Evaluating mortality using length-frequency data when growth parameters are poorly known

A method is presented through which the total mortality undergone by several fish stocks of the same species can be compared when growth parameters are poorly known or unknown. Whereas the estimate of Z obtained via the length-converted catch curve is highly sensitive to the input parameters K and L sub( infinity ), the ratio of Z estimates obtained for different stocks with the same combination of parameters is almost independent of these inputs, at least when the fit of the linear regression is good. The method is tested on simulated data and an application is presented using real data from the Lesser Antilles. It provides the possibility of qualitatively comparing several stocks in situations of scarce biological knowledge.

Characterizing a cyanobacterial bloom in western Lake Erie using satellite imagery and meteorological data

The distribution and intensity of a bloom of the toxic cyanobacterium, Microcystis aeruginosa, in western Lake Erie was characterized using a combination of satellite ocean-color imagery, field data, and meteorological observations. The bloom was first identified by satellite on 14 August 2008 and persisted for more than 2 months. The distribution and intensity of the bloom was estimated using a satellite algorithm that is sensitive to near-surface concentrations of M. aeruginosa. Increases in both area and intensity were most pronounced for wind stress less than 0.05 Pa. Area increased while intensity did not change for wind stresses of 0.05–0.1 Pa, and both decreased for wind stress greater than 0.1 Pa. The recovery in intensity at the surface after strong wind events indicated that high wind stress mixed the bloom through the water column and that it returned to the surface once mixing stopped. This interaction is consistent with the understanding of the buoyancy of these blooms. Cloud cover (reduced light) may have a weak influence on intensity during calm conditions. While water temperature remained greater than 15°C, the bloom intensified if there were calm conditions. For water temperature less than 15°C, the bloom subsided under similar conditions. As a result, wind stress needs to be considered when interpreting satellite imagery of these blooms.

On the problem of nonadditivity in two-way analysis of variance of data for comparing the efficiency of fishing gears

To bring out the relative efficiency of various types of fishing gears, in the analysis of catch data, a combination of Tukey's test, consequent transformation and graphical analysis for outlier elimination has been introduced, which can be advantageously used for applying ANOVA techniques, Application of these procedures to actual sets of data showed that nonadditivity in the data was caused by either the presence of outliers, or the absence of a suitable transformation or both. As a corollary, the concurrent model: X sub(ij) = µ + α sub(i) + β sub(j) + λ α sub(i) β sub(j) + E sub(ij) adequately fits the data.

Transcription of multi-genre media archives using out-of-domain data

We describe our work on developing a speech recognition system for multi-genre media archives. The high diversity of the data makes this a challenging recognition task, which may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks (MLAN), a novel technique for incorporating information from out-of-domain posterior features using deep neural networks. We show that it provides a substantial reduction in WER over other systems, with relative WER reductions of 15% over a PLP baseline, 9% over in-domain tandem features and 8% over the best out-of-domain tandem features. © 2012 IEEE.

System combination and score normalization for spoken term detection

Spoken content in languages of emerging importance needs to be searchable to provide access to the underlying information. In this paper, we investigate the problem of extending data fusion methodologies from Information Retrieval for Spoken Term Detection on low-resource languages in the framework of the IARPA Babel program. We describe a number of alternative methods improving keyword search performance. We apply these methods to Cantonese, a language that presents some new issues in terms of reduced resources and shorter query lengths. First, we show score normalization methodology that improves in average by 20% keyword search performance. Second, we show that properly combining the outputs of diverse ASR systems performs 14% better than the best normalized ASR system. © 2013 IEEE.

Polarization multiplexed write-once-read-many optical data storage in bacteriorhodopsin films

In polymeric films of bacteriorhodopsin (BR) a photoconversion product, which was named the F620 state, was observed on excitation of the film with 532 nm nanosecond laser pulses. This photoproduct shows a strong nonlinear absorption. Such BR films can be used for write-once-read-many (WORM) optical data storage. We demonstrate that a photoproduct similar or even identical to that obtained with nanosecond pulses is generated on excitation with 532 mn femtosecond pulses. This photoproduct also shows strong anisotropic absorption, which facilitates polarization storage of data. The product is thermally stable and is irretrievable to the initial B state either by photochemical reaction or through a thermal pathway. The experimental results indicate that the product is formed by a two-photon absorption process. Optical WORM storage is demonstrated by use of two polarization states, but more polarization states may be used. The combination of polarization data multiplexing and extremely short recording time in the femtosecond range enables very high data volumes to be stored within a very short time. (c) 2005 Optical Society of America.

