Coexistence of high-bit-rate quantum key distribution and data on optical fiber

Quantum key distribution (QKD) uniquely allows distribution of cryptographic keys with security verified by quantum mechanical limits. Both protocol execution and subsequent applications require the assistance of classical data communication channels. While using separate fibers is one option, it is economically more viable if data and quantum signals are simultaneously transmitted through a single fiber. However, noise-photon contamination arising from the intense data signal has severely restricted both the QKD distances and secure key rates. Here, we exploit a novel temporal-filtering effect for noise-photon rejection. This allows high-bit-rate QKD over fibers up to 90 km in length and populated with error-free bidirectional Gb/s data communications. With high-bit rate and range sufficient for important information infrastructures, such as smart cities and 10 Gbit Ethernet, QKD is a significant step closer towards wide-scale deployment in fiber networks.

Digital elevation models and GIS for watershed modelling and flood prediction-A case study of Accra Ghana

Geographical Information Systems (GIS) and Digital Elevation Models (DEM) can be used to perform many geospatial and hydrological modelling including drainage and watershed delineation, flood prediction and physical development studies of urban and rural settlements. This paper explores the use of contour data and planimetric features extracted from topographic maps to derive digital elevation models (DEMs) for watershed delineation and flood impact analysis (for emergency preparedness) of part of Accra, Ghana in a GIS environment. In the study two categories of DEMs were developed with 5 m contour and planimetric topographic data; bare earth DEM and built environment DEM. These derived DEMs were used as terrain inputs for performing spatial analysis and obtaining derivative products. The generated DEMs were used to delineate drainage patterns and watershed of the study area using ArcGIS desktop and its ArcHydro extension tool from Environmental Systems Research Institute (ESRI). A vector-based approach was used to derive inundation areas at various flood levels. The DEM of built-up areas was used as inputs for determining properties which will be inundated in a flood event and subsequently generating flood inundation maps. The resulting inundation maps show that about 80% areas which have perennially experienced extensive flooding in the city falls within the predicted flood extent. This approach can therefore provide a simplified means of predicting the extent of inundation during flood events for emergency action especially in less developed economies where sophisticated technologies and expertise are hard to come by. © 2009 Springer Netherlands.

Bayesian mixed models and divergence time estimation of Chinese cavefishes (Cyprinidae : Sinocyclocheilus)

The genus Sinocyclocheilus is distributed in Yun-Gui Plateau and its surrounding region only, within more than 10 cave species showing different degrees of degeneration of eyes and pigmentation with wonderful adaptations. To present, published morphological and molecular phylogenetic hypotheses of Sinocyclocheilus from prior works are very different and the relationships within the genus are still far from clear. We obtained the sequences of cytochrome b (cyt b) and NADH dehydrogenase subunit 4 (ND4) of 34 species within Sinocyclocheilus, which represent the most dense taxon sampling to date. We performed Bayesian mixed models analyses with this data set. Under this phylogenetic framework, we estimated the divergence times of recovered clades using different methods under relaxed molecular clock. Our phyloegentic results supported the monophyly of Sinocyclocheilus and showed that this genus could be subdivided into 6 major clades. In addition, an earlier finding demonstrating the polyphyletic of cave species and the most basal position of S. jii was corroborated. Relaxed divergence-time estimation suggested that Sinocyclocheilus originated at the late Miocene, about 11 million years ago (Ma), which is older than what have been assumed.

Digital Elevation Models and GIS for watershed modelling and flood prediction - A case study of Accra Ghana

Geographical Information Systems (GIS) and Digital Elevation Models (DEM) can be used to perform many geospatial and hydrological modelling including drainage and watershed delineation, flood prediction and physical development studies of urban and rural settlements. This paper explores the use of contour data and planimetric features extracted from topographic maps to derive digital elevation models (DEMs) for watershed delineation and flood impact analysis (for emergency preparedness) of part of Accra, Ghana in a GIS environment. In the study two categories of DEMs were developed with 5 m contour and planimetric topographic data; bare earth DEM and built environment DEM. These derived DEMs were used as terrain inputs for performing spatial analysis and obtaining derivative products. The generated DEMs were used to delineate drainage patterns and watershed of the study area using ArcGIS desktop and its ArcHydro extension tool from Environmental Systems Research Institute (ESRI). A vector-based approach was used to derive inundation areas at various flood levels. The DEM of built-up areas was used as inputs for determining properties which will be inundated in a flood event and subsequently generating flood inundation maps. The resulting inundation maps show that about 80% areas which have perennially experienced extensive flooding in the city falls within the predicted flood extent. This approach can therefore provide a simplified means of predicting the extent of inundation during flood events for emergency action especially in less developed economies where sophisticated technologies and expertise are hard to come by. © Springer Science + Business Media B.V. 2009.

