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The manual differential count of leukocytes is a common analysis in the hematological laboratory. It is used for morphological assessment of the blood cells and can get valuable information according to diagnosis of hematological diseases. The microscopy assessment is dependent of a good staining result of the blood smear in order to get the best conditions to differentiate the different cell types and detect morphological or pathological findings.The aim of this study was to determine if it was possible to change the staining method without any compromises to the quality of the staining results. For this study 25 whole blood samples were collected, and blood smears was made and stained with four different staining protocols, including the one currently used in the routine practice at laboratoriemedicin Sundsvall. The samples were examined by three biomedical scientists and the staining quality of the cells was graded on a four-point scale. The statistical results with Friedmans and Wilcoxons signed-rank test showed differences between the methods on the nuclear and cytoplasm of lymphocytes and the nuclear of monocytes and neutrophils. The recommended staining protocol from the manufacturer was the method that had highest frequency of statistically significant differences compared to the other methods for those cell types. The differences were in favour for the other methods, and the current method showed the best performance. In conclusion it’s not recommended to continue the study with the manufacturer´s staining protocol, but its valuable to continue compare the best performed staining protocols.

In this study, the bacterial growth dynamics of 14 pilot drinking water distribution systems were studied in order to observe water quality changes due to corrosion inhibitor addition. Empirical models were developed to quantity the effect of inhibitor type and dose on bacterial growth (biofilm and bulk water). Water and pipe coupon samples were taken and examined during the experiments. The coupons were exposed to drinking water at approximately 20°C for at least 5 weeks to allow the formation of a measurable quasi- steady-state biofilm. Bulk water samples were taken every week. In this study, two simple but practical empirical models were created. Sensitivity analysis for the bulk HPC model (for all 14 of the PDSs) showed that maintaining a chloramine residual at 2.6 mg/L instead of 1.1 mg/L would decrease bulk HPC by anywhere from 0.5 to 0.9 log, which was greater than the increase in bulk HPC from inhibitor addition at 0.31 to 0.42 log for Si and P based inhibitors respectively. This means that maintaining higher residual levels can counteract the relatively modest increases due to inhibitors. BF HPC was affected by pipe material, effluent residual and temperature in addition to a small increase due to inhibitor addition. Biofilm density was most affected by material type, with polyvinyl chloride (PVC) biofilm density consistently much lower than other materials (0.66, 0.92, and 1.22 log lower than lined cast iron (LCI), unlined cast iron (UCI), and galvanized steel (G), respectively). Temperature had a significant effect on both biofilm and bulk HPC levels but it is not practical to alter temperature for public drinking water distribution systems so temperature is not a management tool like residual. This study evaluated the effects of four different corrosion inhibitors (i.e. based on either phosphate or silica) on drinking water distribution system biofilms and bulk water HPC levels. Four different pipe materials were used in the pilot scale experiments, polyvinyl chloride (PVC), lined cast iron (LCI), unlined cast iron (UCI), and galvanized steel (G). Three kinds of phosphate based and one silica based corrosion inhibitors were added at concentrations typically applied in a drinking water distribution system for corrosion control. The data showed that there was a statistically significant increase of 0.34 log in biofilm bacterial densities (measured as HPC) with the addition of any of the phosphate based inhibitors (ortho-phosphorus, blended ortho-poly-phosphate, and zinc ortho-phosphate). A silica based inhibitor resulted in an increase of 0.36 log. The biological data also showed that there was a statistically significant increase in bulk water bacterial densities (measured as heterotrophic plates count, HPC) with the addition of any of the four inhibitors. For bulk HPC this increase was relatively small, being 15.4% (0.42 log) when using phosphate based inhibitors, and 11.0% (0.31 log) for the silica based inhibitor. Experiments with PDS influent spiked with phosphate salts, phosphate based inhibitors, and the silicate inhibitor showed that the growth response of P17 and NOx in the AOC test was increased by addition of these inorganic compounds. For this source water and the PDSs there was more than one limiting nutrient. In addition to organic compounds phosphorus was identified as a nutrient stimulating growth, and there was also an unidentified nutrient in the silica based inhibitor. However since the percentage increases due to inhibitors were no greater than 15% it is unlikely that this change would be significant for the bulk water microbial quality. In addition it was shown that increasing the chloramines residual could offset any additional growth and that the inhibitors could help compliance with the lead and copper rule. However corrosion inhibitors might result in an increase in monitoring and maintenance requirements, particularly in dead ends, reaches with long HRTs, and possibly storage facilities. In addition it is unknown what the effect of corrosion inhibitors are on the growth of coliform bacteria and opportunistic pathogens relative to ordinary heterotrophs. A method was developed to monitor precision for heterotrophic plate count (HPC) using both blind duplicates and lab replicates as part of a project looking at pilot drinking water distribution systems. Precision control charts were used to monitor for changes in assay variability with time just as they are used for chemical assays. In adapting these control charts for the HPC assay, it was determined that only plate counts ≥ 30 cfu per plate could be used for Quality Assurance (QA) purposes. In addition, four dilutions were used for all known Quality Control (QC) samples to insure counts usable for QC purposes would be obtained. As a result there was a 50% increase in the required labor for a given number of samples when blind duplicates and lab replicates were run in parallel with the samples. For bulk water HPCs the distributions of the duplicate and replicate data were found to be significantly different and separate control charts were used. A probability based analysis for setting up the warning limit (WL) and control limit (CL) was compared with the method following National Institute of Standard and Technology (NIST) guidelines.

