Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Multivariate analysis of fresh-cut carambola slices stored under different temperatures.

Quality of fresh-cut carambola (Averrhoa carambola L) is related to many chemical and biochemical variables especially those involved with softening and browning, both influenced by storage temperature. To study these effects, a multivariate analysis was used to evaluate slices packaged in vacuum-sealed polyolefin bags, and stored at 2.5 degrees C, 5 degrees C and 10 degrees C, for up to 16 d. The quality of slices at each temperature was correlated with the duration of storage, O(2) and CO(2) concentration in the package, physical chemical constituents, and activity of enzymes involved in softening (PG) and browning (PPO) metabolism. Three quality groups were identified by hierarchical cluster analysis, and the classification of the components within each of these groups was obtained from a principal component analysis (PCA). The characterization of samples by PCA clearly distinguished acceptable and non-acceptable slices. According to PCA, acceptable slices presented higher ascorbic acid content, greater hue angles ((o)h) and final lightness (L-5) in the first principal component (PC1). On the other hand, non-acceptable slices presented higher total pectin content. PPO activity in the PC1. Non-acceptable slices also presented higher soluble pectin content, increased pectin solubilisation and higher CO(2) concentration in the second principal component (PC2) whereas acceptable slices showed lower total sugar content. The hierarchical cluster and PCA analyses were useful for discriminating the quality of slices stored at different temperatures.

Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0.69 to 0.88 with correlations among harvest seasons within trials greater than 0.96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G x E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.

Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering

The family of location and scale mixtures of Gaussians has the ability to generate a number of flexible distributional forms. The family nests as particular cases several important asymmetric distributions like the Generalized Hyperbolic distribution. The Generalized Hyperbolic distribution in turn nests many other well known distributions such as the Normal Inverse Gaussian. In a multivariate setting, an extension of the standard location and scale mixture concept is proposed into a so called multiple scaled framework which has the advantage of allowing different tail and skewness behaviours in each dimension with arbitrary correlation between dimensions. Estimation of the parameters is provided via an EM algorithm and extended to cover the case of mixtures of such multiple scaled distributions for application to clustering. Assessments on simulated and real data confirm the gain in degrees of freedom and flexibility in modelling data of varying tail behaviour and directional shape.

A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering

We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

How many inpatients in our hospitals have foot complications? The foot disease in inpatients study

Background Foot complications have been found to affect large proportions of hospital in patients with diabetes. However, no studies have investigated the proportion of foot complications affecting all people in general inpatient populations. The aims of this cross-sectional study were to investigate the point-prevalence of different foot complications in general inpatient populations, analyse differences in diabetes and non-diabetes sub-groups, and examine characteristics of people primarily admitted for a foot complication. Methods Eligible participants were all adults admitted overnight, for any reason, into five diverse hospitals on one day; excluding maternity, mental health and cognitively impaired patients. All participants underwent a physical foot examination, by trained podiatrists using validated measures, to clinically diagnose different foot complications; including foot wounds, infections, deformity, peripheral arterial disease (PAD) and peripheral neuropathy (PN). Data were also collected on participants' primary reason for admission and a range of demographic, social determinant, medical history, foot complication history, self-care and footwear risk factors. Results Overall, 733 participants consented (83% of eligible participants); mean(±SD) age 62(±19) years, 480 (55.8%) male and 172 (23.5%) had diabetes. Foot complication prevalence included: wounds 9.0% (95% CI) (5.1-8.7), infections 3.3% (2.2-4.9), deformity 22.4% (19.5-26.7), PAD 21.0% (18.2-24.1) and PN 22.0% (19.1-25.1). Diabetes populations had significantly more foot complications than non-diabetes (p < 0.01); wounds (15.7% vs 7.0%), infections (7.1% vs 2.2%), deformity (30.5% vs 19.9%), PAD (35.1% vs 16.7%) and PN (43.3% vs 15.4%). Foot complications were the primary reason for admission in 7.4% (95% CI) (5.7-9.5) of all participants. In a backwards stepwise multivariate analysis having a foot complication as the primary reason for admission was independently associated (OR (95% CI) with foot wounds (18.9 (7.3-48.7)), foot infections (6.0 (1.6-22.4)), history of amputation (4.7 (1.3-17.0) and PAD (2.9 (1.3-6.6)). Conclusions Findings of this study indicate one in every ten hospital inpatients had an active foot wound or infection. In patients with diabetes had significantly higher proportions of foot complications than non-diabetes inpatients. Remarkably one in every thirteen inpatients in this study were primarily hospitalised for a foot complication. Further research and policy is required to tackle this seemingly large inpatient foot complication burden.

