled by the relative location of the station with respect to the auroral oval. We further decompose the climatology of ionospheric irregularities using the empirical orthogonal function (EOF) method. The first four EOFs could reflect the majority (99.49%) of the total data variability. A climatological model of ionospheric irregularities is developed by fitting the EOF coefficients using three geophysical proxies (namely, F10.7P, Bt, and Dst). The data-model comparison shows satisfactory results with a high Pearson correlation coefficient and adequate errors. Additionally, we modeled the historical ROTI during the modern grand maximum dating back to 1965 and made the prediction during solar cycle 25. In such a way, we can directly compare the climatic variations of the ROTI activity across six solar cycles.

Influence of different types of margins in the fish assemblage from an urban river

The presence or absence of a fish species may be associated with the physical and chemical characteristics of the water, in addition to the state of preservation of the riparian zone. This study examined whether the taxonomic fish composition in an urban river varies along three riparian zone preservation conditions: preserved, intermediate preservation and degraded. Six urban stretches of the Sorocaba/São Paulo River were selected for this study, with monthly sampling being carried out from June 2019 to February 2020. The samplings were carried out with the aid of a sieve, dip net, and trawl. Regarding the types of margins and environmental variables, the Principal Component Analysis explained 97.49% of the data variation, axis 1, which explained 91.85% of the total variation, was formed by the variables conductivity and total dissolved solids. Axis 2, which explained 5.64% of the variation, was formed by Sun Rays% and Siltation%, being inversely proportional to Riparian Forest%. For the ichthyofauna, the samples consisted in 50,983 fish distributed in 5 orders, 8 families, and 12 species. Cyprinodontiformes had the highest abundance, with 96.96% of the total number of individuals, followed by Siluriformes (2.39%), and the sum of the other orders did not reach 1% of the total collected. According to each type of margin, it was possible to register 11,592 individuals for the Margin A – preserved, with S=8 species, being 2 exclusive (Corydoras flaveolus and Hoplosternum littorale) and 3 exotic (Coptodon rendalli, Pterygoplichthys ambrosettii, and Poecilia reticulata), the latter was dominant in all types of margins. For Margin B – intermediate preservation, 19,645 individuals were sampled, with S=5 species, no exclusive species, only exotic and more tolerant native species such as Hoplias malabaricus and Hypostomus ancistroides. For Margin C – degraded, 19,746 individuals were recorded, the largest number of species (S=10), 3 were exclusive to this type of margin (Psalidodon fasciatus, Rhamdia Quelen, and Geophagus brasiliensis). The pairwise comparison (similarity percentage) showed that the assemblage structure was different for the types of margins. The Canonical Correspondence Analysis explained 92.46% of the total data variation, showing the relationship of species to environmental data. It is evident the importance of preservation of micro-habitats still present in the locals sampled, since different anthropic pressures cause great loss of diversity, especially for native and more sensitive species.

Forecasting bean yield losses under weed and Fusarium impacts from field plot statistical modeling

A better understanding of bean dynamic threats and pathogens is needed to improve accuracy of field measurement for breeding and mitigation programs. Thus, a two-year field plot study examined associations of bean production, Fusarium root rot, and weed in two cultivars sown under different dates and weed control treatments. We examined six standard curves to model bean leaf area, disease incidence, and weed biomass development across 256 plots. Gaussian, linear-by-linear and exponential models were fitted to bean-disease-weed development data, respectively, and then model parameters besides maximum values were used in the remainder of analyses. There were significant correlations between bean-disease-weed development descriptors. Principal component analysis showed bean-disease-weed development descriptors accounted for 84% of total data variance. Using six principal components as variables, a multivariate regression model showed the descriptors leaf area, Fusarium root rot, and weed biomass development explained 31% of variations in bean yield.

