Charakterisierung von plasmafunktionalisierten Oberflächen mittels ToF-SIMS und multivariaten Analysemethoden

Die grundlegenden Prinzipien und Möglichkeiten der Oberflächencharakterisierung mittels ToF-SIMS (Flugzeit-Sekundärionen Massenspektrometrie) werden an ausgewählten Beispielen aus einem aktuell laufenden und vom BMBF geförderten Verbundforschungsprojekt (Fkz: 03N8022A) zum Thema Nanofunktionalisierung von Grenzflächen vorgestellt. Ein Schwerpunkt innerhalb des Projekts stellen die nichtgeschlossenen Schichtsysteme dar, die entweder über Domänenstrukturen oder einer definierten Einzelfunktionalisierung neuartige funktionelle Oberflächen bereitstellen. Mithilfe der sehr oberflächensensitiven ToF-SIMS Methode sowie der Möglichkeit einer graphischen Darstellung lateraler Molekülionenverteilungen auf funktionalisierten Oberflächen können Informationen über Struktur und Belegungsdichte der Funktionsschicht gewonnen werden. Die Kombination des ToF-SIMS Experimentes und eines multivariaten Algorithmus (partial least squares, PLS) liefert eine interessante Möglichkeit zur quantitativen und simultanen Bestimmung von Oberflächeneigenschaften (Element- und molekulare Konzentrationen sowie Kontaktwinkelwerte). Da das ToF-SIMS Spektrum einer plasmafunktionalisierten Oberfläche im Allgemeinen eine Vielzahl unterschiedlicher Fragmentsignale enthält, lässt eine einfache eindimensionale Korrelation (z.B. CF3 - Fragmentintensität ßà CF3-Konzentration) den größten Teil der im Spektrum prinzipiell enthaltenen Information unberücksichtigt. Aufgrund der großen Anzahl von atomaren und molekularen Signalen, die repräsentativ für die chemische Struktur der analysierten Oberflächen sind, ist es sinnvoll, diese Fülle von Informationen zur Quantifizierung der Oberflächeneigenschaften (Elementkonzentrationen, Kontaktwinkel etc.) zu verwenden. Zusätzlich ermöglicht diese Methode eine quantitative Bestimmung der Oberflächeneigenschaften auf nur µm-großen Bereichen. Das ist vorteilhaft für Untersuchungen chemisch strukturierter Oberflächen, da die Größe der Strukturierung für viele Anwendungen in einem Bereich von mehreren µm liegt. Anhand eines Beispieles aus dem biologisch-medizinischen Fachgebiet, soll der erfolgreiche Einsatz multivariater Modelle aufgezeigt werden. In diesem Experiment wurden menschlichen Bindegewebs- (Fibroblasten) und Pankreaszellen auf plasmafunktionalisiserten Oberflächen kultiviert, um die Beeinflussung der Funktionalisierung auf das Zellwachstum zu untersuchen. Die plasmabehandelten Oberflächen wurden durch die Verwendung von TEM-Gittern mit µm-großen Öffnungen chemisch strukturiert und das Wachstumsverhalten der Zellen beobachtet. Jedem dieser µm-großen Bereiche können mithilfe der multivariaten Modelle quantitative Größen zugeordnet werden (Konzentrationen und Kontaktwinkelwerte), die zur Interpretation des Wachstumsverhaltens der Zellen beitragen.

