Characterisation of antibiotic-resistant psychrotrophic bacteria in raw milk

Dynamics of raw milk associated bacteria during cold storage of raw milk and their antibiotic resistance was reviewed, with focus on psychrotrophic bacteria. This study aimed to investigate the significance of cold storage of raw milk on antibiotic-resistant bacterial population and analyse the antibiotic resistance of the Gram-negative antibiotic-resistant psychrotrophic bacteria isolated from the cold-stored raw milk samples. Twenty-four raw milk samples, six at a time, were obtained from lorries that collected milk from Finnish farms and were stored at 4°C/4 d, 6°C/3 d and 6°C/4 d. Antibiotics representing four classes of antibiotics (gentamicin, ceftazidime, levofloxacin and trimethoprim-sulfamethoxazole) were used to determine the antibiotic resistance of mesophilic and psychrotrophic bacteria during the storage period. A representative number of antibiotic-resistant Gram-negative isolates retrieved from the cold-stored raw milk samples were identified by the phenotypic API 20 NE system and a few isolates by the 16S rDNA gene sequencing. Some of the isolates were further evaluated for their antibiotic resistance by the ATB PSE 5 and HiComb system. The initial average mesophilic counts were found below 105 CFU/mL, suggesting that the raw milk samples were of good quality. However, the mesophilic and psychrotrophic population increased when stored at 4°C/4 d, 6°C/3 d and 6°C/4 d. Gentamicin- and levofloxacin-resistant bacteria increased moderately (P < 0.05) while there was a considerable rise (P < 0.05) of ceftazidime- and trimethoprim-sulfamethoxazole-resistant population during the cold storage. Of the 50.9 % (28) of resistant isolates (total 55) identified by API 20 NE, the majority were Sphingomonas paucimobilis (8), Pseudomonas putida (5), Sphingobacterium spiritivorum (3) and Acinetobacter baumanii (2). The analysis by ATB PSE 5 system suggested that 57.1% of the isolates (total 49) were multiresistant. This study showed that the dairy environment harbours multidrug-resistant Gramnegative psychrotrophic bacteria and the cold chain of raw milk storage amplifies the antibioticresistant psychrotrophic bacterial population.

Test of the Clustering Hypothesis in the Helsinki Exchanges

This paper investigates the clustering pattern in the Finnish stock market. Using trading volume and time as factors capturing the clustering pattern in the market, the Keim and Madhavan (1996) and the Engle and Russell (1998) model provide the framework for the analysis. The descriptive and the parametric analysis provide evidences that an important determinant of the famous U-shape pattern in the market is the rate of information arrivals as measured by large trading volumes and durations at the market open and close. Precisely, 1) the larger the trading volume, the greater the impact on prices both in the short and the long run, thus prices will differ across quantities. 2) Large trading volume is a non-linear function of price changes in the long run. 3) Arrival times are positively autocorrelated, indicating a clustering pattern and 4) Information arrivals as approximated by durations are negatively related to trading flow.

Bootstrapping the Error Correction Model Cointegration Test

This paper is concerned with using the bootstrap to obtain improved critical values for the error correction model (ECM) cointegration test in dynamic models. In the paper we investigate the effects of dynamic specification on the size and power of the ECM cointegration test with bootstrap critical values. The results from a Monte Carlo study show that the size of the bootstrap ECM cointegration test is close to the nominal significance level. We find that overspecification of the lag length results in a loss of power. Underspecification of the lag length results in size distortion. The performance of the bootstrap ECM cointegration test deteriorates if the correct lag length is not used in the ECM. The bootstrap ECM cointegration test is therefore not robust to model misspecification.

