Histogram Reweighting Method for Dynamic Properties

The histogram reweighting technique, widely used to analyze Monte Carlo data, is shown to be applicable to dynamic properties obtained from Molecular Dynamics simulations. The theory presented here is based on the fact that the correlation functions in systems in thermodynamic equilibrium are averages over initial conditions of functions of the trajectory of the system in phase-space, the latter depending on the volume, the total number of particles and the classical Hamiltonian. Thus, the well-known histogram reweighting method can almost straightforwardly be applied to reconstruct the probability distribution of initial states at different thermodynamic conditions, without extra computational effort. Correlation functions and transport coefficients are obtained with this method from few simulation data sets.Comment: 4 pages, 3 figure

Emerging resistance among bacterial pathogens in the intensive care unit – a European and North American Surveillance study (2000–2002)

Background Globally ICUs are encountering emergence and spread of antibiotic-resistant pathogens and for some pathogens there are few therapeutic options available. Methods Antibiotic in vitro susceptibility data of predominant ICU pathogens during 2000–2 were analyzed using data from The Surveillance Network (TSN) Databases in Europe (France, Germany and Italy), Canada, and the United States (US). Results Oxacillin resistance rates among Staphylococcus aureus isolates ranged from 19.7% to 59.4%. Penicillin resistance rates among Streptococcus pneumoniae varied from 2.0% in Germany to as high as 20.2% in the US; however, ceftriaxone resistance rates were comparably lower, ranging from 0% in Germany to 3.4% in Italy. Vancomycin resistance rates among Enterococcus faecalis were ≤ 4.5%; however, among Enterococcus faecium vancomycin resistance rates were more frequent ranging from 0.8% in France to 76.3% in the United States. Putative rates of extended-spectrum β-lactamase (ESBL) production among Enterobacteriaceae were low, \u3c6% among Escherichia coli in the five countries studied. Ceftriaxone resistance rates were generally lower than or similar to piperacillin-tazobactam for most of the Enterobacteriaceae species examined. Fluoroquinolone resistance rates were generally higher for E. coli (6.5% – 13.9%), Proteus mirabilis (0–34.7%), and Morganella morganii (1.6–20.7%) than other Enterobacteriaceae spp (1.5–21.3%). P. aeruginosa demonstrated marked variation in β-lactam resistance rates among countries. Imipenem was the most active compound tested against Acinetobacter spp., based on resistance rates. Conclusion There was a wide distribution in resistance patterns among the five countries. Compared with other countries, Italy showed the highest resistance rates to all the organisms with the exception of Enterococcus spp., which were highest in the US. This data highlights the differences in resistance encountered in intensive care units in Europe and North America and the need to determine current local resistance patterns by which to guide empiric antimicrobial therapy for intensive care infections

Predictability of evolutionary trajectories in fitness landscapes

Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution. However, these experiments deal with individual enzymes and explore a tiny part of the fitness landscape. We report an exhaustive analysis of fitness landscapes constructed with an off-lattice model of protein folding where fitness is equated with robustness to misfolding. This model mimics the essential features of the interactions between amino acids, is consistent with the key paradigms of protein folding and reproduces the universal distribution of evolutionary rates among orthologous proteins. We introduce mean path divergence as a quantitative measure of the degree to which the starting and ending points determine the path of evolution in fitness landscapes. Global measures of landscape roughness are good predictors of path divergence in all studied landscapes: the mean path divergence is greater in smooth landscapes than in rough ones. The model-derived and experimental landscapes are significantly smoother than random landscapes and resemble additive landscapes perturbed with moderate amounts of noise; thus, these landscapes are substantially robust to mutation. The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness. We suggest that smoothness and the substantial deficit of peaks in the fitness landscapes of protein evolution are fundamental consequences of the physics of protein folding.Comment: 14 pages, 7 figure

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

C1–C2 Instability Associated with Periodontoid Inflammatory Tissue Leading to Subarachnoid Hemorrhage: A Case Report and Review of the Literature

Abstract The authors present a case of atlantoaxial instability associated with C1–C2 inflammatory tissue leading to subarachnoid hemorrhage. A 65-year-old male patient arrived in June 2011 to the emergency unit for cervical pain and fever. Imaging studies documented periodontoid pseudotumor at C1–C2 level. Infective disease was suspected; the patient was therefore hospitalized and treated with antibiotics. Subsequent computed tomographic (CT) scans revealed C1–C2 instability. In August, the patient showed acute neurological deterioration and coma. Urgent brain CT revealed a hemorrhagic lesion which caused compression on the medulla oblongata, subarachnoid hemorrhage, and ventricular dilatation. An external ventricular drainage was positioned. Angio-CT and angiography did not show any vascular abnormalities. Cervical magnetic resonance imaging documented a solid tissue lesion between the atlas arch and axis. The lesion was associated with an epidural and subdural hematoma, exerting compression on brainstem. The patient underwent posterior decompression and C1–C2 fusion according to Harms technique in October, with significant clinical improvement. The authors present a case of atlantoaxial instability associated with a periodontoid pseudotumor at C1–C2 level determining dural sac compression. The patient showed an acute neurological deterioration caused by bleeding of the solid component of the cervical lesion. Hemorrhage of the solid component of periodontoid masses linked to atlantoaxial instability has not yet been reported in literature. To the best of our knowledge, this is the first case of C1–C2 instability with periodontoid pseudotumor leading to subarachnoid hemorrhage

