A comparison of upper division performance of community college transfer students to native university students

The purpose of this study was to examine the hypothesis that no differences existed in the upper division performance of academically excellent community college transfer students when compared to native university students. The relationship of enrollment patterns such as skipped terms, dropped terms, summer session utilization, college of major, credits attempted, credits received, test scores, and current status were also studied.^ The data were collected through a hand analysis of 673 student transcripts which provided the information for a database designed specifically for this study. The subjects were 229 transfers from Miami-Dade Community College and 444 natives from Florida International University. The students all began their studies in the lower division in the Fall term of 1982, 1983 or 1984 and eventually transferred to the upper division at FIU. This longitudinal study followed the upper division performance and enrollment patterns through the Spring term of 1991.^ Data analysis included chi-square for all categorical and numerical variables; t-tests were performed for the numerical variables. Correlation coefficients, Two-Way Analysis of Variance and Three-Way Crosstabulations were also used when indicated. There were significant differences among the upper division performance of community college transfer students and native university students for the graduation rate and the GPA range. A significant difference was also found between the math and essay CLAST scores, number of summer terms utilized, number of terms to graduation, current enrollment status, and credits attempted and received for the groups. ^

Risk-taking propensity of faculty, administrators and academic departament heads at large, urban, multicampus community college

This research was undertaken to explore dimensions of the risk construct, identify factors related to risk-taking in education, and study risk propensity among employees at a community college. Risk-taking propensity (RTP) was measured by the 12-item BCDQ, which consisted of personal and professional risk-related situations balanced for the money, reputation, and satisfaction dimensions of the risk construct. Scoring ranged from 1.00 (most cautious) to 6.00 (most risky). Surveys including the BCDQ and seven demographic questions relating to age, gender, professional status, length of service, academic discipline, highest degree, and campus location were sent to faculty, administrators, and academic department heads. A total of 325 surveys were returned, resulting in a 66.7% response rate. Subjects were relatively homogeneous for age, length of service, and highest degree. Subjects were also homogeneous for risk-taking propensity: no substantive differences in RTP scores were noted within and among demographic groups, with the possible exception of academic discipline. The mean RTP score for all subjects was 3.77, for faculty was 3.76, for administrators was 3.83, and for department heads was 3.64. The relationship between propensity to take personal risks and propensity to take professional risks was tested by computing Pearson r correlation coefficients. The relationships for the total sample, faculty, and administrator groups were statistically significant, but of limited practical significance. Subjects were placed into risk categories by dividing the response scale into thirds. A 3 X 3 factorial ANOVA revealed no interaction effects between professional status and risk category with regard to RTP score. A discriminant analysis showed that a seven-factor model was not effective in predicting risk category. The homogeneity of the study sample and the effect of a risk encouraging environment were discussed in the context of the community college. Since very little data on risk-taking in education is available, risk propensity data from this study could serve as a basis for comparison to future research. Results could be used by institutions to plan professional development activities, designed to increase risk-taking and encourage active acceptance of change.

Increasing nurse's knowledge of neonatal pain assessment through inservice education program

Recognizing neonatal pain is a challenge for nurses working with newborns due to the complexity of the pain phenomenon. Pain is subjective, and infants lack the ability to communicate, and their pain is difficult to recognize. The purpose of this study is to determine the effectiveness of education on the NICU nurses' ability to assess neonatal pain. With a better understanding of pain theory and the effects of pain on the newborn the nurse will be better able to assess newborns with pain. Designed as a quasi-experimental one-group pretest and posttest study, the data was collected on a convenience sample of 49 registered nurses employed in the neonatal and special care nursery units at a Childrens Hospital in the Miami area. The nurses were surveyed on the assessment of neonatal pain using the General Information and Pain Sensitivity Questionnaire. After the initial survey, the nurses were inserviced on neonatal pain assessment using a one hour inservice education program. One week after the intervention the nurse was asked to complete the questionnaire again. Data analysis involved comparision of pre and post intervention findings using descriptive methods, t test, correlation coefficients, and ANOVA , where applicable. Findings revealed a significant ( p=.006) increase in nurse's knowledge of neonatal pain assessment after completing the educational inservice when comparing the pre-test and post-test results.