STATISTICAL ANALYSIS OF THE RELATIONSHIP BETWEEN SOCIO­HYGIENIC FACTORS AND THE DISEASES OF BLOOD AND BLOOD­FORMING ORGANS &#x0D; IN THE PRIMORSKY REGION

Aims: To study associations between the incidence of diseases of the blood and blood-forming organs and socio-hygienic factors in the Primorsy region in the Russian Far East. Methods: Statistical analysis of the data was carried using 14 indicators for the period from 1991 to 2017 years. Associations between the studied socio-hygienic factors and the outcomes were identified using correlation analysis. To reduce the dimensionality in the data, principal component analysis was used, which made it possible to isolate two components that explained 86,82% of the total variance in the original data. The first component characterized the socio-economic, demographic and environmental conditions of human life while the second component contained indicators related to medical-social factors. All data were analyzed using R. Results: Strong inverse associations between the incidence of the studied diseases and the first component. Correlations between the second component and the outcomes was moderate inverse. Based on the selected components, a regression model has been developed to morbidity using the values of significant predictors. An increase in morbidity is accompanied by an increase in population income and a decrease in the population with a decrease in emissions into the atmosphere. At the same time, the incidence positively associated with the number of doctors and negative with the provision of hospital beds. Conclusion: Our results suggest that the incidence of diseases of the blood and blood-forming organs is associated with a group of factors that characterize the quality of life of the population.

A model for firmness and low temperature-induced storage breakdown disorder of ‘Hayward’ kiwifruit in supply chain

Flesh softening and chilling injury related storage breakdown disorder (SBD) are the most important quality issues in the kiwifruit industry. This study was aimed at developing a mathematical model that could describe and predict evolution of storage breakdown disorder and firmness. A mechanistic modelling approach was adopted, with different concepts used to model the three phases of softening often observed in kiwifruit. The initial rapid softening phase was modelled as a function of starch breakdown, the slow softening phase was modelled as function of pectin degradation, while the final softening phase often characterised by tissue collapse, was modelled as a function of chilling injury related storage breakdown disorder. The model was calibrated using data collected from three seasons, across multiple maturity areas and subjected to different cooling and storage temperature regimes. 93 % of the total variance in the firmness data could be explained by the model. A mean absolute error of 0.2–2.2 % was obtained for SBD predictions. The developed model could be used by growers and postharvest managers to predict kiwifruit softening under different supply chain conditions.

Natural and Anthropogenic Controls of Groundwater Quality in Sri Lanka: Implications for Chronic Kidney Disease of Unknown Etiology (CKDu)

Poor groundwater quality in household wells is hypothesized as being a potential contributor to chronic kidney disease of unknown etiology (CKDu) in Sri Lanka. However, the influencing factors of groundwater quality in Sri Lanka are rarely investigated at a national scale. Here, the spatial characteristics of groundwater geochemistry in Sri Lanka were described. The relationships of groundwater quality parameters with environmental factors, including lithology, land use, and climatic conditions, were further examined to identify the natural and anthropogenic controlling factors of groundwater quality in Sri Lanka. The results showed that groundwater geochemistry in Sri Lanka exhibited significant spatial heterogeneity. The high concentrations of NO3− were found in the districts that have a higher percentage of agricultural lands, especially in the regions in the coastal zone. Higher hardness and fluoride in groundwater were mainly observed in the dry zone. The concentrations of trace elements such as Cd, Pb, Cu, and Cr of all the samples were lower than the World Health Organization guideline values, while some the samples had higher As and Al concentrations above the guideline values. Principal component analysis identified four components that explained 73.2% of the total data variance, and the first component with high loadings of NO3−, hardness, As, and Cr suggested the effects of agricultural activities, while other components were primarily attributed to natural sources and processes. Further analyses found that water hardness, fluoride and As concentration had positive correlations with precipitation and negative correlations with air temperature. The concentration of NO3− and water hardness were positively correlated with agricultural lands, while As concentration was positively correlated with unconsolidated sediments. The environmental factors can account for 58% of the spatial variation in the overall groundwater geochemistry indicated by the results of redundancy analysis. The groundwater quality data in this study cannot identify whether groundwater quality is related to the occurrence of CKDu. However, these findings identify the coupled controls of lithology, land use, and climate on groundwater quality in Sri Lanka. Future research should be effectively designed to clarify the synergistic effect of different chemical constituents on CKDu.

