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At least 991 records · Page 55Linked to original sources
PubMed2003

B0-B(-)0 mixing beyond factorization in QCD sum rules.

We present a calculation of the B0-B(-)0 mixing matrix element in the framework of QCD sum rules for three-point functions. We compute alpha(s) corrections to a three-point function at the three-loop level in QCD perturbation theory, which allows one to extract the matrix element with next-to-leading order (NLO) accuracy. This calculation is imperative for a consistent evaluation of experimentally measured mixing parameters since the coefficient functions of the effective Hamiltonian for B0-B(-)0 mixing are known at NLO. We find that radiative corrections violate factorization at NLO; this violation is under full control and amounts to 10%. The resulting value of the B parameter is found to be B(B)(m(b))=1+0.1(PT)-0.05(non-PT).

Journal Article↗
PubMed2003

Robust estimation of ultrasound pulses using outlier-resistant de-noising.

A different approach to the problem of estimation of the ultrasound pulse spectrum, which usually arises as a part of ultrasound image restoration algorithms, is presented. It is shown that this estimation problem can be reformulated in terms of a de-noising problem. In this formulation, the log-spectrum of a radio-frequency line (RF-line) is viewed as a noisy measurement of the signal that needs to be estimated, i.e., the ultrasound pulse log-spectrum. The log-spectrum of the tissue reflectivity function (i.e., tissue response) is considered as the noise to be rejected. The contribution of the paper is twofold. First, it provides statistical description of the reflectivity function log-spectrum for the case, when the samples of the reflectivity function are independent identically distributed (i.i.d.) Gaussian random variables. Moreover, it is shown that the problem of the pulse spectrum recovery is essentially a de-noising problem. Consequently, it is suggested to solve the problem within the framework of the de-noising by wavelet shrinkage. Second, a computationally efficient algorithm is proposed for the pulse-spectrum estimation, which can be viewed as a modified version of the classical Donoho's three-step de-noising procedure. This modification is necessary, because of specific properties of the noise to be rejected. It is shown, that whenever the samples of the reflectivity function can be assumed to be i.i.d. Gaussian random variables, the samples of its log-spectrum obey the Fisher-Tippet distribution. For this type of noise, straightforward implementation of the standard de-noising can cause serious estimation errors. In order to overcome this difficulty, an outlier-resistant de-noising is performed. The unique properties of this modified de-noising algorithm allow estimating the pulse spectrum adaptively to its properties, as they are continuously influenced by the frequency-dependent attenuation process. The performance of the proposed algorithm is examined in a series of computer-simulations. It is shown that this algorithm, developed on the assumption of the "Gaussian" reflectivity function, remains applicable for broader classes of distributions. The results obtained in a series of in vivo experiments reveal superior performance of the novel approach over some of alternative estimation techniques, e.g., cepstrum-based estimation.

Adult↗
PubMed2005

Adaptively merging large-scale range data with reflectance properties.

In this paper, we tackle the problem of geometric and photometric modeling of large intricately shaped objects. Typical target objects we consider are cultural heritage objects. When constructing models of such objects, we are faced with several important issues that have not been addressed in the past-issues that mainly arise due to the large amount of data that has to be handled. We propose two novel approaches to efficiently handle such large amounts of data: A highly adaptive algorithm for merging range images and an adaptive nearest-neighbor search to be used with the algorithm. We construct an integrated mesh model of the target object in adaptive resolution, taking into account the geometric and/or photometric attributes associated with the range images. We use surface curvature for the geometric attributes and (laser) reflectance values for the photometric attributes. This adaptive merging framework leads to a significant reduction in the necessary amount of computational resources. Furthermore, the resulting adaptive mesh models can be of great use for applications such as texture mapping, as we will briefly demonstrate. Additionally, we propose an additional test for the k-d tree nearest-neighbor search algorithm. Our approach successfully omits back-tracking, which is controlled adaptively depending on the distance to the nearest neighbor. Since the main consumption of computational cost lies in the nearest-neighbor search, the proposed algorithm leads to a significant speed-up of the whole merging process. In this paper, we present the theories and algorithms of our approaches with pseudo code and apply them to several real objects, including large-scale cultural assets.

Algorithms↗
PubMed1998

Frequency selectivity, multistability, and oscillations emerge from models of genetic regulatory systems.