Using a botanic garden collection to test a bioclimatic vegetation scheme

Vegetation maps and bioclimatic zone classifications communicate the vegetation of an area and are used to explain how the environment regulates the occurrence of plants on large scales. Many practises and methods for dividing the world’s vegetation into smaller entities have been presented. Climatic parameters, floristic characteristics, or edaphic features have been relied upon as decisive factors, and plant species have been used as indicators for vegetation types or zones. Systems depicting vegetation patterns that mainly reflect climatic variation are termed ‘bioclimatic’ vegetation maps. Based on these it has been judged logical to deduce that plants moved between corresponding bioclimatic areas should thrive in the target location, whereas plants moved from a different zone should languish. This principle is routinely applied in forestry and horticulture but actual tests of the validity of bioclimatic maps in this sense seem scanty. In this study I tested the Finnish bioclimatic vegetation zone system (BZS). Relying on the plant collection of Helsinki University Botanic Garden’s Kumpula collection, which according to the BZS is situated at the northern limit of the hemiboreal zone, I aimed to test how the plants’ survival depends on their provenance. My expectation was that plants from the hemiboreal or southern boreal zones should do best in Kumpula, whereas plants from more southern and more northern zones should show progressively lower survival probabilities. I estimated probability of survival using collection database information of plant accessions of known wild origin grown in Kumpula since the mid 1990s, and logistic regression models. The total number of accessions I included in the analyses was 494. Because of problems with some accessions I chose to separately analyse a subset of the complete data, which included 379 accessions. I also analysed different growth forms separately in order to identify differences in probability of survival due to different life strategies. In most analyses accessions of temperate and hemiarctic origin showed lower survival probability than those originating from any of the boreal subzones, which among them exhibited rather evenly high probabilities. Exceptionally mild and wet winters during the study period may have killed off hemiarctic plants. Some winters may have been too harsh for temperate accessions. Trees behaved differently: they showed an almost steadily increasing survival probability from temperate to northern boreal origins. Various factors that could not be controlled for may have affected the results, some of which were difficult to interpret. This was the case in particular with herbs, for which the reliability of the analysis suffered because of difficulties in managing their curatorial data. In all, the results gave some support to the BZS, and especially its hierarchical zonation. However, I question the validity of the formulation of the hypothesis I tested since it may not be entirely justified by the BZS, which was designed for intercontinental comparison of vegetation zones, but not specifically for transcontinental provenance trials. I conclude that botanic gardens should pay due attention to information management and curational practices to ensure the widest possible applicability of their plant collections.

Non-industrial private forest landowners’ choices of timber management strategies.

The factors affecting the non-industrial, private forest landowners' (hereafter referred to using the acronym NIPF) strategic decisions in management planning are studied. A genetic algorithm is used to induce a set of rules predicting potential cut of the landowners' choices of preferred timber management strategies. The rules are based on variables describing the characteristics of the landowners and their forest holdings. The predictive ability of a genetic algorithm is compared to linear regression analysis using identical data sets. The data are cross-validated seven times applying both genetic algorithm and regression analyses in order to examine the data-sensitivity and robustness of the generated models. The optimal rule set derived from genetic algorithm analyses included the following variables: mean initial volume, landowner's positive price expectations for the next eight years, landowner being classified as farmer, and preference for the recreational use of forest property. When tested with previously unseen test data, the optimal rule set resulted in a relative root mean square error of 0.40. In the regression analyses, the optimal regression equation consisted of the following variables: mean initial volume, proportion of forestry income, intention to cut extensively in future, and positive price expectations for the next two years. The R2 of the optimal regression equation was 0.34 and the relative root mean square error obtained from the test data was 0.38. In both models, mean initial volume and positive stumpage price expectations were entered as significant predictors of potential cut of preferred timber management strategy. When tested with the complete data set of 201 observations, both the optimal rule set and the optimal regression model achieved the same level of accuracy.