Accuracy and transferability of GAP models for tungsten

We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian Approximation Potential (GAP) framework, fitted to a database of first principles density functional theory (DFT) calculations. We investigate the performance of a sequence of models based on databases of increasing coverage in configuration space and showcase our strategy of choosing representative small unit cells to train models that predict properties only observable using thousands of atoms. The most comprehensive model is then used to calculate properties of the screw dislocation, including its structure, the Peierls barrier and the energetics of the vacancy-dislocation interaction. All software and raw data are available at www.libatoms.org.

Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals

Enot, D. P., Beckmann, M., Overy, D., Draper, J. (2006). Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals. Proceedings of the National Academy of Sciences of the USA, 103(40), 14865-14870. Sponsorship: BBSRC RAE2008

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Towards the enantioselective synthesis of Plakortide P and its application to the development of drugs for the treatment of Chagas disease

The primary focus of this thesis was the asymmetric peroxidation of α,β-unsaturated aldehydes and the development of this methodology to include the synthesis of bioactive chiral 1,2-dioxane and 1,2-dioxalane rings. In Chapter 1 a review detailing the new and improved methods for the acyclic introduction of peroxide functionality to substrates over the last decade was discussed. These include a detailed examination of metal-mediated transformations, chiral peroxidation using organocatalytic means and the improvements in methodology of well-established peroxidation pathways. The second chapter discusses the method by which peroxidation of our various substrates was attempted and the optimisation studies associated with these reactions. The method by which the enantioselectivity of our β-peroxyaldehydes was determined is also reviewed. Chapters 3 and 4 focus on improving the enantioselectivity associated with our asymmetric peroxidation reaction. A comprehensive analysis exploring the effect of solvent, concentration and temperature on enantioselectivity was examined. The effect that different catalytic systems have on enantioselectivity and reactivity was also investigated in depth. Chapter 5 details the various transformations that β-peroxyaldehydes can undergo and the manipulation of these transformations towards the establishment of several routes for the formation of chiral 1,2-dioxane and 1,2-dioxalane rings. Chapter 6 details the full experimental procedures, including spectroscopic and analytical data for the compounds prepared during this research.