To examine the capability of genetic regulatory systems for complex dynamic activity, we developed simple kinetic models that incorporate known features of these systems. These include autoregulation and stimulus-dependent phosphorylation of transcription factors (TFs), dimerization of TFs, crosstalk, and feedback. The simplest model manifested multiple stable steady states, and brief perturbations could switch the model between these states. Such transitions might explain, for example, how a brief pulse of hormone or neurotransmitter could elicit a long-lasting cellular response. In slightly more complex models, oscillatory regimes were identified. The addition of competition between activating and repressing TFs provided a plausible explanation for optimal stimulus frequencies that give maximal transcription. Such optimal frequencies are suggested by recent experiments comparing training paradigms for long-term memory formation and examining changes in mRNA levels in repetitively stimulated cultured cells. In general, the computational approach illustrated here, combined with appropriate experiments, provides a conceptual framework for investigating the function of genetic regulatory systems.

Animals↗
PubMed2003

Pitfalls in the diagnosis and management of laryngeal chondrosarcoma.

Chondrosarcomas of the larynx (CS) are uncommon and predominantly affect the cricoid cartilage. In the larynx they have a distinctive biological behaviour and require individual treatment. A retrospective study was made on three cases of CS, all presenting with compromise of the upper respiratory tract. The medical history varied from several weeks to six years. Correct diagnosis required open resection of the lesion in all cases. Due to intra-operative findings, all patients underwent total laryngectomy. CS grow slowly and are therefore frequently diagnosed late in the course of the disease. A subglottic bulging of the mucosa should indicate high-resolution-computed-tomography of the larynx to exclude affection of the laryngeal framework. Correct diagnosis of laryngeal cartilaginous tumours requires a complete examination of the entirely resected tumour. Only extended and de-differentiated CS indicate further radical surgery, the remainder call for conservative surgical management.

Aged↗
PubMed2002

Discovery Partners International.

Discovery Partners International (Nasdaq: DPII) is a leader in collaborative drug discovery. DPI's integrated discovery framework encompasses a broad spectrum of capabilities in chemistry, biology, informatics, computational modeling and synthesis automation. DPI's approach to drug discovery places a heavy emphasis on up-front interdisciplinary analysis of its client's problems. By drawing on the collective expertise of its chemists, biologists and computational scientists, lead discovery and optimization strategies with the highest likelihood of success are formulated and then executed using the company's extensive capabilities and high output technology platforms.

Computational Biology↗
PubMed1998

An evaluation of UMLS as a controlled terminology for the Problem List Toolkit.

We are developing a set of software components--the Problem List Toolkit (PL-Tk)--to support operations on clinical problem labels. An adaptation of the National Library of Medicine's Unified Medical Language System (UMLS) provides general vocabulary services to domain-specific software components. Our initial investigation centers on the inclusion in UMLS of problem labels used in the Beth Israel Deaconess Medical Center's Online Medical Record (OMR). We also explore the semantic typing of problem labels matched in UMLS. We have operationally defined a clinical problem to derive its semantic type from classes of terms representing findings or processes typically requiring diagnostic evaluation or therapeutic management in clinical practice. Of 1262 unique OMR problem labels, 999 terms (79%) have matches in UMLS. 986 of 999 terms (99%) map to the UMLS concept of the corresponding lexical match. 952 of 999 terms (95%) have semantic types that comply with our operational definition of clinical problems. These 952 terms (75%) constitute Version 1.0 of the problem list vocabulary B196. Matching terms with inappropriate semantic types raise issues regarding requirements for PL-Tk, typing of existing UMLS terms, and the adequacy of our operational definition for clinical problems. UMLS provides a large repertoire of pre-coordinated terms that are used as problem labels in a heavily used computer-based patient record system. The semantic type hierarchy provides a framework for the consistent use of clinical concepts in problem lists such that clinical problem labels represent "good" clinical problems.

Evaluation Studies as Topic↗
PubMed1999

Innovative multidisciplinary research in environmental epidemiology: the challenges and needs.

The ability of epidemiology to determine the relationships between health and environmental insults has become exceedingly difficult. The multifactorial nature of disease and the diversity of the insults, which include biologic, physical, social and cultural factors, combined with genetic susceptibility, suggest the need to develop better models of multidisciplinary epidemiologic investigation. This paper highlights the needs of an environmental epidemiologic team, discusses ways to incorporate new ideas across disciplines and to integrate constructs and paradigms of social, ecological, cultural and population determinants with individual-based exposure assessments. Innovation will be the key to the survival and increasing importance of epidemiology in addressing the public health needs of the future, but what are the ways to enhance and encourage creativity in environmental epidemiology? The process of self-renewal and continuing education will be highlighted. Additionally, the complexity of the problems and the need for clear supervision and control of multidisciplinary research efforts require a forum of communication and an 'information-processing approach' beyond those in traditional epidemiologic studies. New approaches in data management and medical informatics must be incorporated into the epidemiologic investigative framework. Methods to be included should focus on opportunities for computer-supported sharing of ideas. Such capabilities minimise the geographic distances and the disparate knowledge and training of the investigators and bring the team closer to the objectives and functions inherent in multidisciplinary investigation.