Further evidence for the relationship between eating frequency, body mass index, and physical activity

The relationship between the frequency of eating, physical activity and Body Mass Index (BMI) was investigated. Seventy five women, aged 24 to 55, were recruited from Florida International University. Via interview, subjects provided information regarding demographics and habitual eating frequency over 24-hours, and completed both the Baecke Questionnaire of Habitual Physical Activity and the Health Insurance Plan of New York Questionnaire on Physical Activity. Pearson correlations and partial correlation coefficients were used to assess the relationship between eating frequency, physical activity, age, and BMI. Results revealed significant positive correlations between eating frequency and total physical activity scores, and leisure time physical activity scores, but not between eating frequency and physical activity on the job. Partial correlations suggest that there may be an effect of eating frequency on BMI both through an effect on physical activity and through another mechanism. These results suggest that more frequent eaters tend to be more physically active, which may partially explain why lower body weights is associated with more frequent eating.

Hourly values of X, Y and Z geomagnetic components recorded at Coimbra Station (IAGA code: COI) and local geomagnetic K indices for 2007 to 2014

This study examines the performance of series of two geomagnetic indices and series synthesized from a semi-empirical model of magnetospheric currents, in explaining the geomagnetic activity observed at Northern Hemipshere's mid-latitude ground-based stations. We analyse data, for the 2007 to 2014 period, from four magnetic observatories (Coimbra, Portugal; Panagyurishte, Bulgary; Novosibirsk, Russia and Boulder, USA), at geomagnetic latitudes between 40° and 50° N. The quiet daily (QD) variation is firstly removed from the time series of the geomagnetic horizontal component (H) using natural orthogonal components (NOC) tools. We compare the resulting series with series of storm-time disturbance (Dst) and ring current (RC) indices and with H series synthesized from the Tsyganenko and Sitnov (2005, doi:10.1029/2004JA010798) (TS05) semi-empirical model of storm-time geomagnetic field. In the analysis, we separate days with low and high local K-index values. Our results show that NOC models are as efficient as standard models of QD variation in preparing raw data to be compared with proxies, but with much less complexity. For the two stations in Europe, we obtain indication that NOC models could be able to separate ionospheric and magnetospheric contributions. Dst and RC series explain the four observatory H-series successfully, with values for the mean of significant correlation coefficients, from 0.5 to 0.6 during low geomagnetic activity (K less than 4) and from 0.6 to 0.7 for geomagnetic active days (K greater than or equal to 4). With regard to the performance of TS05, our results show that the four observatories separate into two groups: Coimbra and Panagyurishte, in one group, for which the magnetospheric/ionospheric ratio in QD variation is smaller, a dominantly QD ionospheric contribution can be removed and TS05 simulations are the best proxy; Boulder and Novosibirsk,in the other group, for which the ionospheric and magnetospheric contributions in QD variation can not be differentiated and correlations with TS05 series can not be made to improve. The main contributor to magnetospheric QD signal are Birkeland currents. The relatively good success of TS05 model in explaining ground-based irregular geomagnetic activity at mid-latitudes makes it an effective tool to classify storms according to their main sources. For Coimbra and Panagyurishte in particular, where ionospheric and magnetospheric daily contributions seem easier to separate, we can aspire to use the TS05 model for ensemble generation in space weather (SW) forecasting and interpretation of past SW events.

Avaliação da aplicabilidade de correlações matemáticas e redes neurais na predição de parâmetros de especificação do diesel

Diesel fuel is one of leading petroleum products marketed in Brazil, and has its quality monitored by specialized laboratories linked to the National Agency of Petroleum, Natural Gas and Biofuels - ANP. The main trial evaluating physicochemical properties of diesel are listed in the resolutions ANP Nº 65 of December 9th, 2011 and Nº 45 of December 20th, 2012 that determine the specification limits for each parameter and methodologies of analysis that should be adopted. However the methods used although quite consolidated, require dedicated equipment with high cost of acquisition and maintenance, as well as technical expertise for completion of these trials. Studies for development of more rapid alternative methods and lower cost have been the focus of many researchers. In this same perspective, this work conducted an assessment of the applicability of existing specialized literature on mathematical equations and artificial neural networks (ANN) for the determination of parameters of specification diesel fuel. 162 samples of diesel with a maximum sulfur content of 50, 500 and 1800 ppm, which were analyzed in a specialized laboratory using ASTM methods recommended by the ANP, with a total of 810 trials were used for this study. Experimental results atmospheric distillation (ASTM D86), and density (ASTM D4052) of diesel samples were used as basic input variables to the equations evaluated. The RNAs were applied to predict the flash point, cetane number and sulfur content (S50, S500, S1800), in which were tested network architectures feed-forward backpropagation and generalized regression varying the parameters of the matrix input in order to determine the set of variables and the best type of network for the prediction of variables of interest. The results obtained by the equations and RNAs were compared with experimental results using the nonparametric Wilcoxon test and Student's t test, at a significance level of 5%, as well as the coefficient of determination and percentage error, an error which was obtained 27, 61% for the flash point using a specific equation. The cetane number was obtained by three equations, and both showed good correlation coefficients, especially equation based on aniline point, with the lowest error of 0,816%. ANNs for predicting the flash point and the index cetane showed quite superior results to those observed with the mathematical equations, respectively, with errors of 2,55% and 0,23%. Among the samples with different sulfur contents, the RNAs were better able to predict the S1800 with error of 1,557%. Generally, networks of the type feedforward proved superior to generalized regression.