Many chemicals are released into surface water and biodegradation is one important mechanismto remove the contaminants. Their actual concentrations in water depend on their persistence.The OECD 309 test is the most recognized method to assess the persistence of chemicals in theaquatic environment. However, the quantity of sediment used in the OECD 309 suspensiontests is not clearly defined. To investigate the influence of sediment concentrations and thebacterial cell count on the biodegradation, we compared the biodegradation rate constants of amixture of organic chemicals in suspension tests with 5 different water-sediment ratios. Thesediment concentrations were 125, 50, 20, 8 and 3 g L-1. The 5 test systems were spiked with129 chemicals including pharmaceuticals, agrochemicals, food additives, and cosmetics at aconcentration of 1 μg mL-1 each. The chemical degradation was monitored by analyzing thewater subsamples collected at different time points during the 14-day incubation period. It wasthe first time that the effects of sediment concentrations on biodegradation were systematicallystudied for a broad range of chemicals. The bacterial cell count was measured to test whether itcorrelated with sediment concentration and biodegradation rate constant. In the present study,k could be quantified for at least 2 sediment concentrations for 10 chemicals. The resultsshowed that the biodegradation rate constants of the chemicals, k, were positively correlatedwith sediment concentration for 10 chemicals; 6 of these correlations were significant. The totalbacterial cell count showed a positive correlation with sediment concentration, but it was notsignificant. Also, k was positively correlated with total cell count, but these correlations werenot significant for any of 10 chemicals. It is concluded that sediment plays an important role inchemical biodegradation. Sediment-associated total cell count can be one factor contributing tothis effect. The variable results of chemical biodegradation rate constants caused by differentsediment concentrations in the present suspension tests suggest the need for a standardizedsediment concentration in OECD 309 tests.

BACKGROUND: Fetal movement count (FMC) is a non-invasive tool used to evaluate fetal well-being and prevent stillbirth. FMC decrease the chances of stillbirth occurrence (Tveit et al., 2009), but in other countries, research shows that women are not taught about the benefits of FMC (Berndl, O'Connel, & Mcload, 2013). The purposes of this study were to discover 1) the knowledge of pregnant women in their third trimester about FMC and 2) sources through which women learn about FMC. METHODOLOGY: The study is a quantitative descriptive design. An original survey consisting of 12 questions was created for the study. Inclusion criteria included women who were in their third trimester (28 weeks of gestation or more), were 18 years old or older, and speak and read English fluently. Consent to take the anonymous survey was implied by completing and returning the survey. Participants were recruited from childbirth and newborn parenting classes. RESULTS: The investigator surveyed 36 women. Eighty-three percent of women (N=30) know what FMC is. All women were seen by a physician, and 50% of them (N=18) were taught about FMC by their providers. In spite of knowing what FMC is, 50% women (15 of the 30) responded incorrectly to specific questions about FMC. DISCUSSION: Women have misconceptions about fetal movement, and about the importance of FMC. This misconception can lead women to less benefit from using FMC tools. Providers need to improve their method of transmitting information about FMC and provide detailed education about fetal movement.