High-throughput synergy screening identifies microbial metabolites as combination agents for the treatment of fungal infections

The high mortality rate of immunocompromised patients with fungal infections and the limited availability of highly efficacious and safe agents demand the development of new antifungal therapeutics. To rapidly discover such agents, we developed a high-throughput synergy screening (HTSS) strategy for novel microbial natural products. Specifically, a microbial natural product library was screened for hits that synergize the effect of a low dosage of ketoconazole (KTC) that alone shows little detectable fungicidal activity. Through screening of approximate to 20,000 microbial extracts, 12 hits were identified with broadspectrum antifungal activity. Seven of them showed little cytotoxicity against human hepatoma cells. Fractionation of the active extracts revealed beauvericin (BEA) as the most potent component, because it dramatically synergized KTC activity against diverse fungal pathogens by a checkerboard assay. Significantly, in our immunocompromised mouse model, combinations of BEA (0.5 mg/kg) and KTC (0.5 mg/kg) prolonged survival of the host infected with Candida parapsilosis and reduced fungal colony counts in animal organs including kidneys, lungs, and brains. Such an effect was not achieved even with the high dose of 50 mg/kg KTC. These data support synergism between BEA and KTC and thereby a prospective strategy for antifungal therapy.

Reference genes for quantitative RT-PCR data in gastric tissues and cell lines

AIM: To evaluate the suitability of reference genes in gastric tissue samples and cell lines.METHODS: the suitability of genes ACTB, B2M, GAPDH, RPL29, and 18S rRNA was assessed in 21 matched pairs of neoplastic and adjacent nonneoplastic gastric tissues from patients with gastric adenocarcinoma, 27 normal gastric tissues from patients without cancer, and 4 cell lines using reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). the ranking of the best single and combination of reference genes was determined by NormFinder, geNorm (TM), BestKeeper, and DataAssist (TM). in addition, GenEx software was used to determine the optimal number of reference genes. To validate the results, the mRNA expression of a target gene, DNMT1, was quantified using the different reference gene combinations suggested by the various software packages for normalization.RESULTS: ACTB was the best reference gene for all gastric tissues, cell lines and all gastric tissues plus cell lines. GAPDH + B2M or ACTB + B2M was the best combination of reference genes for all the gastric tissues. On the other hand, ACTB + B2M was the best combination for all the cell lines tested and was also the best combination for analyses involving all the gastric tissues plus cell lines. According to the GenEx software, 2 or 3 genes were the optimal number of references genes for all the gastric tissues. the relative quantification of DNMT1 showed similar patterns when normalized by each combination of reference genes. the level of expression of DNMT1 in neoplastic, adjacent non-neoplastic and normal gastric tissues did not differ when these samples were normalized using GAPDH + B2M (P = 0.32), ACTB + B2M (P = 0.61), or GAPDH + B2M + ACTB (P = 0.44).CONCLUSION: GAPDH + B2M or ACTB + B2M is the best combination of reference gene for all the gastric tissues, and ACTB + B2M is the best combination for the cell lines tested. (C) 2013 Baishideng Publishing Group Co., Limited. All rights reserved.

Confirmation of Data Mining Based Predictions of Protein Function

King, R. D. and Wise, P. H. and Clare, A. (2004) Confirmation of Data Mining Based Predictions of Protein Function. Bioinformatics 20(7), 1110-1118

Representation, comparison, and interpretation of metabolome fingerprint data for total composition analysis and quality trait investigation in potato cultivars

Manfred Beckmann, David P. Enot, David P. Overy, and John Draper (2007). Representation, comparison, and interpretation of metabolome fingerprint data for total composition analysis and quality trait investigation in potato cultivars. Journal of Agricultural and Food Chemistry, 55 (9) pp.3444-3451 RAE2008

Taxanes alone or in combination with anthracyclines as first-line therapy of patients with metastatic breast cancer.

PURPOSE: Taxanes (paclitaxel or docetaxel) have been sequenced or combined with anthracyclines (doxorubicin or epirubicin) for the first-line treatment of advanced breast cancer. This meta-analysis uses data from all relevant trials to detect any advantages of taxanes in terms of tumor response, progression-free survival (PFS), and survival. PATIENTS AND METHODS: Individual patient data were collected on eight randomized combination trials comparing anthracyclines + taxanes (+ cyclophosphamide in one trial) with anthracyclines + cyclophosphamide (+ fluorouracil in four trials), and on three single-agent trials comparing taxanes with anthracyclines. Combination trials included 3,034 patients; single-agent trials included 919 patients. RESULTS: Median follow-up of living patients was 43 months, median survival was 19.3 months, and median PFS was 7.1 months. In single-agent trials, response rates were similar in the taxanes (38%) and in the anthracyclines (33%) arms (P = .08). The hazard ratios for taxanes compared with anthracyclines were 1.19 (95% CI, 1.04 to 1.36; P = .011) for PFS and 1.01 (95% CI, 0.88 to 1.16; P = .90) for survival. In combination trials, response rates were 57% (10% complete) in taxane-based combinations and 46% (6% complete) in control arms (P < .001). The hazard ratios for taxane-based combinations compared with control arms were 0.92 (95% CI, 0.85 to 0.99; P = .031) for PFS and 0.95 (95% CI, 0.88 to 1.03; P = .24) for survival. CONCLUSION: Taxanes were significantly worse than single-agent anthracyclines in terms of PFS, but not in terms of response rates or survival. Taxane-based combinations were significantly better than anthracycline-based combinations in terms of response rates and PFS, but not in terms of survival.

