Plasma arginine vasopressin concentrations in epileptics under monotherapy

Plasma arginine vasopressin concentrations were determined by radio-immunoassay in 112 adult epileptics who were taking carbamazepine, phenytoin, primidone, or sodium valproate in long-term monotherapy, and in 19 controls. No significant difference was found between the groups, but some epileptics taking carbamazepine and primidone showed low values. Serum concentrations of carbamazepine did not correlate with the concentrations of plasma arginine vasopressin. In conclusion, there was no evidence of a stimulating effect of chronic carbamazepine medication or a special inhibiting effect of phenytoin on the release of vasopressin arginine from the posterior pituitary

Effect of wire diameter on ultrasonic enhancement of subcooled pool boiling

Paper presented at the 9th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Malta, 16-18 July, 2012.New methods for cooling of microelectronic elements have been recently developed, including application of ultrasonic fields. Ultrasonic fields enhance the heat transfer in two-phase cooling. The present work deals with ultrasonic enhancement of heat transfer from wires in sub-cooled pool boiling. The experiments have been carried out using three wires of different diameters: 0.05, 0.09, 0.2mm, submerged into a bath with water. The applied ultrasonic field was of frequency of 40 kHz and intensity of 0.5 W/cm2. The wire wall temperature was measured as a function of wire surface heat flux. When the ultrasonic field was applied, the wall temperature reduced in the range of measured heat fluxes. The temperature difference increased with the heat flux. It also increased with the wire diameter. At the smallest diameter only a small decrease of the wall temperature, about 10-15 degrees, was observed, while at larger diameters the decrease of the wall temperature was about 30 - 35 degrees.dc201

Efficacy of methylsulfonylmethane supplementation on osteoarthritis of the knee: a randomized controlled study

BACKGROUND: Patients with osteoarthritis (OA) take a variety of health supplements in an attempt to reduce pain and improve function. The aim of this study was to determine the efficacy of methylsulfonylmethane (MSM) in treating patients with knee OA. METHODS: This study was a prospective, randomized, double-blind, controlled clinical trial. Forty nine men and women 45-90 (mean 68 ± SD 7.3) years of age with knee OA according to the American College of Rheumatology clinical criteria for OA of the knee and with radiographic confirmed knee OA were enrolled in the study and randomly assigned into 2 groups: One received MSM in doses of 1.125 grams 3 times daily for 12 weeks and the other received a placebo in the same dosing frequency. The primary outcomes were the WOMAC Osteoarthritis Index for pain, stiffness and physical function, the Aggregated Locomotor Function (ALF) test that evaluates each patient's physical function, the SF-36 quality of life health survey and the visual-analogue-scale (VAS) for pain. The secondary outcomes were Knee Society Clinical Rating System for Knee Score (KSKS) and Function Score (KSFS). Patients were assessed at baseline, 6 weeks and 12 weeks. All continuous variables were tested by the Kolmogorov-Smirnov test for Normal distribution. Changes within the groups and differences between the groups were calculated by repeated measures of analysis (ANOVA) with one nested variable. RESULTS: There were significant differences between treatment groups over time in WOMAC physical function (14.6 mm [CI: 4.3, 25.0]; p = 0.04) and in WOMAC total score (15.0 mm [CI: 5.1, 24.9]; p = 0.03). Treatment groups did not differ significantly in WOMAC pain (12.4 mm [CI: 0.0, 24.8]); p = 0.08) or WOMAC stiffness (27.2 mm [CI: 8.2, 46.2]; p = 0.08). There was a non-significant difference in SF-36 total score between treatment groups (11.6 [CI: 1.0, 22.1]; p = 0.54). A significant difference was found between groups in VAS for pain (0.7 s [CI: -0.9, 2.4]; p = 0.05). Secondary outcomes showed non-significant differences between the two groups. CONCLUSIONS: Patients with OA of the knee taking MSM for 12 weeks showed an improvement in pain and physical function. These improvements, however, are small and it is yet to be determined if they are of clinical significance. TRIAL REGISTRATION: ClinicalTrials.gov: NCT0118821