Does crossbreeding improve the performance and carcass traits of Berganês sheep?

This study aimed to evaluate the performance and carcass of lambs of different genotypes through univariate and multivariate analysis. Intact male lambs of the Berganês (BG), Berganês x Santa Inês (BSI), and Berganês x Dorper (BD) genotypes and the control group Dorper x Santa Inês (DSI) were used. Eight lambs of each genotype were used, aged between 4 and 5months, with an average initial weight of 27.52 ± 3.79kg, distributed in a completely randomized design. Data were tested by analyzing variance, followed by Tukey's test and principal component analysis (PCA) and discriminant analysis (DA). Slaughter body and average daily gain were higher for BD and BG than BSI (P < 0.05). On the other hand, BSI, BD, and DSI showed higher cold carcass yield than BG (P < 0.05). Lower cooling losses (CL) and more excellent conformation were observed in DSI (P < 0.05). BD showed a higher carcass compactness index (CCI), and DSI showed a higher leg compactness index (LCI) (P < 0.05). PCA produced seven components explaining 83.59% of total data variability. Based on the discriminant analysis, LCI, CL, CCI, and conformation showed greater power to distinguish the genotypes, with a high index of the lambs' classification to their respective genotypes. The crossbreeding improved carcass yield and reduced cooling weight loss. The Berganês ecotype and its crossbreeds have performance and carcass traits compatible with the DSI genotype. According to the DA, heterogeneity was found between the Berganês ecotype and its crossbreeds, with CL, LCI CCI, and conformation showing greater discrimination power.

Multivariate analysis of chemical attributes of an oxisoil under Saccharum spp. and Pinus spp., Batatais, São Paulo

This study aims investigate the correlations of soil chemical attributes in watersheds in order to highlight the changes caused by soil management, differentiating them by multivariate statistical analysis. The watersheds of the first order of magnitude were selected three occupied with sugarcane, three with the Pinus reforestation and a witness with greater occupation of native forest. The composite samples of soil were collected at five points around each major source in depth 0-0,2 m, on two occasions (April and December). The first two factors aggregated 58.09% of the total variance of the initial data (F1 = 44.80%; F2 = 13.29%). The first factor shows a direct correlation between the pH, Ca, Mg, SB and V variables, which are inversely correlation with H+Al and Al variables, showing the process of soil acidity correction (lime) in the occupied watersheds with sugarcane crop. The second factor show a direct correlation between the T, OM and Cu variables, indicating high soil acidity in reforestation of Pinus. The multivariate analyses show the change of the agroecosystem of watersheds studied as a function of soil management.

Progression of powdery mildew in susceptible‐resistant wheat (Triticum aestivum) cultivars sown at different dates

AbstractA better understanding of non‐climatic predictors of temporal dynamics of wheat powdery mildew is needed to improve accuracy of disease measurement for breeding and control programmes. The present study examined fourteen types of standard curves to model powdery mildew progress data collected from 282 plots of commercial wheat cultivars sown at different dates over four growing seasons (2013–2017). An exponential model was fitted to disease progress data, and then, relationships between commonly used disease descriptors and exponential parameters were examined. From exponential function, parameters b and r defined disease increase factor and rate of disease increase, respectively. This model described a slow increase of wheat powdery mildew at first, followed by a more rapid increase lacking bound. According to Kruskal–Wallis one‐way ANOVA results, wheat resistance and sowing date were significantly effective on area under disease progress curve (AUDPC), maximum disease severity and exponential parameter b. Greatest mean values for AUDPC, maximum severity and exponential parameter b were detected in later sowings of susceptible cultivars. There were significant correlations between powdery mildew curve elements. Based on principal component analysis, AUDPC, disease onset time, maximum severity and exponential parameters b and r accounted for 77% of total data variance. This information on specific predictors of wheat powdery mildew progress may improve accuracy of future disease estimates in breeding and control programmes.