Trophic role of small cetaceans and seals in Irish waters

Understanding the role of marine mammals in specific ecosystems and their interactions with fisheries involves, inter alia, an understanding of their diet and dietary requirements. In this thesis, the foraging ecology of seven marine mammal species that regularly occur in Irish waters was investigated by reconstructing diet using hard parts from digestive tracts and scats. Of the species examined, two (striped and Atlantic white-sided dolphin) can be considered offshore species or species inhabiting neritic waters, while five others usually inhabit more coastal areas (white-beaked dolphin, harbour porpoise, harbour seal and grey seal); the last species studied was the bottlenose dolphin whose population structure is more complex, with coastal and offshore populations. A total of 13,028 prey items from at least 81 different species (62 fish species, 14 cephalopods, four crustaceans, and a tunicate) were identified. 28% of the fish species were identified using bones other than otoliths, highlighting the importance of using all identifiable structures to reconstruct diet. Individually, each species of marine mammal presented a high diversity of prey taxa, but the locally abundant Trisopterus spp. were found to be the most important prey item for all species, indicating that Trisopterus spp. is probably a key species in understanding the role of these predators in Irish waters. In the coastal marine mammals, other Gadiformes species (haddock, pollack, saithe, whiting) also contributed substantially to the diet; in contrast, in pelagic or less coastal marine mammals, prey was largely comprised of planktivorous fish, such as Atlantic mackerel, horse mackerel, blue whiting, and mesopelagic prey. Striped dolphins and Atlantic white-sided dolphins are offshore small cetaceans foraging in neritic waters. Differences between the diet of striped dolphins collected in drift nets targeting tuna and stranded on Irish coasts showed a complex foraging behaviour; the diet information shows that although this dolphin forages mainly in oceanic waters it may occasionally forage on the continental shelf, feeding on available prey. The Atlantic white-sided dolphin diet showed that this species prefers to feed over the continental edge, where planktivorous fish are abundant. Some resource partitioning was found in bottlenose dolphins in Irish waters consistent with previous genetic and stable isotope analysis studies. Bottlenose dolphins in Irish waters appears to be generalist feeders consuming more than 30 prey species, however most of the diet comprised a few locally abundant species, especially gadoid fish including haddock/pollack/saithe group and Trisopterus spp., but the contribution of Atlantic hake, conger eels and the pelagic planktivorous horse mackerel were also important. Stomach content information suggests that three different feeding behaviours might occur in bottlenose dolphin populations in Irish waters; firstly a coastal behaviour, with animals feeding on prey that mainly inhabit areas close to the coast; secondly an offshore behaviour where dolphins feed on offshore species such as squid or mesopelagic fish; and a third more complex behaviour that involves movements over the continental shelf and close to the shelf edge. The other three coastal marine mammal species (harbour porpoise, harbour seal and grey seal) were found to be feeding on similar prey and competition for food resources among these sympatric species might occur. Both species of seals were found to have a high overlap (more than 80%) in their diet composition, but while grey seals feed on large fish (>110mm), harbour seals feed mostly on smaller fish (<110mm), suggesting some spatial segregation in foraging. Harbour porpoises and grey seals are potentially competing for the same food resource but some differences in prey species were found and some habitat partitioning might occur. Direct interaction (by catch) between dolphins and fisheries was detected in all species. Most of the prey found in the stomach contents from both stranded and by catch dolphins were smaller sizes than those targeted by commercial fisheries. In fact, the total annual food consumption of the species studied was found to be very small (225,160 tonnes) in comparison to fishery landings for the same area (~2 million tonnes). However, marine mammal species might be indirectly interacting with fisheries, removing forage fish. Incorporating the dietary information obtained from the four coastal species, an ECOPATH food web model was established for the Irish Sea, based on data from 2004. Five trophic levels were found, with bottlenose dolphins and grey and harbour seals occurring at the highest trophic level. A comparison with a previous model based on 1973 data suggests that while the overall Irish Sea ecosystem appears to be “maturing”, some indices indicate that the 2004 fishery was less efficient and was targeting fish at higher trophic levels than in 1973, which is reflected in the mean trophic level of the catch. Depletion or substantial decrease of some of the Irish Sea fish stocks has resulted in a significant decline in landings in this area. The integration of diet information in mass-balance models to construct ecosystem food-webs will help to understand the trophic role of these apex predators within the ecosystem.

Nonparametric estimation of structural models for high-frequency currency market data

Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities. This paper investigates whether an equilibrium monetary model can account for nonlinearities in weekly data. The model incorporates time-nonseparable preferences and a transaction cost technology. Simulated sample paths are generated using Marcet's parameterized expectations procedure. The paper also develops a new method for estimation of structural economic models. The method forces the model to match (under a GMM criterion) the score function of a nonparametric estimate of the conditional density of observed data. The estimation uses weekly U.S.-German currency market data, 1975-90. © 1995.

A cross-site, comparative effectiveness study of an integrated HIV and substance use treatment program.

Co-occurrence of HIV and substance abuse is associated with poor outcomes for HIV-related health and substance use. Integration of substance use and medical care holds promise for HIV patients, yet few integrated treatment models have been reported. Most of the reported models lack data on treatment outcomes in diverse settings. This study examined the substance use outcomes of an integrated treatment model for patients with both HIV and substance use at three different clinics. Sites differed by type and degree of integration, with one integrated academic medical center, one co-located academic medical center, and one co-located community health center. Participants (n=286) received integrated substance use and HIV treatment for 12 months and were interviewed at 6-month intervals. We used linear generalized estimating equation regression analysis to examine changes in Addiction Severity Index (ASI) alcohol and drug severity scores. To test whether our treatment was differentially effective across sites, we compared a full model including site by time point interaction terms to a reduced model including only site fixed effects. Alcohol severity scores decreased significantly at 6 and 12 months. Drug severity scores decreased significantly at 12 months. Once baseline severity variation was incorporated into the model, there was no evidence of variation in alcohol or drug score changes by site. Substance use outcomes did not differ by age, gender, income, or race. This integrated treatment model offers an option for treating diverse patients with HIV and substance use in a variety of clinic settings. Studies with control groups are needed to confirm these findings.

Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.

BACKGROUND: Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed and Cochrane databases (2000-2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e(-lambdat)) where lambda was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. CONCLUSION/SIGNIFICANCE: Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.