Databases as Topic↗
PubMed2006

Dispersion and repulsion contributions to the solvation free energy: comparison of quantum mechanical and classical approaches in the polarizable continuum model.

We report a systematic comparison of the dispersion and repulsion contributions to the free energy of solvation determined using quantum mechanical self-consistent reaction field (QM-SCRF) and classical methods. In particular, QM-SCRF computations have been performed using the dispersion and repulsion expressions developed in the framework of the integral equation formalism of the polarizable continuum model, whereas classical methods involve both empirical pairwise potential and surface-dependent approaches. Calculations have been performed for a series of aliphatic and aromatic compounds containing prototypical functional groups in four solvents: water, octanol, chloroform, and carbon tetrachloride. The analysis is focused on the dependence of the dispersion and repulsion components on the level of theory used in QM-SCRF computations, the contribution of those terms in different solvents, and the magnitude of the coupling between electrostatic and dispersion-repulsion components. Finally, comparison is made between the dispersion-repulsion contributions obtained from QM-SCRF calculations and the results determined from classical approaches.

Acetaldehyde↗
PubMed2006

Tikhonov training of the CMAC neural network.

The architecture of the cerebellar model articulation controller (CMAC) presents a rigid compromise between learning and generalization. In the presence of a sparse training dataset, this limitation manifestly causes overfitting, a drawback that is not overcome by current training algorithms. This paper proposes a novel training framework founded on the Tikhonov regularization, which relates to the minimization of the power of the sigma-order derivative. This smoothness criterion yields to an internal cell-interaction mechanism that increases the generalization beyond the degree hardcoded in the CMAC architecture while preserving the potential CMAC learning capabilities. The resulting training mechanism, which proves to be simple and computationally efficient, is deduced from a rigorous theoretical study. The performance of the new training framework is validated against comparative benchmarks from the DELVE environment.

Algorithms↗
PubMed2004

A similarity learning approach to content-based image retrieval: application to digital mammography.

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

Algorithms↗
PubMed2005

Building the TeraGrid.

On 1 October 2004, the most ambitious high-performance Grid project in the United States-the TeraGrid-became fully operational. Resources at nine sites--the San Diego Supercomputer Center, the California Institute of Technology, the National Center for Supercomputing Applications, the University of Chicago/Argonne National Laboratory, Pittsburgh Supercomputing Center, Texas Advanced Computing Center, Purdue University, Indiana University and Oak Ridge National Laboratory-were joined via an ultra-fast optical network, unified policies and security procedures and a sophisticated distributed computing software environment. Funded by the National Science Foundation, the TeraGrid enables scientists and engineers to combine distributed, multiple data sources with computation at any of the sites or link massively parallel computer simulations to extreme-resolution visualizations at remote sites. A single shared utility lets multiple resources be easily leveraged and provides improved access to advanced computational capabilities. One of the demonstrations of this new model for using distributed resources, Teragyroid, linked the infrastructure of the TeraGrid with computing resources in the United Kingdom via a transatlantic data fibre link. Once connected, the software framework of the RealityGrid project was used to successfully explore lattice-Boltzmann simulations involving lattices of over one billion sites.

Computer Security↗
PubMed2006

Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions.

Metabolic flux analysis (MFA) has emerged as a tool of great significance for metabolic engineering and mammalian physiology. An important limitation of MFA, as carried out via stable isotope labeling and GC/MS and nuclear magnetic resonance (NMR) measurements, is the large number of isotopomer or cumomer equations that need to be solved, especially when multiple isotopic tracers are used for the labeling of the system. This restriction reduces the ability of MFA to fully utilize the power of multiple isotopic tracers in elucidating the physiology of realistic situations comprising complex bioreaction networks. Here, we present a novel framework for the modeling of isotopic labeling systems that significantly reduces the number of system variables without any loss of information. The elementary metabolite unit (EMU) framework is based on a highly efficient decomposition method that identifies the minimum amount of information needed to simulate isotopic labeling within a reaction network using the knowledge of atomic transitions occurring in the network reactions. The functional units generated by the decomposition algorithm, called EMUs, form the new basis for generating system equations that describe the relationship between fluxes and stable isotope measurements. Isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer methods, however, require significantly less computation time. For a typical (13)C-labeling system the total number of equations that needs to be solved is reduced by one order-of-magnitude (100s EMUs vs. 1000s isotopomers). As such, the EMU framework is most efficient for the analysis of labeling by multiple isotopic tracers. For example, analysis of the gluconeogenesis pathway with (2)H, (13)C, and (18)O tracers requires only 354 EMUs, compared to more than two million isotopomers.

