The impact of CpG island methylator phenotype and microsatellite instability on tumour budding in colorectal cancer

In colorectal cancer, tumour budding, a process likened to epithelial mesenchymal transition, is an adverse prognostic factor which is rarely found in tumours with high-level microsatellite instability (MSI-H). Cases with MSI-H or high-level CpG island methylator phenotype (CIMP-H) have similar histomorphological features, yet seemingly opposite prognosis. We hypothesized that tumour budding is related to CIMP, thus partially explaining this prognostic difference.

Increased genome instability in Escherichia coli lon mutants: relation to emergence of multiple-antibiotic-resistant (Mar) mutants caused by insertion sequence elements and large tandem genomic amplifications

Thirteen spontaneous multiple-antibiotic-resistant (Mar) mutants of Escherichia coli AG100 were isolated on Luria-Bertani (LB) agar in the presence of tetracycline (4 microg/ml). The phenotype was linked to insertion sequence (IS) insertions in marR or acrR or unstable large tandem genomic amplifications which included acrAB and which were bordered by IS3 or IS5 sequences. Five different lon mutations, not related to the Mar phenotype, were also found in 12 of the 13 mutants. Under specific selective conditions, most drug-resistant mutants appearing late on the selective plates evolved from a subpopulation of AG100 with lon mutations. That the lon locus was involved in the evolution to low levels of multidrug resistance was supported by the following findings: (i) AG100 grown in LB broth had an important spontaneous subpopulation (about 3.7x10(-4)) of lon::IS186 mutants, (ii) new lon mutants appeared during the selection on antibiotic-containing agar plates, (iii) lon mutants could slowly grow in the presence of low amounts (about 2x MIC of the wild type) of chloramphenicol or tetracycline, and (iv) a lon mutation conferred a mutator phenotype which increased IS transposition and genome rearrangements. The association between lon mutations and mutations causing the Mar phenotype was dependent on the medium (LB versus MacConkey medium) and the antibiotic used for the selection. A previously reported unstable amplifiable high-level resistance observed after the prolonged growth of Mar mutants in a low concentration of tetracycline or chloramphenicol can be explained by genomic amplification.

State affective instability in borderline personality disorder assessed by ambulatory monitoring

BACKGROUND: Although affective instability is an essential criterion for borderline personality disorder (BPD), it has rarely been reported as an outcome criterion. To date, most of the studies assessing state affective instability in BPD using paper-pencil diaries did not find indications of this characteristic, whereas in others studies, the findings were conflicting. Furthermore, the pattern of instability that characterizes BPD has not yet been identified. METHOD: We assessed the affective states of 50 female patients with BPD and 50 female healthy controls (HC) during 24 hours of their everyday life using electronic diaries. RESULTS: In contrast to previous paper-and-pencil diary studies, heightened affective instability for both emotional valence and distress was clearly exhibited in the BPD group but not in the HC group. Inconsistencies in previous papers can be explained by the methods used to calculate instability (see Appendix). In additional, we were able to identify a group-specific pattern of instability in the BPD group characterized by sudden large decreases from positive mood states. Furthermore, 48% of the declines from a very positive mood state in BPD were so large that they reached a negative mood state. This was the case in only 9% of the HC group, suggesting that BPD patients, on average, take less time to fluctuate from a very positive mood state to a negative mood state. CONCLUSION: Future ambulatory monitoring studies will be useful in clarifying which events lead to the reported, sudden decrease in positive mood in BPD patients.

Losses of chromosome arms 4q, 8p, 13q and gain of 8q are correlated with increasing chromosomal instability in hepatocellular carcinoma

OBJECTIVE: Chromosomal instability is a key feature in hepatocellular carcinoma (HCC). Array comparative genomic hybridization (aCGH) revealed recurring structural aberrations, whereas fluorescence in situ hybridization (FISH) indicated an increasing number of numerical aberrations in dedifferentiating HCC. Therefore, we examined whether there was a correlation between structural and numerical aberrations of chromosomal instability in HCC. METHODS AND RESULTS: 27 HCC (5 well, 10 moderately, 12 lower differentiated) already cytogenetically characterized by aCGH were analyzed. FISH analysis using probes for chromosomes 1, 3, 7, 8 and 17 revealed 1.46-4.24 signals/nucleus, which correlated with the histological grade (well vs. moderately,p < 0.0003; moderately vs. lower, p < 0.004). The number of chromosomes to each other was stable with exceptions only seen for chromosome 8. Loss of 4q and 13q, respectively, were correlated with the number of aberrations detected by aCGH (p < 0.001, p < 0.005; Mann-Whitney test). Loss of 4q and gain of 8q were correlated with an increasing number of numerical aberrations detected by FISH (p < 0.020, p < 0.031). Loss of 8p was correlated with the number of structural imbalances seen in aCGH (p < 0.048), but not with the number of numerical changes seen in FISH. CONCLUSION: We found that losses of 4q, 8p and 13q were closely correlated with an increasing number of aberrations detected by aCGH, whereas a loss of 4q and a gain of 8q were also observed in the context of polyploidization, the cytogenetic correlate of morphological dedifferentiation.

