Statistical evaluation of texture analysis from the biocrystallization method

The consumers are becoming more concerned about food quality, especially regarding how, when and where the foods are produced (Haglund et al., 1999; Kahl et al., 2004; Alföldi, et al., 2006). Therefore, during recent years there has been a growing interest in the methods for food quality assessment, especially in the picture-development methods as a complement to traditional chemical analysis of single compounds (Kahl et al., 2006). The biocrystallization as one of the picture-developing method is based on the crystallographic phenomenon that when crystallizing aqueous solutions of dihydrate CuCl2 with adding of organic solutions, originating, e.g., from crop samples, biocrystallograms are generated with reproducible crystal patterns (Kleber & Steinike-Hartung, 1959). Its output is a crystal pattern on glass plates from which different variables (numbers) can be calculated by using image analysis. However, there is a lack of a standardized evaluation method to quantify the morphological features of the biocrystallogram image. Therefore, the main sakes of this research are (1) to optimize an existing statistical model in order to describe all the effects that contribute to the experiment, (2) to investigate the effect of image parameters on the texture analysis of the biocrystallogram images, i.e., region of interest (ROI), color transformation and histogram matching on samples from the project 020E170/F financed by the Federal Ministry of Food, Agriculture and Consumer Protection(BMELV).The samples are wheat and carrots from controlled field and farm trials, (3) to consider the strongest effect of texture parameter with the visual evaluation criteria that have been developed by a group of researcher (University of Kassel, Germany; Louis Bolk Institute (LBI), Netherlands and Biodynamic Research Association Denmark (BRAD), Denmark) in order to clarify how the relation of the texture parameter and visual characteristics on an image is. The refined statistical model was accomplished by using a lme model with repeated measurements via crossed effects, programmed in R (version 2.1.0). The validity of the F and P values is checked against the SAS program. While getting from the ANOVA the same F values, the P values are bigger in R because of the more conservative approach. The refined model is calculating more significant P values. The optimization of the image analysis is dealing with the following parameters: ROI(Region of Interest which is the area around the geometrical center), color transformation (calculation of the 1 dimensional gray level value out of the three dimensional color information of the scanned picture, which is necessary for the texture analysis), histogram matching (normalization of the histogram of the picture to enhance the contrast and to minimize the errors from lighting conditions). The samples were wheat from DOC trial with 4 field replicates for the years 2003 and 2005, “market samples”(organic and conventional neighbors with the same variety) for 2004 and 2005, carrot where the samples were obtained from the University of Kassel (2 varieties, 2 nitrogen treatments) for the years 2004, 2005, 2006 and “market samples” of carrot for the years 2004 and 2005. The criterion for the optimization was repeatability of the differentiation of the samples over the different harvest(years). For different samples different ROIs were found, which reflect the different pictures. The best color transformation that shows efficiently differentiation is relied on gray scale, i.e., equal color transformation. The second dimension of the color transformation only appeared in some years for the effect of color wavelength(hue) for carrot treated with different nitrate fertilizer levels. The best histogram matching is the Gaussian distribution. The approach was to find a connection between the variables from textural image analysis with the different visual criteria. The relation between the texture parameters and visual evaluation criteria was limited to the carrot samples, especially, as it could be well differentiated by the texture analysis. It was possible to connect groups of variables of the texture analysis with groups of criteria from the visual evaluation. These selected variables were able to differentiate the samples but not able to classify the samples according to the treatment. Contrarily, in case of visual criteria which describe the picture as a whole there is a classification in 80% of the sample cases possible. Herewith, it clearly can find the limits of the single variable approach of the image analysis (texture analysis).

Statistical Analysis of Diabetes Mellitus

Diabetes mellitus is a disease where the glucosis-content of the blood does not automatically decrease to a ”normal” value between 70 mg/dl and 120 mg/dl (3,89 mmol/l and 6,67 mmol/l) between perhaps one hour (or two hours) after eating. Several instruments can be used to arrive at a relative low increase of the glucosis-content. Besides drugs (oral antidiabetica, insulin) the blood-sugar content can mainly be influenced by (i) eating, i.e., consumption of the right amount of food at the right time (ii) physical training (walking, cycling, swimming). In a recent paper the author has performed a regression analysis on the influence of eating during the night. The result was that one ”bread-unit” (12g carbon-hydrats) increases the blood-sugar by about 50 mg/dl, while one hour after eating the blood-sugar decreases by about 10 mg/dl per hour. By applying this result-assuming its correctness - it is easy to eat the right amount during the night and to arrive at a fastening blood-sugar (glucosis-content) in the morning of about 100 mg/dl (5,56 mmol/l). In this paper we try to incorporate some physical exercise into the model.