Diagnosis of liver cancer by FTIR spectra of serum

Liver cancer is the most common fatal malignant tumor in the world. Early diagnosis of liver cancer can improve the survival rate of the patients with liver disease. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with curve fitting and chemometrics was used to distinguish the serum from patients from that of healthy people. The curve fitting results in protein range of 1700–1600 cm−1 showed that there were differences in the secondary structure of protein in serum between the patients with liver cancer and healthy people. Principal component analysis (PCA) in lipid range of 2900–2800 cm−1 could distinguish the serum of patients with liver cancer from that of healthy people. The first two principal components PC1 and PC2 explained 95% of the total data variance. The sensitivity and specificity of partial least squares discriminant analysis (PLS-DA) in lipid range of 2900–2800 cm−1 reached 92.85% and 95.23% respectively. It is shown that FTIR spectroscopy might be developed as an effective method for the diagnosis of liver cancer.

ANÁLISIS DE DATOS MIXTOS PARA SELECCIONAR CLONES DE BANANA (Musa SPP.) A SER INCLUIDOS EN UN BANCO DE GERMOPLASMA

In an asexually reproducing hybrid such as banana (Musa spp.), the assessment of clones in the short term is limited because replications are frequently unavailable in the proper number. The aim of this work is to propose the Multiple Factor Analysis of Mixed Data (MFAmix) as a tool for establishing objective criteria to identify banana clones that preserve variability for qualitative and quantitative variables. In the long term, the aim is the development of a banana germplasm bank. MFAmix was applied on a population composed of 124 banana clones collected from different farmers’ fields and four controls. Two groups of variables related to the agronomic aptitude of the clones were evaluated, one composed of nine quantitative variables, and the other, composed of three dichotomous qualitative variables. A Selection Index (SI) was built from the MFAmix coordinates in order to rank the clones and select a subset that allows to preserve the existing genetic variability. The first two axes of MFAmix explained a 49.47% of the total data variability. The set of the banana clones was successfully characterized based on quantitative and qualitative variables. In the long term, the creation of a banana germplasm bank should consider the height and diameter of the plant, the rachis bunch weight and the hands weight, and the qualitative variable plant leafiness. Key words: asexual hybrid, collection of germplasm, multivariate analysis, Musaceae.

ANÁLISIS DE DATOS MIXTOS PARA SELECCIONAR CLONES DE BANANA (Musa SPP.) A SER INCLUIDOS EN UN BANCO DE GERMOPLASMA

In an asexually reproducing hybrid such as banana (Musa spp.), the assessment of clones in the short term is limited because replications are frequently unavailable in the proper number. The aim of this work is to propose the Multiple Factor Analysis of Mixed Data (MFAmix) as a tool for establishing objective criteria to identify banana clones that preserve variability for qualitative and quantitative variables. In the long term, the aim is the development of a banana germplasm bank. MFAmix was applied on a population composed of 124 banana clones collected from different farmers’ fields and four controls. Two groups of variables related to the agronomic aptitude of the clones were evaluated, one composed of nine quantitative variables, and the other, composed of three dichotomous qualitative variables. A Selection Index (SI) was built from the MFAmix coordinates in order to rank the clones and select a subset that allows to preserve the existing genetic variability. The first two axes of MFAmix explained a 49.47% of the total data variability. The set of the banana clones was successfully characterized based on quantitative and qualitative variables. In the long term, the creation of a banana germplasm bank should consider the height and diameter of the plant, the rachis bunch weight and the hands weight, and the qualitative variable plant leafiness. Key words: asexual hybrid, collection of germplasm, multivariate analysis, Musaceae.