Analysis of bio-derived platform chemicals in ionic liquid media by desorption/ionization mass spectrometry

In this work, desorption/ionization mass spectrometry was employed for the analysis of sugars and small platform chemicals that are common intermediates in biomass transformation reactions. Specifically, matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) mass spectrometric techniques were employed as alternatives to traditional chromatographic methods. Ionic liquid matrices (ILMs) were designed based on traditional solid MALDI matrices (2,5-dihydroxybenzoic acid (DHB) and α-cyano-4-hydroxycinnamic acid (CHCA)) and 1,3-dialkylimidazolium ionic liquids ([BMIM]Cl, [EMIM]Cl, and [EMIM]OAc) that have been employed as reaction media for biomass transformation reactions such as the conversion of carbohydrates to valuable platform chemicals. Although two new ILMs were synthesized ([EMIM][DHB] and [EMIM][CHCA] from [EMIM]OAc), chloride-containing ILs did not react with matrices and resulted in mixtures of IL and matrix in solution. Compared to the parent solid matrices, much less matrix interference was observed in the low mass region of the mass spectrum (< 500 Da) using each of the IL-matrices. Furthermore, the formation of a true ILM (i.e. a new ion pair) does not appear to be necessary for analyte ionization. MALDI sample preparation techniques were optimized based on the compatibility with analyte, IL and matrix. ILMs and IL-matrix mixtures of DHB allowed for qualitative analysis of glucose, fructose, sucrose and N-acetyl-D-glucosamine. Analogous CHCA-containing ILMs did not result in appreciable analyte signals under similar conditions. Small platform compounds such as 5-hydroxymethylfurfural (HMF) and levulinic acid were not detected by direct analysis using MALDI-MS. Furthermore, sugar analyte signals were only detected at relatively high matrix:IL:analyte ratios (1:1:1) due to significant matrix and analyte suppression by the IL ions. Therefore, chemical modification of analytes with glycidyltrimethylammonium chloride (GTMA) was employed to extend this method to quantitative applications. Derivatization was accomplished in aqueous IL solutions with fair reaction efficiencies (36.9 – 48.4 % glucose conversion). Calibration curves of derivatized glucose-GTMA yielded good linearity in all solvent systems tested, with decreased % RSDs of analyte ion signals in IL solutions as compared to purely aqueous systems (1.2 – 7.2 % and 4.2 – 8.7 %, respectively). Derivatization resulted in a substantial increase in sensitivity for MALDI-MS analyses: glucose was reliably detected at IL:analyte ratios of 100:1 (as compared to 1:1 prior to derivatization). Screening of all test analytes resulted in appreciable analyte signals in MALDI-MS spectra, including both HMF and levulinic acid. Using appropriate internal standards, calibration curves were constructed and this method was employed for monitoring a model dehydration reaction of fructose to HMF in [BMIM]Cl. Calibration curves showed wide dynamic ranges (LOD – 100 ng fructose/μg [BMIM]Cl, LOD – 75 ng HMF/μg [BMIM]Cl) with correlation coefficients of 0.9973 (fructose) and 0.9931 (HMF). LODs were estimated from the calibration data to be 7.2 ng fructose/μg [BMIM]Cl and 7.5 ng HMF/μg [BMIM]Cl, however relatively high S/N ratios at these concentrations indicate that these values are likely overestimated. Application of this method allowed for the rapid acquisition of quantitative data without the need for prior separation of analyte and IL. Finally, small molecule platform chemicals HMF and levulinic acid were qualitatively analyzed by DESI-MS. Both HMF and levulinic acid were easily ionized and the corresponding molecular ions were easily detected in the presence of 10 – 100 times IL, without the need for chemical modification prior to analysis. DESI-MS analysis of ILs in positive and negative ion modes resulted in few ions in the low mass region, showing great potential for the analysis of small molecules in IL media.