Determinants of institutional care at older ages in Finland

Väitöskirjassa selvitettiin ikäihmisten laitoshoitoon siirtymisen todennäköisyyttä ja sen taustoja kansainvälisesti ainutlaatuisen rekisteriaineiston avulla. Selvitettäviä asioita olivat eri sairauksien, sosioekonomisten tekijöiden, puolison olemassaolon ja leskeksi jäämisen yhteys laitoshoitoon siirtymiseen yli 65-vuotiailla suomalaisilla. Tutkimuksessa havaittiin, että dementia, Parkinsonin tauti, aivohalvaus, masennusoireet ja muut mielenterveysongelmat, lonkkamurtuma sekä diabetes lisäsivät ikäihmisten todennäköisyyttä siirtyä laitoshoitoon yli 50 prosentilla, kun muut sairaudet ja sosiodemografiset tekijät oli otettu huomioon. Korkeat tulot vähensivät laitoshoidon todennäköisyyttä, kun taas puutteellinen asuminen (ilman peseytymistiloja tai keskus- tai sähkölämmitystä) sekä erittäin puutteellinen asuminen (ilman lämmintä vettä, vesijohtoa, viemäriä tai vesivessaa) lisäsivät todennäkösyyttä, kun muut sosiodemografiset tekijät, sairaudet ja asuinalue oli huomioitu. Kerrostalon hissittömyys ei ollut yhteydessä laitoshoidon todennäköisyyteen. Todennäköisyys siirtyä laitoshoitoon oli jostain syystä korkeampaa niillä ikäihmisillä, jotka asuivat vuokralla ja matalampaa omakotitalossa asuvilla ja niillä, joilla oli auto. Puolison olemassaolo vähensi ja leskeksi jääminen lisäsi laitoshoidon todennäköisyyttä huomattavasti. Todennäköisyys oli erityisen suuri, yli kolminkertainen, kun puolison kuolemasta oli kulunut enintään kuukausi verrattuna niihin, joiden puoliso oli elossa. Todennäköisyys laski, kun puolison kuolemasta kului aikaa. Miesten ja naisten tulokset olivat samansuuntaisia. Korkeat tulot tai koulutus eivät suojanneet riskiltä joutua laitoshoitoon puolison kuoltua. Puolison kuolema näyttää lisäävän hoidon tarvetta, kun kotona ei ole enää puolisoa tukemassa ja huolehtimassa kodin askareista. Laitoshoidon tarve vähenee, jos ja kun lesket ajan kuluessa oppivat elämään yksin. Toisaalta tutkimustulokset saattavat viitata myös siihen, että kaikkein huonokuntoisimmat lesket, jotka eivät pärjää yksin asuessaan, siirtyvät laitoshoitoon hyvin nopeasti puolison kuoltua. Tutkimuksessa oli mukana yhteensä yli 280 000 yli 65-vuotiasta henkilöä, joiden pitkäaikaiseen laitoshoitoon siirtymistä seurattiin tammikuusta 1998 syyskuuhun 2003. Laitoshoidoksi määriteltiin terveyskeskuksissa, sairaaloissa ja vanhainkodeissa tai vastaavissa yksiköissä tapahtuva pitkäaikainen hoito, joka kesti yli 90 vuorokautta tai oli vahvistettu pitkäaikaishoidon päätöksellä. Tutkimuksessa käytetty aineisto koottiin väestörekistereistä, sosiaali- ja terveydenhuollon rekistereistä ja lääkerekistereistä.

Multivariate Techniques for Identifying Diffractive Interactions at the LHC

Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.

Local systems, global impacts : Using life cycle assessment to analyse the potential and constraints of industrial symbioses