Reinforcement Lernen mit Regularisierungsnetzwerken

Die Arbeit behandelt das Problem der Skalierbarkeit von Reinforcement Lernen auf hochdimensionale und komplexe Aufgabenstellungen. Unter Reinforcement Lernen versteht man dabei eine auf approximativem Dynamischen Programmieren basierende Klasse von Lernverfahren, die speziell Anwendung in der Künstlichen Intelligenz findet und zur autonomen Steuerung simulierter Agenten oder realer Hardwareroboter in dynamischen und unwägbaren Umwelten genutzt werden kann. Dazu wird mittels Regression aus Stichproben eine Funktion bestimmt, die die Lösung einer "Optimalitätsgleichung" (Bellman) ist und aus der sich näherungsweise optimale Entscheidungen ableiten lassen. Eine große Hürde stellt dabei die Dimensionalität des Zustandsraums dar, die häufig hoch und daher traditionellen gitterbasierten Approximationsverfahren wenig zugänglich ist. Das Ziel dieser Arbeit ist es, Reinforcement Lernen durch nichtparametrisierte Funktionsapproximation (genauer, Regularisierungsnetze) auf -- im Prinzip beliebig -- hochdimensionale Probleme anwendbar zu machen. Regularisierungsnetze sind eine Verallgemeinerung von gewöhnlichen Basisfunktionsnetzen, die die gesuchte Lösung durch die Daten parametrisieren, wodurch die explizite Wahl von Knoten/Basisfunktionen entfällt und so bei hochdimensionalen Eingaben der "Fluch der Dimension" umgangen werden kann. Gleichzeitig sind Regularisierungsnetze aber auch lineare Approximatoren, die technisch einfach handhabbar sind und für die die bestehenden Konvergenzaussagen von Reinforcement Lernen Gültigkeit behalten (anders als etwa bei Feed-Forward Neuronalen Netzen). Allen diesen theoretischen Vorteilen gegenüber steht allerdings ein sehr praktisches Problem: der Rechenaufwand bei der Verwendung von Regularisierungsnetzen skaliert von Natur aus wie O(n**3), wobei n die Anzahl der Daten ist. Das ist besonders deswegen problematisch, weil bei Reinforcement Lernen der Lernprozeß online erfolgt -- die Stichproben werden von einem Agenten/Roboter erzeugt, während er mit der Umwelt interagiert. Anpassungen an der Lösung müssen daher sofort und mit wenig Rechenaufwand vorgenommen werden. Der Beitrag dieser Arbeit gliedert sich daher in zwei Teile: Im ersten Teil der Arbeit formulieren wir für Regularisierungsnetze einen effizienten Lernalgorithmus zum Lösen allgemeiner Regressionsaufgaben, der speziell auf die Anforderungen von Online-Lernen zugeschnitten ist. Unser Ansatz basiert auf der Vorgehensweise von Recursive Least-Squares, kann aber mit konstantem Zeitaufwand nicht nur neue Daten sondern auch neue Basisfunktionen in das bestehende Modell einfügen. Ermöglicht wird das durch die "Subset of Regressors" Approximation, wodurch der Kern durch eine stark reduzierte Auswahl von Trainingsdaten approximiert wird, und einer gierigen Auswahlwahlprozedur, die diese Basiselemente direkt aus dem Datenstrom zur Laufzeit selektiert. Im zweiten Teil übertragen wir diesen Algorithmus auf approximative Politik-Evaluation mittels Least-Squares basiertem Temporal-Difference Lernen, und integrieren diesen Baustein in ein Gesamtsystem zum autonomen Lernen von optimalem Verhalten. Insgesamt entwickeln wir ein in hohem Maße dateneffizientes Verfahren, das insbesondere für Lernprobleme aus der Robotik mit kontinuierlichen und hochdimensionalen Zustandsräumen sowie stochastischen Zustandsübergängen geeignet ist. Dabei sind wir nicht auf ein Modell der Umwelt angewiesen, arbeiten weitestgehend unabhängig von der Dimension des Zustandsraums, erzielen Konvergenz bereits mit relativ wenigen Agent-Umwelt Interaktionen, und können dank des effizienten Online-Algorithmus auch im Kontext zeitkritischer Echtzeitanwendungen operieren. Wir demonstrieren die Leistungsfähigkeit unseres Ansatzes anhand von zwei realistischen und komplexen Anwendungsbeispielen: dem Problem RoboCup-Keepaway, sowie der Steuerung eines (simulierten) Oktopus-Tentakels.