Escala diagramática e influência de sistemas de produção na ocorrência do crestamento gomoso da melancia

CHAPTER 1 - The gummy stem blight, caused by the fungus D. bryoniae, is a disease commonly found in watermelon cultivated in several countries. In Brazil, there are numerous studies related to the disease, but there are not uniform methods for quantifying of disease severity in the field. Thus, we developed a diagrammatic scale based on scanned photos of watermelon leaves infected with D. bryoniae. The scale developed showed levels of 0; 10; 20; 45; 65 and 90% of severity. The scale validation was divided into two parts: initially, 10 evaluators (half with experienced and other half without experience) estimated the disease severity based on the initial observation of 100 photos of watermelon leaves with symptoms of the disease at different severity levels. Before, the same evaluators estimated the disease severity with the support of the scale prepared from the Quant program. Data were analyzed using linear regression and were obtained angular, linear, and correlation coefficients. Based on these data, we determined the accuracy and precision of the evaluations. The correlation coefficients (R2) ranged from 0.88 - 0.97 for the experienced evaluators and from 0.55 - 0.95 for the inexperienced evaluators. The average angular coefficient (A) for inexperienced evaluators was 20.42 and 8.61 with and without the support of diagrammatic scale, respectively. Experienced evaluators showed values of average linear coefficient of 5.30 and 1.68 with and without the support of diagrammatic scale, respectively. The absolute errors analysis indicated that the use of diagrammatic scale contributed to minimize the flaws in the severity levels estimation. The diagrammatic scale proposed shown adequate for gummy stem blight severity evaluation in watermelon. CHAPTER 2 - The gummy stem blight (Didymella bryoniae) is a disease that affects the productivity of watermelon leading to losses over 40%. This study aimed to evaluate the efficiency of different production systems in control of gummy stem blight in watermelon for to establish efficient methods to combat the disease. There were applied the following treatments: conventional tillage (T1), integrated management (T2) and organic management (T3). In T1 and T2 were applied mineral fertilization and T3 was used bovine manure. There was application of fungicides and insecticides in commercial dose in T1 and T2, being after soil chemical analysis in T2. Disease severity was assessed by grading scale. The experimental design was randomized blocks. The severity of gummy stem blight has increased substantially during the fruit formation. Watermelon plants grown with integrated management (T2) showed lower levels of disease severity, while plants in organic management (T3) exhibited higher levels of severity. We conclude that management based on judicious accompaniments in field represents best way to achieve the phytosanitary aspect adequate for cultivation of watermelon in Tocantins.

Determinação de teor de biodieseis de algodão e pinhão manso em misturas com diesel usando espectroscopia no infravermelho médio e quadrados mínimos parciais

Biodiesel is a renewable fuel derived from vegetable oils or animal fats, which can be a total or partial substitute for diesel. Since 2005, this fuel was introduced in the Brazilian energy matrix through Law 11.097 that determines the percentage of biodiesel added to diesel oil as well as monitoring the insertion of this fuel in market. The National Agency of Petroleum, Natural Gas and Biofuels (ANP) establish the obligation of adding 7% (v/v) of biodiesel to diesel commercialized in the country, making crucial the analytical control of this content. Therefore, in this study were developed and validated methodologies based on the use of Mid Infrared Spectroscopy (MIR) and Multivariate Calibration by Partial Least Squares (PLS) to quantify the methyl and ethyl biodiesels content of cotton and jatropha in binary blends with diesel at concentration range from 1.00 to 30.00% (v/v), since this is the range specified in standard ABNT NBR 15568. The biodiesels were produced from two routes, using ethanol or methanol, and evaluated according to the parameters: oxidative stability, water content, kinematic viscosity and density, presenting results according to ANP Resolution No. 45/2014. The built PLS models were validated on the basis of ASTM E1655-05 for Infrared Spectroscopy and Multivariate Calibration and ABNT NBR 15568, with satisfactory results due to RMSEP (Root Mean Square Error of Prediction) values below 0.08% (<0.1%), correlation coefficients (R) above 0.9997 and the absence of systematic error (bias). Therefore, the methodologies developed can be a promising alternative in the quality control of this fuel.