Price risk management in the copper market using commodity derivatives and options strategies

Metals price risk management is a key issue related to financial risk in metal markets because of uncertainty of commodity price fluctuation, exchange rate, interest rate changes and huge price risk either to metals’ producers or consumers. Thus, it has been taken into account by all participants in metal markets including metals’ producers, consumers, merchants, banks, investment funds, speculators, traders and so on. Managing price risk provides stable income for both metals’ producers and consumers, so it increases the chance that a firm will invest in attractive projects. The purpose of this research is to evaluate risk management strategies in the copper market. The main tools and strategies of price risk management are hedging and other derivatives such as futures contracts, swaps and options contracts. Hedging is a transaction designed to reduce or eliminate price risk. Derivatives are financial instruments, whose returns are derived from other financial instruments and they are commonly used for managing financial risks. Although derivatives have been around in some form for centuries, their growth has accelerated rapidly during the last 20 years. Nowadays, they are widely used by financial institutions, corporations, professional investors, and individuals. This project is focused on the over-the-counter (OTC) market and its products such as exotic options, particularly Asian options. The first part of the project is a description of basic derivatives and risk management strategies. In addition, this part discusses basic concepts of spot and futures (forward) markets, benefits and costs of risk management and risks and rewards of positions in the derivative markets. The second part considers valuations of commodity derivatives. In this part, the options pricing model DerivaGem is applied to Asian call and put options on London Metal Exchange (LME) copper because it is important to understand how Asian options are valued and to compare theoretical values of the options with their market observed values. Predicting future trends of copper prices is important and would be essential to manage market price risk successfully. Therefore, the third part is a discussion about econometric commodity models. Based on this literature review, the fourth part of the project reports the construction and testing of an econometric model designed to forecast the monthly average price of copper on the LME. More specifically, this part aims at showing how LME copper prices can be explained by means of a simultaneous equation structural model (two-stage least squares regression) connecting supply and demand variables. A simultaneous econometric model for the copper industry is built: {█(Q_t^D=e^((-5.0485))∙P_((t-1))^((-0.1868) )∙〖GDP〗_t^((1.7151) )∙e^((0.0158)∙〖IP〗_t ) @Q_t^S=e^((-3.0785))∙P_((t-1))^((0.5960))∙T_t^((0.1408))∙P_(OIL(t))^((-0.1559))∙〖USDI〗_t^((1.2432))∙〖LIBOR〗_((t-6))^((-0.0561))@Q_t^D=Q_t^S )┤ P_((t-1))^CU=e^((-2.5165))∙〖GDP〗_t^((2.1910))∙e^((0.0202)∙〖IP〗_t )∙T_t^((-0.1799))∙P_(OIL(t))^((0.1991))∙〖USDI〗_t^((-1.5881))∙〖LIBOR〗_((t-6))^((0.0717) Where, Q_t^D and Q_t^Sare world demand for and supply of copper at time t respectively. P(t-1) is the lagged price of copper, which is the focus of the analysis in this part. GDPt is world gross domestic product at time t, which represents aggregate economic activity. In addition, industrial production should be considered here, so the global industrial production growth that is noted as IPt is included in the model. Tt is the time variable, which is a useful proxy for technological change. A proxy variable for the cost of energy in producing copper is the price of oil at time t, which is noted as POIL(t ) . USDIt is the U.S. dollar index variable at time t, which is an important variable for explaining the copper supply and copper prices. At last, LIBOR(t-6) is the 6-month lagged 1-year London Inter bank offering rate of interest. Although, the model can be applicable for different base metals' industries, the omitted exogenous variables such as the price of substitute or a combined variable related to the price of substitutes have not been considered in this study. Based on this econometric model and using a Monte-Carlo simulation analysis, the probabilities that the monthly average copper prices in 2006 and 2007 will be greater than specific strike price of an option are defined. The final part evaluates risk management strategies including options strategies, metal swaps and simple options in relation to the simulation results. The basic options strategies such as bull spreads, bear spreads and butterfly spreads, which are created by using both call and put options in 2006 and 2007 are evaluated. Consequently, each risk management strategy in 2006 and 2007 is analyzed based on the day of data and the price prediction model. As a result, applications stemming from this project include valuing Asian options, developing a copper price prediction model, forecasting and planning, and decision making for price risk management in the copper market.