The integration of settlers into existing socio-environmental settings: reclaiming the greek lands after the Late Medieval Crisis

This chapter examines to what extent two late medieval nomadic groups in the southern Balkans adopted the economic practices of the areas they moved into, in order to achieve agricultural sustainability. In the fourteenth century, these two groups, Turk yörüks and transhumant Albanians, migrated to Greece in order to invigorate depopulated areas and reclaim lands in Thessaly and the Peloponnese respectively. Almost three generations after their establishment, Ottoman taxation cadastres cast light on their agricultural and pastoral activities. Even though these groups followed different trajectories in their sedentarisation—more or less dictated by their ethnocultural peculiarities—they both focused over time on farming basic, life-sustaining crops, such as cereals, which were complimentary to the manifold market-oriented farming activities of the long-settled local Greeks

Evaluation of methods for predicting the topology of β-barrel outer membrane proteins and a consensus prediction method

BACKGROUND: Prediction of the transmembrane strands and topology of β-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of β-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 β-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. RESULTS: We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane β-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. CONCLUSIONS: The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at

Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins

BACKGROUND: Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. RESULTS: We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM's classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications. CONCLUSION: The algorithms presented here, are easily implemented in any kind of a Hidden Markov Model, whereas the prediction method (HMM-TM) is freely available for academic users at , offering the most advanced decoding options currently available

Suppression of humoral immune response to hepatitis B surface antigen vaccine in BALB/c mice by 1-methyl-tryptophan co-administration

  Background and the purpose of the study:Indoleamine 2,3-dioxygenase (IDO) suppresses adaptive immune response. The purpose of this study was to determine the effect of the IDO inhibitor namely 1-methyl-DL-tryptophan (DL-1-MT) on antibody production after vaccination with hepatitis B surface (HBs) antigen. Methods:Four groups of BALB/c mice were immunized with a HBs antigen vaccine. In the first group the vaccine had no DL-1-MT, whereas in the other three groups the vaccine contained 1 mg , 10 mg and 20 mg DL-1-MT. Blood samples were collected 5 weeks post-vaccination and anti-HBs antibodies in the serum were measured by ELISA. Results:Compared to the three groups of mice that were immunized with the vaccines containing DL-1-MT, serum anti-HBs level was much higher in the mice that were immunized with the vaccine with out DL-1-MT. Conclusions:Inhibition of IDO at the time of vaccination decreased humoral immune response to HBs antigen vaccine. The idea that IDO activity is simply immunosuppressive may need to be re-evaluated

A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins

BACKGROUND: Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. RESULTS: The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. CONCLUSION: Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: , and it is the only freely available HMM-based predictor for β-barrel outer membrane protein topology

The incidence and risk factors for new onset atrial fibrillation in the PROSPER study

Aims Atrial fibrillation/flutter (AF) is the most common arrhythmia in older people. It associates with reduced exercise capacity, increased risk of stroke, and mortality. We aimed to determine retrospectively whether pravastatin reduces the incidence of AF and whether any electrocardiographic measures or clinical conditions might be risk factors for its development. Methods and results The PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) was a randomized, double-blind controlled trial that recruited 5804 individuals aged 70-82 years with a history of, or risk factors for, vascular disease. A total of 2891 were allocated to pravastatin and 2913 to placebo; mean follow-up was 3.2 years. Electrocardiograms (ECGs), which were recorded at baseline, annually thereafter, and at run-out, were processed by computer and reviewed manually. In all, 264 of 2912 (9.1%) of the placebo group and 283 of 2888 (9.8%) of the pravastatin-treated group developed AF [hazard ratio 1.08 (0.92,1.28), P = 0.35)]. Multivariate analysis showed that PR and QTc intervals, age, left ventricular hypertrophy, and ST-T abnormalities were related to development of AF after adjustment for many variables including alcohol consumption, which itself was univariately predictive of developing AF. Previous myocardial infarction on the ECG was not a risk factor. A history of vascular disease was strongly linked with developing AF but not diabetes and hypertension. Conclusion Pravastatin does not reduce the incidence of AF in older people at risk of vascular disease, at least in the short-medium term. Risk factors for AF include older age, prolongation of PR or QTc intervals, left ventricular hypertrophy, and ST-T abnormalities on the EC