Entrepreneurship influential factors in development of rural tourism as diversified rural activity in Republic of Macedonia

Rural tourism is relatively new product in the process of diversification of the rural economy in Republic of Macedonia. This study used desk research and life story interviews of rural tourism entrepreneurs as qualitative research method to identify prevalent success influential factors. Further quantitative analysis was applied in order to measure the strength of influence of identified success factors. The primary data for the quantitative research was gathered using telephone questionnaire composed of 37 questions with 5-points Likert scale. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) by SmartPLS 3.1.6. Results indicated that human capital, social capital, entrepreneurial personality and external business environment are predominant influential success factors. However, human capital has non-significant direct effect on success (p 0.493) nonetheless the effect was indirect with high level of partial mediation through entrepreneurial personality as mediator (VAF 73%). Personality of the entrepreneur, social capital and business environment have direct positive affect on entrepreneurial success (p 0.001, 0.003 and 0.045 respectably). Personality also mediates the positive effect of social capital on entrepreneurial success (VAF 28%). Opposite to the theory the data showed no interaction between social and human capital on the entrepreneurial success. This research suggests that rural tourism accommodation entrepreneurs could be more successful if there is increased support in development of social capital in form of conservation of cultural heritage and natural attractions. Priority should be finding the form to encourage and support the establishment of formal and informal associations of entrepreneurs in order to improve the conditions for management and marketing of the sector. Special support of family businesses in the early stages of the operation would have a particularly positive impact on the success of rural tourism. Local infrastructure, access to financial instruments, destination marketing and entrepreneurial personality have positive effect on success.

Compressed sensing in digital tomosynthesis reconstruction

In this work we study a model for the breast image reconstruction in Digital Tomosynthesis, that is a non-invasive and non-destructive method for the three-dimensional visualization of the inner structures of an object, in which the data acquisition includes measuring a limited number of low-dose two-dimensional projections of an object by moving a detector and an X-ray tube around the object within a limited angular range. The problem of reconstructing 3D images from the projections provided in the Digital Tomosynthesis is an ill-posed inverse problem, that leads to a minimization problem with an object function that contains a data fitting term and a regularization term. The contribution of this thesis is to use the techniques of the compressed sensing, in particular replacing the standard least squares problem of data fitting with the problem of minimizing the 1-norm of the residuals, and using as regularization term the Total Variation (TV). We tested two different algorithms: a new alternating minimization algorithm (ADM), and a version of the more standard scaled projected gradient algorithm (SGP) that involves the 1-norm. We perform some experiments and analyse the performance of the two methods comparing relative errors, iterations number, times and the qualities of the reconstructed images. In conclusion we noticed that the use of the 1-norm and the Total Variation are valid tools in the formulation of the minimization problem for the image reconstruction resulting from Digital Tomosynthesis and the new algorithm ADM has reached a relative error comparable to a version of the classic algorithm SGP and proved best in speed and in the early appearance of the structures representing the masses.

Dynamic registration using ultrasound for anatomical referencing

This paper proposes methods to circumvent the need to attach physical markers to bones for anatomical referencing in computer-assisted orthopedic surgery. Using ultrasound, a bone could be non-invasively referenced, and so the problem is formulated as the need for dynamic registration. A method for correspondence establishment is presented, and the matching step is based on three least-squares algorithms: two that are typically used in registration methods such as ICP, and the third is a form of the Unscented Kalman filter that was adapted to work in this context. A simulation was developed in order to reliably evaluate and compare the dynamic registration methods

Impact of different cultivation methods on the metabolic profile of apples studied by 1H HR-MAS NMR spectroscopy