Verzerrungen durch sozial erwünschtes Antwortverhalten bei heiklen Fragen stellen ein großes Problem der empirischen Sozialforschung dar. Dies führt bei Umfragen zu Item- und Unit-Nonresponse sowie zu unehrlichen Antworten. Diese Verzerrung durch soziale Erwünschtheit (Social Desirability Bias, SD-Bias) führt oftmals dazu, dass Fragen zu sozial erwünschtem Verhalten oder Meinungen (z. B. Wahlteilnahme, Blutspenden oder soziales Engagement) überschätzt und zu sozial unerwünschtem Verhalten oder Einstellungen (z. B. Drogenkonsum, Rassismus oder Diebstahl) unterschätzt werden. In dieser Dissertation wird anhand eines Survey-Experiments überprüft ob die Item Count Technique (ICT) und ihr ähnliche Count-Techniken (z.B. Person Count Techique, Item Sum Technique etc.) bessere Ergebnisse bei heiklen Fragen erzielen als direktes Fragen. Dies wurde mit einem experimentellen Design anhand eines Online Access Panels (n = 3044) überprüft. Dabei wurden die gleichen heiklen Fragen in der Experimentalgruppe mit Count-Techniken gestellt (n =2527) und in der Kontrollgruppe (n = 517) direkt gestellt. Insgesamt konnten Count-Techniken keine besseren Ergebnisse erzielen als direktes Fragen.:1 Problemstellung 1.1 Auswirkungen heikler Fragen 2 Dimensionen heikler Fragen 2.1 Soziale Erwünschtheit 2.2 Eingriff in die Privatsphäre 2.3 Gefahr der Aufdeckung 2.4 Psychologische Kosten 2.5 Definition 3 Soziale Erwünschtheit 3.1 Social-Desirability-Neigung 3.1.1 Definition und Konstrukt 3.2 Messung sozialer Erwünschtheit 3.2.1 Die Crowne-Marlowe-Skala 3.2.2 Das Balanced Inventory of Desirable Responding (BIDR) 3.3 Social Desirability Belief und Trait Desirability 3.3.1 Messung des SD-Beliefs 3.3.2 Trait Desirability 4 Handlungstheorie und Befragtenverhalten 4.1 SEU-Theorie und heikle Fragen 4.2 Das Modell der Frame-Selection und heikle Fragen 5 Externe Effekte und heikle Fragen 5.1 Interviewereffekte und heikle Fragen 5.2 Bystander-Effekte und soziale Erwünschtheit 5.3 Modus-Effekte und heikle Fragen 6 Klassische Lösungen für heikle Fragen 6.1 Bogus Pipeline 6.2 Sealed Envelope 6.3 Forgiving Wording 7 Die Randomized Response Technique 7.1 Entstehung und Grundprinzip nach Warner (1965) 7.2 Die „Unrelated Question“ Technique 7.3 Die „Forced Response“ Technique 7.4 Takahasi’s Technique 7.5 Die „Two Step Procedure“ 7.6 Kuks Card Method 7.7 Multivariate Analyse mit der RRT 7.8 Empirische Tests der RRT 7.9 Handlungstheorie und RRT 7.10 Allgemeine Probleme der RRT 8 Das Crosswise-Model 8.1 Entstehung und Grundprinzip 8.2 Empirischer Test des Crosswise-Models mit Meta-Analyse 9 Die Item Count Technique 9.1 Probleme und Design der ICT-Fragen 9.2 Handlungstheorie und ICT 9.3 Kurze Geschichte der ICT 9.4 Das Double List Design 9.5 Die Person Count Technique 9.6 Die Fixed und Variable Person Count Technique 9.7 Die Item Sum Technique 9.8 Die Person Sum Technique 9.9 Multivariate Analyse von ICT-Daten 9.10 Bisherige empirische Tests der Item Count Technique mit Meta-Studie 10 Empirischer Test der Count-Techniken 10.1 Hypothesen 10.2 Grundlegendes Design 10.3 Datenerhebung 10.4 Operationalisierung 10.4.1 SD-Neigung (BIDR16) 10.4.2 SD-Belief und Trait Desirability 10.4.3 Modell der Frame Selection 10.5 Grundlegende Annahmen und Probleme der Hypothesenprüfung 10.6 Prüfung der Item Count Technique 10.7 Prüfung der Person Count Technique 10.8 Prüfung der Fixed Person Count Technique 10.9 Prüfung der Variable Person Count Technique 10.10Prüfung der Item Sum Technique 10.11Prüfung der Person Sum Technique 10.12Prüfung der Count-Techniken insgesamt 11 Prüfung der Theorien des Befragtenverhaltens 11.1 Test der SEU-Theorie 11.2 Test des Modells der Frame Selection 11.3 Prüfung der handlungstheoretischen Betrachtung der Count-Techniken 12 Fazit 13 Anhang 13.1 Deskriptive Statistiken 13.1.1 BIDR16 13.1.2 SD-Belief 13.1.3 Umfrageeinstellung Literatur

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