Exchange rate predictability in a changing world

An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world, however, Taylor rule parameters may be subject to structural instabilities, for example in the aftermath of the Global Financial Crisis. This paper forecasts exchange rates using Taylor rules with Time-Varying Parameters (TVP) estimated by Bayesian methods. Focusing on the data from the crisis, we improve upon the random walk for at least half, and for as many as seven out of 10, of the currencies considered. Results are stronger when we allow the TVP of the Taylor rules to differ between countries

Effects of interspecific gene flow on the phenotypic variance–covariance matrix in Lake Victoria Cichlids

Quantitative genetics theory predicts adaptive evolution to be constrained along evolutionary lines of least resistance. In theory, hybridization and subsequent interspecific gene flow may, however, rapidly change the evolutionary constraints of a population and eventually change its evolutionary potential, but empirical evidence is still scarce. Using closely related species pairs of Lake Victoria cichlids sampled from four different islands with different levels of interspecific gene flow, we tested for potential effects of introgressive hybridization on phenotypic evolution in wild populations. We found that these effects differed among our study species. Constraints measured as the eccentricity of phenotypic variance–covariance matrices declined significantly with increasing gene flow in the less abundant species for matrices that have a diverged line of least resistance. In contrast, we find no such decline for the more abundant species. Overall our results suggest that hybridization can change the underlying phenotypic variance–covariance matrix, potentially increasing the adaptive potential of such populations

Bayesboost: Identifying and Handling Bias Using Synthetic Data Generators

Paper presented at the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications (LIDTA 2021), online, 17 September 2021.Advanced synthetic data generators can model sensitive personal datasets by creating simulated samples of data with realistic correlation structures and distributions, but with a greatly reduced risk of identifying individuals. This has huge potential in medicine where sensitive patient data can be simulated and shared, enabling the development and robust validation of new AI technologies for diagnosis and disease management. However, even when massive ground truth datasets are available (such as UK-NHS databases which contain patient records in the order of millions) there is a high risk that biases still exist which are carried over to the data generators. For example, certain cohorts of patients may be under-represented due to cultural sensitivities amongst some communities, or due to institutionalised procedures in data collection. The under-representation of groups is one of the forms in which bias can manifest itself in machine learning, and it is the one we investigate in this work.These factors may also lead to structurally missing data or incorrect correlations and distributions which will be mirrored in the synthetic data generated from biased ground truth datasets. In this paper, we explore methods to improve synthetic data generators by using probabilistic methods to firstly identify the difficult to predict data samples in ground truth data, and then to boost these types of data when generating synthetic samples. The paper explores attempts to create synthetic data that contain more realistic distributions and that lead to predictive models with better performance.NHSX

The sovereign debt crisis: the impact on the intermediation model of Italian banks

The aim of the contribute is to analyze the impact of the financial crisis, in particular since the start of the sovereign debt phase, on Italian banks and their intermediation model. Italian banks\u2019 specific business model explains why they suffered less than those of other countries during the first phase of the crisis, requiring one of the lowest levels of public facilities in the EC as compared to GDP. Most of these same characteristics have changed from positive to negative factors since the sovereign debt crisis, which hit Italy hard, affecting first banks\u2019 liquidity and secondly the cost and volumes of funding and loans. Italian banks are now facing the effects of the double-dip recession, which has significantly weakened businesses and households, their key customer segments, and their borrowing and saving capability, with an increasing rate of non-performing loans. This situation is impairing the sustainability of the \u201ctraditional\u201d intermediation model and means that banks must introduce strategies for significantly modifying the banking business model they adopt

The transmission of unconventional monetary policy to bank credit supply : evidence from the TLTRO

We assess the transmission of the Targeted Longer-Term Refinancing Operations (TLTRO) to the bank credit supply for the Euro area (2014:05-2018:01) and for Portugal (2011:01-2018:01), using a panel data setup. For the Euro area, we find a positive relationship between the TLTRO and the amount of credit granted to the real economy. For the vulnerable countries, the effects of the TLTRO on the stock of credit increased from 2016 to 2017. Among the group of small banks, the effects are stronger in less vulnerable countries. We also find that competition has no statistically significant impact on the transmission of the TLTRO to the bank credit supply for the Euro area. For Portugal, using a difference-in-differences model, we find no statistically significant impact of the TLTRO on credit granted by banks. Finally, bidding banks set lower interest rates than non-bidding banks and the difference seems to be larger in 2017. In Portugal, the effects of the TLTRO on loan interest rates also increased from 2016 to 2017 and are stronger for small banks.info:eu-repo/semantics/publishedVersio