Organizational Mortality of Small Firms: The Effects of Entrepreneurial Age and Human Capital

This paper addresses the issue of internal determination of organizational outcomes. It is argued that in small and simply structured organizations a considerable proportion of the variance in organizational activities and outcomes is associated with individuals. In particular, the paper uses human capital theory to derive hypotheses about individual determinants of organizational mortality. These hypotheses are tested with event-history data of firm registrations and de-registrations in a West German region. The hypotheses are corroborated by the data, but the effects may nonetheless be due to processes linking individual characteristics with organizational performance other than those suggested by the human capital approach

Using Rheo-Small-Angle Neutron Scattering to Understand How Functionalised Dipeptides Form Gels

We explore the use of rheo-small-angle neutron scattering as a method to collect structural information from neutron scattering simultaneously with rheology to understand how low-molecular-weight hydrogels form and behave under shear. We examine three different gelling hydrogel systems to assess what structures are formed and how these influence the rheology. Furthermore, we probe what is happening to the network during syneresis and why the gels do not recover after an applied strain. All this information is vital when considering gels for applications such as 3D-printing and injection

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Do Online Trolling Strategies Differ in Political and Interest Forums : Early Results

This study compares the effectiveness of different trolling strategies in two online contexts: politically oriented forums that address issues like global warming, and interest-based forums that deal with peo- ple’s personal interests. Based on previous research, we consider trolling as context-bound and suggest that relevance theory and common ground- ing theory can explain why people may attend and react to certain types of troll posts in one forum, but pay scant attention to them in another. We postulate two hypotheses on how successful (i.e., disrup- tive) trolling varies according to context: that trolls’ messaging strate- gies appear in different frequencies in political and interest forums (H1), and that context-matching strategies also produce longer futile conver- sations (H2). Using Hardaker’s categorization of trolling strategies on a covert–overt continuum, our statistical analysis on a dataset of 49 online conversations verified H1: in political forums covert strategies were more common than overt ones; in interest forums the opposite was the case. Regarding H2 our results were inconclusive. However, the results moti- vate further research on this phenomenon with larger datasets.Peer reviewe

How managers can build trust in strategic alliances: a meta-analysis on the central trust-building mechanisms

Trust is an important driver of superior alliance performance. Alliance managers are influential in this regard because trust requires active involvement, commitment and the dedicated support of the key actors involved in the strategic alliance. Despite the importance of trust for explaining alliance performance, little effort has been made to systematically investigate the mechanisms that managers can use to purposefully create trust in strategic alliances. We use Parkhe’s (1998b) theoretical framework to derive nine hypotheses that distinguish between process-based, characteristic-based and institutional-based trust-building mechanisms. Our meta-analysis of 64 empirical studies shows that trust is strongly related to alliance performance. Process-based mechanisms are more important for building trust than characteristic- and institutional-based mechanisms. The effects of prior ties and asset specificity are not as strong as expected and the impact of safeguards on trust is not well understood. Overall, theoretical trust research has outpaced empirical research by far and promising opportunities for future empirical research exist

Dealing with Missing Data and Uncertainty in the Context of Data Mining

Missing data is an issue in many real-world datasets yet robust methods for dealing with missing data appropriately still need development. In this paper we conduct an investigation of how some methods for handling missing data perform when the uncertainty increases. Using benchmark datasets from the UCI Machine Learning repository we generate datasets for our experimentation with increasing amounts of data Missing Completely At Random (MCAR) both at the attribute level and at the record level. We then apply four classification algorithms: C4.5, Random Forest, Naïve Bayes and Support Vector Machines (SVMs). We measure the performance of each classifiers on the basis of complete case analysis, simple imputation and then we study the performance of the algorithms that can handle missing data. We find that complete case analysis has a detrimental effect because it renders many datasets infeasible when missing data increases, particularly for high dimensional data. We find that increasing missing data does have a negative effect on the performance of all the algorithms tested but the different algorithms tested either using preprocessing in the form of simple imputation or handling the missing data do not show a significant difference in performance