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.

Evolutionary characters, phenotypes and ontologies: curating data from the systematic biology literature.

BACKGROUND: The wealth of phenotypic descriptions documented in the published articles, monographs, and dissertations of phylogenetic systematics is traditionally reported in a free-text format, and it is therefore largely inaccessible for linkage to biological databases for genetics, development, and phenotypes, and difficult to manage for large-scale integrative work. The Phenoscape project aims to represent these complex and detailed descriptions with rich and formal semantics that are amenable to computation and integration with phenotype data from other fields of biology. This entails reconceptualizing the traditional free-text characters into the computable Entity-Quality (EQ) formalism using ontologies. METHODOLOGY/PRINCIPAL FINDINGS: We used ontologies and the EQ formalism to curate a collection of 47 phylogenetic studies on ostariophysan fishes (including catfishes, characins, minnows, knifefishes) and their relatives with the goal of integrating these complex phenotype descriptions with information from an existing model organism database (zebrafish, http://zfin.org). We developed a curation workflow for the collection of character, taxonomic and specimen data from these publications. A total of 4,617 phenotypic characters (10,512 states) for 3,449 taxa, primarily species, were curated into EQ formalism (for a total of 12,861 EQ statements) using anatomical and taxonomic terms from teleost-specific ontologies (Teleost Anatomy Ontology and Teleost Taxonomy Ontology) in combination with terms from a quality ontology (Phenotype and Trait Ontology). Standards and guidelines for consistently and accurately representing phenotypes were developed in response to the challenges that were evident from two annotation experiments and from feedback from curators. CONCLUSIONS/SIGNIFICANCE: The challenges we encountered and many of the curation standards and methods for improving consistency that we developed are generally applicable to any effort to represent phenotypes using ontologies. This is because an ontological representation of the detailed variations in phenotype, whether between mutant or wildtype, among individual humans, or across the diversity of species, requires a process by which a precise combination of terms from domain ontologies are selected and organized according to logical relations. The efficiencies that we have developed in this process will be useful for any attempt to annotate complex phenotypic descriptions using ontologies. We also discuss some ramifications of EQ representation for the domain of systematics.

A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data.

Interest in structural brain connectivity has grown with the understanding that abnormal neural connections may play a role in neurologic and psychiatric diseases. Small animal connectivity mapping techniques are particularly important for identifying aberrant connectivity in disease models. Diffusion magnetic resonance imaging tractography can provide nondestructive, 3D, brain-wide connectivity maps, but has historically been limited by low spatial resolution, low signal-to-noise ratio, and the difficulty in estimating multiple fiber orientations within a single image voxel. Small animal diffusion tractography can be substantially improved through the combination of ex vivo MRI with exogenous contrast agents, advanced diffusion acquisition and reconstruction techniques, and probabilistic fiber tracking. Here, we present a comprehensive, probabilistic tractography connectome of the mouse brain at microscopic resolution, and a comparison of these data with a neuronal tracer-based connectivity data from the Allen Brain Atlas. This work serves as a reference database for future tractography studies in the mouse brain, and demonstrates the fundamental differences between tractography and neuronal tracer data.

Use of satellite data for modelling food availability and survival of marine fish larvae

A modelling scheme is described which uses satellite retrieved sea-surface temperature and chlorophyll-a to derive monthly zooplankton biomass estimates in the eastern North Atlantic; this forms part of a bio-physical model of inter-annual variations in the growth and survival of larvae and post-larvae of mackerel (Scomber scombrus). The temperature and chlorophyll data are incorporated first to model copepod (Calanus) egg production rates. Egg production is then converted to available food using distribution data from the Continuous Plankton Recorder (CPR) Survey, observed population biomass per unit daily egg production and the proportion of the larval mackerel diet comprising Calanus. Results are validated in comparison with field observations of zooplankton biomass. The principal benefit of the modelling scheme is the ability to use the combination of broad scale coverage and fine scale temporal and spatial variability of satellite data as driving forces in the model; weaknesses are the simplicity of the egg production model and the broad-scale generalizations assumed in the raising factors to convert egg production to biomass.