Verbal proficiency as fitness indicator: Experimental and comparative research on the evolutionary psychology of language and verbal displays

Summary: Recent research on the evolution of language and verbal displays (e.g., Miller, 1999, 2000a, 2000b, 2002) indicated that language is not only the result of natural selection but serves as a sexually-selected fitness indicator that is an adaptation showing an individual’s suitability as a reproductive mate. Thus, language could be placed within the framework of concepts such as the handicap principle (Zahavi, 1975). There are several reasons for this position: Many linguistic traits are highly heritable (Stromswold, 2001, 2005), while naturally-selected traits are only marginally heritable (Miller, 2000a); men are more prone to verbal displays than women, who in turn judge the displays (Dunbar, 1996; Locke & Bogin, 2006; Lange, in press; Miller, 2000a; Rosenberg & Tunney, 2008); verbal proficiency universally raises especially male status (Brown, 1991); many linguistic features are handicaps (Miller, 2000a) in the Zahavian sense; most literature is produced by men at reproduction-relevant age (Miller, 1999). However, neither an experimental study investigating the causal relation between verbal proficiency and attractiveness, nor a study showing a correlation between markers of literary and mating success existed. In the current studies, it was aimed to fill these gaps. In the first one, I conducted a laboratory experiment. Videos in which an actor and an actress performed verbal self-presentations were the stimuli for counter-sex participants. Content was always alike, but the videos differed on three levels of verbal proficiency. Predictions were, among others, that (1) verbal proficiency increases mate value, but that (2) this applies more to male than to female mate value due to assumed past sex-different selection pressures causing women to be very demanding in mate choice (Trivers, 1972). After running a two-factorial analysis of variance with the variables sex and verbal proficiency as factors, the first hypothesis was supported with high effect size. For the second hypothesis, there was only a trend going in the predicted direction. Furthermore, it became evident that verbal proficiency affects long-term more than short-term mate value. In the second study, verbal proficiency as a menstrual cycle-dependent mate choice criterion was investigated. Basically the same materials as in the former study were used with only marginal changes in the used questionnaire. The hypothesis was that fertile women rate high verbal proficiency in men higher than non-fertile women because of verbal proficiency being a potential indicator of “good genes”. However, no significant result could be obtained in support of the hypothesis in the current study. In the third study, the hypotheses were: (1) most literature is produced by men at reproduction-relevant age. (2) The more works of high literary quality a male writer produces, the more mates and children he has. (3) Lyricists have higher mating success than non-lyric writers because of poetic language being a larger handicap than other forms of language. (4) Writing literature increases a man’s status insofar that his offspring shows a significantly higher male-to-female sex ratio than in the general population, as the Trivers-Willard hypothesis (Trivers & Willard, 1973) applied to literature predicts. In order to test these hypotheses, two famous literary canons were chosen. Extensive biographical research was conducted on the writers’ mating successes. The first hypothesis was confirmed; the second one, controlling for life age, only for number of mates but not entirely regarding number of children. The latter finding was discussed with respect to, among others, the availability of effective contraception especially in the 20th century. The third hypothesis was not satisfactorily supported. The fourth hypothesis was partially supported. For the 20th century part of the German list, the secondary sex ratio differed with high statistical significance from the ratio assumed to be valid for a general population.

Parameter identification of structural dynamic models by inverse statistical analysis

Auf dem Gebiet der Strukturdynamik sind computergestützte Modellvalidierungstechniken inzwischen weit verbreitet. Dabei werden experimentelle Modaldaten, um ein numerisches Modell für weitere Analysen zu korrigieren. Gleichwohl repräsentiert das validierte Modell nur das dynamische Verhalten der getesteten Struktur. In der Realität gibt es wiederum viele Faktoren, die zwangsläufig zu variierenden Ergebnissen von Modaltests führen werden: Sich verändernde Umgebungsbedingungen während eines Tests, leicht unterschiedliche Testaufbauten, ein Test an einer nominell gleichen aber anderen Struktur (z.B. aus der Serienfertigung), etc. Damit eine stochastische Simulation durchgeführt werden kann, muss eine Reihe von Annahmen für die verwendeten Zufallsvariablengetroffen werden. Folglich bedarf es einer inversen Methode, die es ermöglicht ein stochastisches Modell aus experimentellen Modaldaten zu identifizieren. Die Arbeit beschreibt die Entwicklung eines parameter-basierten Ansatzes, um stochastische Simulationsmodelle auf dem Gebiet der Strukturdynamik zu identifizieren. Die entwickelte Methode beruht auf Sensitivitäten erster Ordnung, mit denen Parametermittelwerte und Kovarianzen des numerischen Modells aus stochastischen experimentellen Modaldaten bestimmt werden können.