Human activities extract and displace different substances and materials from the earth s crust, thus causing various environmental problems, such as climate change, acidification and eutrophication. As problems have become more complicated, more holistic measures that consider the origins and sources of pollutants have been called for. Industrial ecology is a field of science that forms a comprehensive framework for studying the interactions between the modern technological society and the environment. Industrial ecology considers humans and their technologies to be part of the natural environment, not separate from it. Industrial operations form natural systems that must also function as such within the constraints set by the biosphere. Industrial symbiosis (IS) is a central concept of industrial ecology. Industrial symbiosis studies look at the physical flows of materials and energy in local industrial systems. In an ideal IS, waste material and energy are exchanged by the actors of the system, thereby reducing the consumption of virgin material and energy inputs and the generation of waste and emissions. Companies are seen as part of the chains of suppliers and consumers that resemble those of natural ecosystems. The aim of this study was to analyse the environmental performance of an industrial symbiosis based on pulp and paper production, taking into account life cycle impacts as well. Life Cycle Assessment (LCA) is a tool for quantitatively and systematically evaluating the environmental aspects of a product, technology or service throughout its whole life cycle. Moreover, the Natural Step Sustainability Principles formed a conceptual framework for assessing the environmental performance of the case study symbiosis (Paper I). The environmental performance of the case study symbiosis was compared to four counterfactual reference scenarios in which the actors of the symbiosis operated on their own. The research methods used were process-based life cycle assessment (LCA) (Papers II and III) and hybrid LCA, which combines both process and input-output LCA (Paper IV). The results showed that the environmental impacts caused by the extraction and processing of the materials and the energy used by the symbiosis were considerable. If only the direct emissions and resource use of the symbiosis had been considered, less than half of the total environmental impacts of the system would have been taken into account. When the results were compared with the counterfactual reference scenarios, the net environmental impacts of the symbiosis were smaller than those of the reference scenarios. The reduction in environmental impacts was mainly due to changes in the way energy was produced. However, the results are sensitive to the way the reference scenarios are defined. LCA is a useful tool for assessing the overall environmental performance of industrial symbioses. It is recommended that in addition to the direct effects, the upstream impacts should be taken into account as well when assessing the environmental performance of industrial symbioses. Industrial symbiosis should be seen as part of the process of improving the environmental performance of a system. In some cases, it may be more efficient, from an environmental point of view, to focus on supply chain management instead.  

Thermodynamic modeling to analyse composition of carbonaceous coatings of MnO and other oxides of manganese grown by MOCVD

Equilibrium thermodynamic analysis has been applied to the low-pressure MOCVD process using manganese acetylacetonate as the precursor. ``CVD phase stability diagrams'' have been constructed separately for the processes carried out in argon and oxygen ambient, depicting the compositions of the resulting films as functions of CVD parameters. For the process conduced in argon ambient, the analysis predicts the simultaneous deposition of MnO and elemental carbon in 1: 3 molar proportion, over a range of temperatures. The analysis predicts also that, if CVD is carried out in oxygen ambient, even a very low flow of oxygen leads to the complete absence of carbon in the film deposited oxygen, with greater oxygen flow resulting in the simultaneous deposition of two different manganese oxides under certain conditions. The results of thermodynamic modeling have been verified quantitatively for low-pressure CVD conducted in argon ambient. Indeed, the large excess of carbon in the deposit is found to constitute a MnO/C nanocomposite, the associated cauliflower-like morphology making it a promising candidate for electrode material in supercapacitors. CVD carried out in oxygen flow, under specific conditions, leads to the deposition of more than one manganese oxide, as expected from thermodynamic analysis ( and forming an oxide-oxide nanocomposite). These results together demonstrate that thermodynamic analysis of the MOCVD process can be employed to synthesize thin films in a predictive manner, thus avoiding the inefficient trial-and-error method usually associated with MOCVD process development. The prospect of developing thin films of novel compositions and characteristics in a predictive manner, through the appropriate choice of CVD precursors and process conditions, emerges from the present work.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

Spin State Selective Excitation of Single Quantum Transitions Using Multiple Quantum Spectroscopy- an Aid to Analyse Complex Proton NMR spectra of Oriented Molecules

NMR spectra of molecules oriented in liquid-crystalline matrix provide information on the structure and orientation of the molecules. Thermotropic liquid crystals used as an orienting media result in the spectra of spins that are generally strongly coupled. The number of allowed transitions increases rapidly with the increase in the number of interacting spins. Furthermore, the number of single quantum transitions required for analysis is highly redundant. In the present study, we have demonstrated that it is possible to separate the subspectra of a homonuclear dipolar coupled spin system on the basis of the spin states of the coupled heteronuclei by multiple quantum (MQ)−single quantum (SQ) correlation experiments. This significantly reduces the number of redundant transitions, thereby simplifying the analysis of the complex spectrum. The methodology has been demonstrated on the doubly 13C labeled acetonitrile aligned in the liquid-crystal matrix and has been applied to analyze the complex spectrum of an oriented six spin system.