Estrutura produtiva da agropecuária, ocupação e pobreza no estado de Minas Gerais no ano de 2010

This thesis aimed to contribute to the discussion about the relationship between agricultural production structure, occupation and poverty in Brazil, specifically in the state of Minas Gerais, in 2010. The issue of employment is becoming increasingly challenging in the face of ongoing modernization process in agriculture, capital intensive and labor saver looking levels ever higher production and productivity. The productive inclusion can be an effective way to exit from poverty (the work is often the only asset of the poor). In this sense, we sought to investigate what activities or groups of activities occupied a larger number of people and generated higher yields and can potentially have contributed to a lower incidence of poverty. The basis for primary data was the 2010 Population Census (microdata). To achieve the objectives we used descriptive analysis, Pearson correlation coefficients and quantile regressions. Among the main findings highlight that agriculture occupied more and generated higher overall income than ranching presented more precarious, despite having lower average incomes and income percentile values, greater heterogeneity and instability, as well as higher proportions of poor. Overall, commodities showed greater formalization and lower poor proportions. In the case of agriculture, commodities activities occupied less, generated lower mass income and middle-income (although income percentiles slightly larger and more informality) and had lower poverty indicators than non-commodity (more heterogeneous rents). In livestock, commodities had higher percentages of occupation, income (although middle-income values and percentiles slightly smaller), and smaller proportions of poor than non-commodity (more heterogenous). In terms of occupation and income stood out the farming activities unspecified (non-commodity), the coffee growing and cattle (commodities). The cultivation of coffee and cattle had the lowest poverty indicators. agricultural production diversification indicators showed positive correlations with the occupation in activities not commodities (only), but also with the proportion of poor, indigent and concentration of income. In addition, the occupation in not commodities showed positive correlations with poverty indicators. It is noteworthy that the occupations in soybeans, coffee and fruit showed negative correlation coefficients with the indicators of poverty, indigence and gini. Finally, among the agricultural activities, there was to go to occupied in agricultural activities not commodities for commodity would be 'more equalizer' (decreasing coefficients over the distribution of income) than for cattle. The occupation in livestock (mostly non-commodity) would generate greater impact on the lower income deciles, but their coefficients grow back in the last deciles, which shows its most perverse character. Among the activities that would affect more strongly the lower deciles and less the higher deciles stand out pig farming, poultry, citrus cultivation, coffee and sugar cane. The cattle and the cultivation of soy, had the highest rates, but they grow back in the last deciles, which shows a more wicked character.

To what extent does the Health Professions Admission Test-Ireland predict performance in early undergraduate tests of communication and clinical skills? – An observational cohort study

Background: Internationally, tests of general mental ability are used in the selection of medical students. Examples include the Medical College Admission Test, Undergraduate Medicine and Health Sciences Admission Test and the UK Clinical Aptitude Test. The most widely used measure of their efficacy is predictive validity.A new tool, the Health Professions Admission Test- Ireland (HPAT-Ireland), was introduced in 2009. Traditionally, selection to Irish undergraduate medical schools relied on academic achievement. Since 2009, Irish and EU applicants are selected on a combination of their secondary school academic record (measured predominately by the Leaving Certificate Examination) and HPAT-Ireland score. This is the first study to report on the predictive validity of the HPAT-Ireland for early undergraduate assessments of communication and clinical skills. Method. Students enrolled at two Irish medical schools in 2009 were followed up for two years. Data collected were gender, HPAT-Ireland total and subsection scores; Leaving Certificate Examination plus HPAT-Ireland combined score, Year 1 Objective Structured Clinical Examination (OSCE) scores (Total score, communication and clinical subtest scores), Year 1 Multiple Choice Questions and Year 2 OSCE and subset scores. We report descriptive statistics, Pearson correlation coefficients and Multiple linear regression models. Results: Data were available for 312 students. In Year 1 none of the selection criteria were significantly related to student OSCE performance. The Leaving Certificate Examination and Leaving Certificate plus HPAT-Ireland combined scores correlated with MCQ marks.In Year 2 a series of significant correlations emerged between the HPAT-Ireland and subsections thereof with OSCE Communication Z-scores; OSCE Clinical Z-scores; and Total OSCE Z-scores. However on multiple regression only the relationship between Total OSCE Score and the Total HPAT-Ireland score remained significant; albeit the predictive power was modest. Conclusion: We found that none of our selection criteria strongly predict clinical and communication skills. The HPAT- Ireland appears to measures ability in domains different to those assessed by the Leaving Certificate Examination. While some significant associations did emerge in Year 2 between HPAT Ireland and total OSCE scores further evaluation is required to establish if this pattern continues during the senior years of the medical course.