Uterine cervix nitric oxide and human papillomavirus infection in women

Most women acquire genital high risk human papillomavirus (HPV) infection during their lifetime, but seldom the infection persists and leads to cervical cancer. However, currently it is not possible to identify the women who will develop HPV mediated cervical cancer and this often results to large scale follow-up and overtreatment of the likely spontaneously regressing infection. Thus, it is important to obtain more information on the course of HPV and find markers that could help to identify HPV infected women in risk for progression of cervical lesions and ultimately cancer. Nitric oxide is a free radical gas that takes part both in immune responses and carcinogenesis. Nitric oxide is produced also by cervical cells and therefore, it is possible that cervical nitric oxide could affect also HPV infection. In the present study, including 801 women from the University of Helsinki between years of 2006 and 2011, association between HPV and cervical nitric oxide was evaluated. The levels of nitric oxide were measured as its metabolites nitrate and nitirite (NOx) by spectrophotometry and the expression of nitric oxide producing enzymes endothelial and inducible synthases (eNOS, iNOS) by Western blotting. Women infected with HPV had two-times higher cervical fluid NOx levels compared with non-infected ones. The expression levels of both eNOS and iNOS were higher in HPV-infected women compared with non-infected. Another sexually transmitted disease Chlamydia trachomatis that is an independent risk factor for cervical cancer was also accompanied with elevated NOx levels, whereas vaginal infections, bacterial vaginosis and candida, did not have any effect on NOx levels. The meaning of the elevated HPV related cervical nitric oxide was evaluated in a 12 months follow-up study. It was revealed that high baseline cervical fluid NOx levels favored HPV persistence with OR 4.1. However, low sensitivity (33%) and high false negative rate (67%) restrict the clinical use of the current NOx test. This study indicated that nitric oxide favors HPV persistence and thus it seems to be one of the cofactor associated with a risk of carcinogenesis.

Literature Review: Investigating Drug Transport at the Blood-Brain Barrier and the Effects of P-glycoprotein on the Brain Distribution of Drugs.

The blood-brain barrier (BBB) is a unique barrier that strictly regulates the entry of endogenous substrates and xenobiotics into the brain. This is due to its tight junctions and the array of transporters and metabolic enzymes that are expressed. The determination of brain concentrations in vivo is difficult, laborious and expensive which means that there is interest in developing predictive tools of brain distribution. Predicting brain concentrations is important even in early drug development to ensure efficacy of central nervous system (CNS) targeted drugs and safety of non-CNS drugs. The literature review covers the most common current in vitro, in vivo and in silico methods of studying transport into the brain, concentrating on transporter effects. The consequences of efflux mediated by p-glycoprotein, the most widely characterized transporter expressed at the BBB, is also discussed. The aim of the experimental study was to build a pharmacokinetic (PK) model to describe p-glycoprotein substrate drug concentrations in the brain using commonly measured in vivo parameters of brain distribution. The possibility of replacing in vivo parameter values with their in vitro counterparts was also studied. All data for the study was taken from the literature. A simple 2-compartment PK model was built using the Stella™ software. Brain concentrations of morphine, loperamide and quinidine were simulated and compared with published studies. Correlation of in vitro measured efflux ratio (ER) from different studies was evaluated in addition to studying correlation between in vitro and in vivo measured ER. A Stella™ model was also constructed to simulate an in vitro transcellular monolayer experiment, to study the sensitivity of measured ER to changes in passive permeability and Michaelis-Menten kinetic parameter values. Interspecies differences in rats and mice were investigated with regards to brain permeability and drug binding in brain tissue. Although the PK brain model was able to capture the concentration-time profiles for all 3 compounds in both brain and plasma and performed fairly well for morphine, for quinidine it underestimated and for loperamide it overestimated brain concentrations. Because the ratio of concentrations in brain and blood is dependent on the ER, it is suggested that the variable values cited for this parameter and its inaccuracy could be one explanation for the failure of predictions. Validation of the model with more compounds is needed to draw further conclusions. In vitro ER showed variable correlation between studies, indicating variability due to experimental factors such as test concentration, but overall differences were small. Good correlation between in vitro and in vivo ER at low concentrations supports the possibility of using of in vitro ER in the PK model. The in vitro simulation illustrated that in the simulation setting, efflux is significant only with low passive permeability, which highlights the fact that the cell model used to measure ER must have low enough paracellular permeability to correctly mimic the in vivo situation.