171 research outputs found
Hidden Markov model speed heuristic and iterative HMM search procedure
BACKGROUND: Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. RESULTS: We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. CONCLUSIONS: Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST
Eigenvectors of the discrete Laplacian on regular graphs - a statistical approach
In an attempt to characterize the structure of eigenvectors of random regular
graphs, we investigate the correlations between the components of the
eigenvectors associated to different vertices. In addition, we provide
numerical observations, suggesting that the eigenvectors follow a Gaussian
distribution. Following this assumption, we reconstruct some properties of the
nodal structure which were observed in numerical simulations, but were not
explained so far. We also show that some statistical properties of the nodal
pattern cannot be described in terms of a percolation model, as opposed to the
suggested correspondence for eigenvectors of 2 dimensional manifolds.Comment: 28 pages, 11 figure
Geometric characterization of nodal domains: the area-to-perimeter ratio
In an attempt to characterize the distribution of forms and shapes of nodal
domains in wave functions, we define a geometric parameter - the ratio
between the area of a domain and its perimeter, measured in units of the
wavelength . We show that the distribution function can
distinguish between domains in which the classical dynamics is regular or
chaotic. For separable surfaces, we compute the limiting distribution, and show
that it is supported by an interval, which is independent of the properties of
the surface. In systems which are chaotic, or in random-waves, the
area-to-perimeter distribution has substantially different features which we
study numerically. We compare the features of the distribution for chaotic wave
functions with the predictions of the percolation model to find agreement, but
only for nodal domains which are big with respect to the wavelength scale. This
work is also closely related to, and provides a new point of view on
isoperimetric inequalities.Comment: 22 pages, 11 figure
Personal health promotion at US medical schools: a quantitative study and qualitative description of deans' and students' perceptions
BACKGROUND: Prior literature has shown that physicians with healthy personal habits are more likely to encourage patients to adopt similar habits. However, despite the possibility that promoting medical student health might therefore efficiently improve patient outcomes, no one has studied whether such promotion happens in medical school. We therefore wished to describe both typical and outstanding personal health promotion environments experienced by students in U.S. medical schools. METHODS: We collected information through four different modalities: a literature review, written surveys of medical school deans and students, student and dean focus groups, and site visits at and interviews with medical schools with reportedly outstanding student health promotion programs. RESULTS: We found strong correlations between deans' and students' perceptions of their schools' health promotion environments, including consistent support of the idea of schools' encouraging healthy student behaviors, with less consistent follow-through by schools on this concept. Though students seemed to have thought little about the relationships between their own personal and clinical health promotion practices, deans felt strongly that faculty members should model healthy behaviors. CONCLUSIONS: Deans' support of the relationship between physicians' personal and clinical health practices, and concern about their institutions' acting on this relationship augurs well for the role of student health promotion in the future of medical education. Deans seem to understand their students' health environment, and believe it could and should be improved; if this is acted on, it could create important positive changes in medical education and in disease prevention
Isospectral discrete and quantum graphs with the same flip counts and nodal counts
The existence of non-isomorphic graphs which share the same Laplace spectrum
(to be referred to as isospectral graphs) leads naturally to the following
question: What additional information is required in order to resolve
isospectral graphs? It was suggested by Band, Shapira and Smilansky that this
might be achieved by either counting the number of nodal domains or the number
of times the eigenfunctions change sign (the so-called flip count). Recently
examples of (discrete) isospectral graphs with the same flip count and nodal
count have been constructed by K. Ammann by utilising Godsil-McKay switching.
Here we provide a simple alternative mechanism that produces systematic
examples of both discrete and quantum isospectral graphs with the same flip and
nodal counts.Comment: 16 pages, 4 figure
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Modeling Implantable Passive Mechanisms for Modifying the Transmission of Forces and Movements Between Muscle and Tendons
This paper explores the development of biomechanical models for evaluating a new class of passive mechanical implants for orthopedic surgery. The proposed implants take the form of passive engineered mechanisms, and will be used to improve the functional attachment of muscles to tendons and bone by modifying the transmission of forces and movement inside the body. Specifically, we present how two types of implantable mechanisms may be modeled in the open-source biomechanical software OpenSim. The first implant, which is proposed for hand tendon-transfer surgery, differentially distributes the forces and movement from one muscle across multiple tendons. The second implant, which is proposed for knee-replacement surgery, scales up the forces applied to the knee joint by the quadriceps muscle. This paper's key innovation is that such mechanisms have never been considered before in biomechanical simulation modeling and in surgery. When compared with joint function enabled by the current surgical practice of using sutures to make the attachment, biomechanical simulations show that the surgery with 1) the differential mechanism (tendon network) implant improves the fingers' ability to passively adapt to an object's shape significantly during grasping tasks (2.74× as measured by the extent of finger flexion) for the same muscle force, and 2) the force-scaling implant increases knee-joint torque by 84% for the same muscle force. The critical significance of this study is to provide a methodology for the design and inclusion of the implants into biomechanical models and validating the improvement in joint function they enable when compared with current surgical practice.Keywords: implant design, Biomechanical modeling, orthopedic surgeryKeywords: implant design, Biomechanical modeling, orthopedic surger
Data standards can boost metabolomics research, and if there is a will, there is a way.
Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption
Cushing's Syndrome and Hypothalamic-Pituitary-Adrenal Axis Hyperactivity in Chronic Central Serous Chorioretinopathy
Clinical epidemiolog
A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid detection of Bacillus spores and identification of Bacillus species
Background
The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS.
Results
We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers) to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features.
Conclusions
This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA). The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA
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