Knowledge Modeling for The Outcome of Brain Stereotactic Radiosurgery

Purpose: To build a model that will predict the survival time for patients that were treated with stereotactic radiosurgery for brain metastases using support vector machine (SVM) regression.

Methods and Materials: This study utilized data from 481 patients, which were equally divided into training and validation datasets randomly. The SVM model used a Gaussian RBF function, along with various parameters, such as the size of the epsilon insensitive region and the cost parameter (C) that are used to control the amount of error tolerated by the model. The predictor variables for the SVM model consisted of the actual survival time of the patient, the number of brain metastases, the graded prognostic assessment (GPA) and Karnofsky Performance Scale (KPS) scores, prescription dose, and the largest planning target volume (PTV). The response of the model is the survival time of the patient. The resulting survival time predictions were analyzed against the actual survival times by single parameter classification and two-parameter classification. The predicted mean survival times within each classification were compared with the actual values to obtain the confidence interval associated with the model’s predictions. In addition to visualizing the data on plots using the means and error bars, the correlation coefficients between the actual and predicted means of the survival times were calculated during each step of the classification.

Results: The number of metastases and KPS scores, were consistently shown to be the strongest predictors in the single parameter classification, and were subsequently used as first classifiers in the two-parameter classification. When the survival times were analyzed with the number of metastases as the first classifier, the best correlation was obtained for patients with 3 metastases, while patients with 4 or 5 metastases had significantly worse results. When the KPS score was used as the first classifier, patients with a KPS score of 60 and 90/100 had similar strong correlation results. These mixed results are likely due to the limited data available for patients with more than 3 metastases or KPS scores of 60 or less.

Conclusions: The number of metastases and the KPS score both showed to be strong predictors of patient survival time. The model was less accurate for patients with more metastases and certain KPS scores due to the lack of training data.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Geological reference standards (JLK-1,JSD-1, JSD-2, JSD-3,JMS-1, JMS-2) measured by Itrax-XRF core scanner in different exposure times

X-ray fluorescence (XRF) core-scanning is a fast and nondestructive technique to assess elemental variations of unprocessed sediments. However, although the exposure time of XRF-scanning directly affects the scanning counts and total measurement time, only a few studies have considered the influence of exposure time during the scan. How to select an optimal exposure time to achieve reliable results and reduce the total measurement time is an important issue. To address this question, six geological reference materials from the Geological Survey of Japan (JLK-1, JMS-1, JMS-2, JSD-1, JSD-2, and JSD-3) were scanned by the Itrax-XRF core scanner using the Mo- and the Cr-tube with different exposure times to allow a comparison of scanning counts with absolute concentrations. The regression lines and correlation coefficients of elements that are generally used in paleoenvironmental studies were examined for the different exposure times and X-ray tubes. The results show that for those elements with relatively high concentrations or high detectability, the correlation coefficients are higher than 0.90 for all exposure times. In contrast, for the low detectability or low concentration elements, the correlation coefficients are relatively low, and improve little with increased exposure time. Therefore, we suggest that the influence of different exposure times is insignificant for the accuracy of the measurements. Thus, caution must be taken when interpreting the results of elements with low detectability, even when the exposure times are long and scanning counts are reasonably high.

Hydrogen isotope compositions of terrestrial plant-wax n-alkanes analysed on a transect of marine surface sediment samples from 1°N to 28°S off southwest Africa

A transect of marine surface sediment samples from 1° N to 28° S off southwest Africa was analysed to verify the application of hydrogen isotope compositions of terrestrial plant-wax n-alkanes preserved in ocean sediments as a proxy for continental hydrological conditions. Conditions on the adjacent continent range from humid evergreen forests to deciduous forests, wood- and shrub land and further to arid grasslands and deserts. The hydrogen isotope values for the dominant n-alkane homologues (C29, C31 and C33) vary from -123 per mil to -141 per mil VSMOW and correlate with the modelled hydrogen isotope composition of mean annual and growing season precipitation of postulated continental source areas (r up to 0.8, p < 0.01). The apparent hydrogen isotope fractionation between alkanes and mean annual precipitation is remarkably uniform (-109 per mil on average, Sigma <= 5 per mil, n = 27). Potentially, effects of aridity on the apparent hydrogen isotope fractionation are concealed by the contribution of different plants (C3 dicotyledons vs C4 grasses). Thus, isotope ratios of leaf wax n-alkanes preserved in ocean margin sediments in these and similar tropical regions may be directly converted to dD ratios of ancient precipitation by employing a constant hydrogen isotope fractionation.