The cultivation of dessert apples has to meet the consumer's increasing demand for high fruit quality and a sustainable mostly residue-free production while ensuring a competitive agricultural productivity. It is therefore of great interest to know the impact of different cultivation methods on the fruit quality and the chemical composition, respectively. Previous studies have demonstrated the feasibility of High Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy directly performed on apple tissue as analytical tool for metabonomic studies. In this study, HR-MAS NMR spectroscopy is applied to apple tissue to analyze the metabolic profiles of apples grown under 3 different cultivation methods. Golden Delicious apples were grown applying organic (Bio), integrated (IP) and low-input (LI) plant protection strategies. A total of 70 1H HR-MAS NMR spectra were analyzed by means of principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Apples derived from Bio-production could be well separated from the two other cultivation methods applying both, PCA and PLS-DA. Apples obtained from integrated (IP) and low-input (LI) production discriminated when taking the third PLS-component into account. The identified chemical composition and the compounds responsible for the separation, i.e. the PLS-loadings, are discussed. The results are compared with fruit quality parameters assessed by conventional methods. The present study demonstrates the potential of HR-MAS NMR spectroscopy of fruit tissue as analytical tool for finding markers for specific fruit production conditions like the cultivation method.

Association of optical tracklets from a geosynchronous belt survey via the direct Bayesian admissible region approach

The direct Bayesian admissible region approach is an a priori state free measurement association and initial orbit determination technique for optical tracks. In this paper, we test a hybrid approach that appends a least squares estimator to the direct Bayesian method on measurements taken at the Zimmerwald Observatory of the Astronomical Institute at the University of Bern. Over half of the association pairs agreed with conventional geometric track correlation and least squares techniques. The remaining pairs cast light on the fundamental limits of conducting tracklet association based solely on dynamical and geometrical information.

The Discontinuous Nature of Kriging Interpolation for Digital Terrain Modeling

Kriging is a widely employed method for interpolating and estimating elevations from digital elevation data. Its place of prominence is due to its elegant theoretical foundation and its convenient practical implementation. From an interpolation point of view, kriging is equivalent to a thin-plate spline and is one species among the many in the genus of weighted inverse distance methods, albeit with attractive properties. However, from a statistical point of view, kriging is a best linear unbiased estimator and, consequently, has a place of distinction among all spatial estimators because any other linear estimator that performs as well as kriging (in the least squares sense) must be equivalent to kriging, assuming that the parameters of the semivariogram are known. Therefore, kriging is often held to be the gold standard of digital terrain model elevation estimation. However, I prove that, when used with local support, kriging creates discontinuous digital terrain models, which is to say, surfaces with “rips” and “tears” throughout them. This result is general; it is true for ordinary kriging, kriging with a trend, and other forms. A U.S. Geological Survey (USGS) digital elevation model was analyzed to characterize the distribution of the discontinuities. I show that the magnitude of the discontinuity does not depend on surface gradient but is strongly dependent on the size of the kriging neighborhood.

Interrelationships between the structural and functional aspects of social relationships, comorbidity and health related quality of life in breast cancer survivors

It is widely acknowledged in theoretical and empirical literature that social relationships, comprising of structural measures (social networks) and functional measures (perceived social support) have an undeniable effect on health outcomes. However, the actual mechanism of this effect has yet to be clearly understood or explicated. In addition, comorbidity is found to adversely affect social relationships and health related quality of life (a valued outcome measure in cancer patients and survivors). ^ This cross sectional study uses selected baseline data (N=3088) from the Women's Healthy Eating and Living (WHEL) study. Lisrel 8.72 was used for the latent variable structural equation modeling. Due to the ordinal nature of the data, Weighted Least Squares (WLS) method of estimation using Asymptotic Distribution Free covariance matrices was chosen for this analysis. The primary exogenous predictor variables are Social Networks and Comorbidity; Perceived Social Support is the endogenous predictor variable. Three dimensions of HRQoL, physical, mental and satisfaction with current quality of life were the outcome variables. ^ This study hypothesizes and tests the mechanism and pathways between comorbidity, social relationships and HRQoL using latent variable structural equation modeling. After testing the measurement models of social networks and perceived social support, a structural model hypothesizing associations between the latent exogenous and endogenous variables was tested. The results of the study after listwise deletion (N=2131) mostly confirmed the hypothesized relationships (TLI, CFI >0.95, RMSEA = 0.05, p=0.15). Comorbidity was adversely associated with all three HRQoL outcomes. Strong ties were negatively associated with perceived social support; social network had a strong positive association with perceived social support, which served as a mediator between social networks and HRQoL. Mental health quality of life was the most adversely affected by the predictor variables. ^ This study is a preliminary look at the integration of structural and functional measures of social relationships, comorbidity and three HRQoL indicators using LVSEM. Developing stronger social networks and forming supportive relationships is beneficial for health outcomes such as HRQoL of cancer survivors. Thus, the medical community treating cancer survivors as well as the survivor's social networks need to be informed and cognizant of these possible relationships. ^

Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development. ^

Current meter data from mooring stations in the English Channel (Table 1)

A new technique for the harmonic analysis of current observations is described. It consists in applying a linear band pass filter which separates the various species and removes the contribution of non-tidal effects at intertidal frequencies. The tidal constituents are then evaluated through the method of least squares. In spite of the narrowness of the filter, only three days of data are lost through the filtering procedure and the only requirement on the data is that the time interval between samples be an integer fraction of one day. This technique is illustrated through the analysis of a few French current observations from the English Channel within the framework of INOUT. The characteristics of the main tidal constituents are given.

Pollen record and calculated holocene vegetation and climate dynamics from Sihailongwan Maar Lake, NE China

High-resolution palynological analysis on annually laminated sediments of Sihailongwan Maar Lake (SHL) provides new insights into the Holocene vegetation and climate dynamics of NE China. The robust chronology of the presented record is based on varve counting and AMS radiocarbon dates from terrestrial plant macro-remains. In addition to the qualitative interpretation of the pollen data, we provide quantitative reconstructions of vegetation and climate based on the method of biomization and weighted averaging partial least squares regression (WA-PLS) technique, respectively. Power spectra were computed to investigate the frequency domain distribution of proxy signals and potential natural periodicities. Pollen assemblages, pollen-derived biome scores and climate variables as well as the cyclicity pattern indicate that NE China experienced significant changes in temperature and moisture conditions during the Holocene. Within the earliest phase of the Holocene, a large-scale reorganization of vegetation occurred, reflecting the reconstructed shift towards higher temperatures and precipitation values and the initial Holocene strengthening and northward expansion of the East Asian summer monsoon (EASM). Afterwards, summer temperatures remain at a high level, whereas the reconstructed precipitation shows an increasing trend until approximately 4000 cal. yr BP. Since 3500 cal. yr BP, temperature and precipitation values decline, indicating moderate cooling and weakening of the EASM. A distinct periodicity of 550-600 years and evidence of a Mid-Holocene transition from a temperature-triggered to a predominantly moisture-triggered climate regime are derived from the power spectra analysis. The results obtained from SHL are largely consistent with other palaeoenvironmental records from NE China, substantiating the regional nature of the reconstructed vegetation and climate patterns. However, the reconstructed climate changes contrast with the moisture evolution recorded in S China and the mid-latitude (semi-)arid regions of N China. Whereas a clear insolation-related trend of monsoon intensity over the Holocene is lacking from the SHL record, variations in the coupled atmosphere-Pacific Ocean system can largely explain the reconstructed changes in NE China.