Carbon↗
PubMed2006

Flooding in GROMACS: accelerated barrier crossings in molecular dynamics.

The major bottleneck of today's atomistic molecular dynamics (MD) simulations is that because of the enormous computational effort involved, only processes at nanoseconds to microseconds time scales or faster can be studied directly. Unfortunately, apart from a few exceptions, relevant processes, such as chemical reactions or many large scale conformational transitions in proteins, occur at slower time scales and therefore are currently far out of reach for conventional MD. The flooding technique addresses this problem by inclusion of a flooding potential into the force field. This flooding potential locally destabilizes the educt state and thereby significantly accelerates the escape from the initial energy well without affecting the reaction pathway. Here, we summarize the theory and method for the computational chemistry community and detail the implementation within the official version 3.3 of the freely available MD program package GROMACS. Two examples shall demonstrate the application of flooding to accelerate conformational transitions and chemical reactions. The second example was carried out within a QM/MM framework.

Butanes↗
PubMed2006

A unifying framework for seed sensitivity and its application to subset seeds.

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem--a set of target alignments, an associated probability distribution, and a seed model--that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds.

Algorithms↗
PubMed2026

Mutational signatures in blood-brain barrier: mechanisms, computational insights, and clinical applications in precision oncology.

The blood - brain barrier (BBB) plays a central role in maintaining central nervous system (CNS) homeostasis, and its disruption is a defining feature of malignant brain tumors such as glioblastoma. Emerging evidence indicates that BBB dysfunction not only alters the tumor microenvironment but also shapes the mutational processes that drive genomic instability in CNS malignancies. This review synthesizes current understanding of the biological mechanisms linking BBB breakdown with distinct mutational signatures, including those arising from oxidative stress, hypoxia-induced replication stress, lipid peroxidation, inflammation, and metabolic reprogramming. Advances in next-generation sequencing, coupled with computational tools such as non-negative matrix factorization, Bayesian modeling, and deep learning, have enabled precise extraction of these signatures and their integration with multi-omics data. Clinically, BBB-associated mutational signatures offer significant promise for therapeutic stratification, prediction of treatment response, and noninvasive monitoring through cerebrospinal fluid - derived circulating tumor DNA. Despite these advances, challenges persist due to limited tissue accessibility, low-yield CSF samples, incomplete mechanistic models, and the lack of CNS-specific analytical frameworks. A deeper understanding of BBB-driven mutational processes, supported by improved computational approaches and integrative datasets, holds potential to advance precision oncology in neuro-oncology.

Humans↗
PubMed1995

Three-dimensional reconstruction of the feline larynx with serial histologic sections.

This paper reviews a new technique to develop high-resolution three-dimensional (3-D) images of the larynx using histological sections. Three-dimensional computer-reconstructed histological sections of the cat are used in this study to evaluate the recurrent laryngeal nerve (RLN) in its true anatomic course, with emphasis on its relationship to surrounding structures (laryngeal framework). A cat model was used because of specimen availability and technical ease of tissue preparation. Computer-reconstructed serial histologic sections add a new dimension to the study of laryngeal anatomy, laryngeal trauma, and growth patterns of laryngeal cancer. The technical aspects of three-dimensional reconstruction and future applications are discussed.

Animals↗
PubMed2001

Angular weighted hybrid cone-beam CT reconstruction for circular trajectories.

Hybrid reconstruction techniques have been introduced for the volume reconstruction of axially truncated cone-beam computed tomography projection data acquired along a circular source-detector trajectory. The introduction of weighted half-scan techniques into this framework is described in this paper. Due to the cone-beam geometry it is not possible to perform the weighting on the projections as is typically done in conventional single-line computed tomography. Hence, in this paper we present an efficient way to incorporate angular weighting functions, depending on the object point position, into the framework of hybrid cone-beam reconstruction. Four different angular weighting functions are introduced and discussed with respect to their cone-beam artefact behaviour and their influence on the signal-to-noise ratio. As a result, the most effective angular weighting function for hybrid circular cone-beam reconstruction is determined by means of a simulation study based on mathematical phantoms and clinical data sets. This distance-weighted angular weighting scheme yields the best results in terms of high image quality, low computational complexity and signal-to-noise variations in the reconstruction volume.

Computer Simulation↗