Physical properties and sedimentology on core GeoB1510-1

Detailed information about the sediment properties and microstructure can be provided through the analysis of digital ultrasonic P wave seismograms recorded automatically during full waveform core logging. The physical parameter which predominantly affects the elastic wave propagation in water-saturated sediments is the P wave attenuation coefficient. The related sedimentological parameter is the grain size distribution. A set of high-resolution ultrasonic transmission seismograms (ca. 50-500 kHz), which indicate downcore variations in the grain size by their signal shape and frequency content, are presented. Layers of coarse-grained foraminiferal ooze can be identified by highly attenuated P waves, whereas almost unattenuated waves are recorded in fine-grained areas of nannofossil ooze. Color-encoded pixel graphics of the seismograms and instantaneous frequencies present full waveform images of the lithology and attenuation. A modified spectral difference method is introduced to determine the attenuation coefficient and its power law a = kfn. Applied to synthetic seismograms derived using a "constant Q" model, even low attenuation coefficients can be quantified. A downcore analysis gives an attenuation log which ranges from ca. 700 dB/m at 400 kHz and a power of n = 1-2 in coarse-grained sands to few decibels per meter and n ? 0.5 in fine-grained clays. A least squares fit of a second degree polynomial describes the mutual relationship between the mean grain size and the attenuation coefficient. When it is used to predict the mean grain size, an almost perfect coincidence with the values derived from sedimentological measurements is achieved.

Iberian Margin 420 kyr geochemical and color variations record

Here we report 420 kyr long records of sediment geochemical and color variations from the southwestern Iberian Margin. We synchronized the Iberian Margin sediment record to Antarctic ice cores and speleothem records on millennial time scales and investigated the phase responses relative to orbital forcing of multiple proxy records available from these cores. Iberian Margin sediments contain strong precession power. Sediment "redness" (a* and 570-560 nm) and the ratio of long-chain alcohols to n-alkanes (C26OH/(C26OH + C29)) are highly coherent and in-phase with precession. Redder layers and more oxidizing conditions (low alcohol ratio) occur near precession minima (summer insolation maxima). We suggest these proxies respond rapidly to low-latitude insolation forcing by wind-driven processes (e.g., dust transport, upwelling, precipitation). Most Iberian Margin sediment parameters lag obliquity maxima by 7-8 ka, indicating a consistent linear response to insolation forcing at obliquity frequencies driven mainly by high-latitude processes. Although the lengths of the time series are short (420 ka) for detecting 100 kyr eccentricity cycles, the phase relationships support those obtained by Shackleton []. Antarctic temperature and the Iberian Margin alcohol ratios (C26OH/(C26OH + C29)) lead eccentricity maxima by 6 kyr, with lower ratios (increased oxygenation) occurring at eccentricity maxima. CO2, CH4, and Iberian SST are nearly in phase with eccentricity, and minimum ice volume (as inferred from Pacific d18Oseawater) lags eccentricity maxima by 10 kyr. The phase relationships derived in this study continue to support a potential role of the Earth's carbon cycle in contributing to the 100 kyr cycle.

Consolidation properties and permeability values of sediments from three ODP Leg 205 sites

Vertical permeability and sediment consolidation measurements were taken on seven whole-round drill cores from Sites 1253 (three samples), 1254 (one sample), and 1255 (three samples) drilled during Ocean Drilling Program Leg 205 in the Middle America Trench off of Costa Rica's Pacific Coast. Consolidation behavior including slopes of elastic rebound and virgin compression curves (Cc) was measured by constant rate of strain tests. Permeabilities were determined from flow-through experiments during stepped-load tests and by using coefficient of consolidation (Cv) values continuously while loading. Consolidation curves and the Casagrande method were used to determine maximum preconsolidation stress. Elastic slopes of consolidation curves ranged from 0.097 to 0.158 in pelagic sediments and 0.0075 to 0.018 in hemipelagic sediments. Cc values ranged from 1.225 to 1.427 for pelagic carbonates and 0.504 to 0.826 for hemipelagic clay-rich sediments. In samples consolidated to an axial stress of ~20 MPa, permeabilities determined by flow-through experiments ranged from a low value of 7.66 x 10**-20 m**2 in hemipelagic sediments to a maximum value of 1.03 x 10**-16 m**2 in pelagic sediments. Permeabilities calculated from Cv values in the hemipelagic sediments ranged from 4.81 x 10**-16 to 7.66 x 10**-20 m**2 for porosities 49.9%-26.1%.