96 research outputs found
Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.
BackgroundUsing knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms?ResultsOur existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.ConclusionsWith some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels
Direct Amortized Likelihood Ratio Estimation
We introduce a new amortized likelihood ratio estimator for likelihood-free
simulation-based inference (SBI). Our estimator is simple to train and
estimates the likelihood ratio using a single forward pass of the neural
estimator. Our approach directly computes the likelihood ratio between two
competing parameter sets which is different from the previous approach of
comparing two neural network output values. We refer to our model as the direct
neural ratio estimator (DNRE). As part of introducing the DNRE, we derive a
corresponding Monte Carlo estimate of the posterior. We benchmark our new ratio
estimator and compare to previous ratio estimators in the literature. We show
that our new ratio estimator often outperforms these previous approaches. As a
further contribution, we introduce a new derivative estimator for likelihood
ratio estimators that enables us to compare likelihood-free Hamiltonian Monte
Carlo (HMC) with random-walk Metropolis-Hastings (MH). We show that HMC is
equally competitive, which has not been previously shown. Finally, we include a
novel real-world application of SBI by using our neural ratio estimator to
design a quadcopter. Code is available at https://github.com/SRI-CSL/dnre.Comment: 12 Pages, 10 Figures, GitHub: https://github.com/SRI-CSL/dnr
Global measurement of coagulation in plasma from normal and haemophilia dogs using a novel modified thrombin generation test – Demonstrated in vitro and ex vivo
Canine models of severe haemophilia resemble their human equivalents both regarding clinical bleeding phenotype and response to treatment. Therefore pre-clinical studies in haemophilia dogs have allowed researchers to make valuable translational predictions regarding the potency and efficacy of new anti-haemophilia drugs (AHDs) in humans. To refine in vivo experiments and reduce number of animals, such translational studies are ideally preceded by in vitro prediction of compound efficacy using a plasma based global coagulation method. One such widely used method is the thrombin generation test (TGT). Unfortunately, commercially available TGTs are incapable of distinguishing between normal and haemophilia canine plasma, and therefore in vitro prediction using TGT has so far not been possible in canine plasma material
A Structural Model for Octagonal Quasicrystals Derived from Octagonal Symmetry Elements Arising in -Mn Crystallization of a Simple Monatomic Liquid
While performing molecular dynamics simulations of a simple monatomic liquid,
we observed the crystallization of a material displaying octagonal symmetry in
its simulated diffraction pattern. Inspection of the atomic arrangements in the
crystallization product reveals large grains of the beta-Mn structure aligned
along a common 4-fold axis, with 45 degree rotations between neighboring
grains. These 45 degree rotations can be traced to the intercession of a second
crystalline structure fused epitaxially to the beta-Mn domain surfaces, whose
primitive cell has lattice parameters a = b = c = a_{beta-Mn}, alpha = beta =
90 degrees, and gamma = 45 degrees. This secondary phase adopts a structure
which appears to have no known counterpart in the experimental literature, but
can be simply derived from the Cr_3Si and Al_3Zr_4 structure types. We used
these observations as the basis for an atomistic structural model for octagonal
quasicrystals, in which the beta-Mn and the secondary phase structure unit
cells serve as square and rhombic tiles (in projection), respectively. Its
diffraction pattern down the octagonal axis resembles those experimentally
measured. The model is unique in being consistent with high-resolution electron
microscopy images showing square and rhombic units with edge-lengths equal to
that of the beta-Mn unit cell. Energy minimization of this configuration, using
the same pair potential as above, results in an alternative octagonal
quasiperiodic structure with the same tiling but a different atomic decoration
and diffraction pattern.Comment: 25 pages, 10 figure
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
We present AircraftVerse, a publicly available aerial vehicle design dataset.
Aircraft design encompasses different physics domains and, hence, multiple
modalities of representation. The evaluation of these cyber-physical system
(CPS) designs requires the use of scientific analytical and simulation models
ranging from computer-aided design tools for structural and manufacturing
analysis, computational fluid dynamics tools for drag and lift computation,
battery models for energy estimation, and simulation models for flight control
and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the
largest corpus of engineering designs with this level of complexity. Each
design comprises the following artifacts: a symbolic design tree describing
topology, propulsion subsystem, battery subsystem, and other design details; a
STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a
stereolithography (STL) file format; a 3D point cloud for the shape of the
design; and evaluation results from high fidelity state-of-the-art physics
models that characterize performance metrics such as maximum flight distance
and hover-time. We also present baseline surrogate models that use different
modalities of design representation to predict design performance metrics,
which we provide as part of our dataset release. Finally, we discuss the
potential impact of this dataset on the use of learning in aircraft design and,
more generally, in CPS. AircraftVerse is accompanied by a data card, and it is
released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The
dataset is hosted at https://zenodo.org/record/6525446, baseline models and
code at https://github.com/SRI-CSL/AircraftVerse, and the dataset description
at https://aircraftverse.onrender.com/.Comment: The dataset is hosted at https://zenodo.org/record/6525446, baseline
models and code at https://github.com/SRI-CSL/AircraftVerse, and the dataset
description at https://aircraftverse.onrender.com
Proteolytic Processing of ErbB4 in Breast Cancer
Peer reviewe
Regulation of mammary gland branching morphogenesis by the extracellular matrix and its remodeling enzymes.
A considerable body of research indicates that mammary gland branching morphogenesis is dependent, in part, on the extracellular matrix (ECM), ECM-receptors, such as integrins and other ECM receptors, and ECM-degrading enzymes, including matrix metalloproteinases (MMPs) and their inhibitors, tissue inhibitors of metalloproteinases (TIMPs). There is some evidence that these ECM cues affect one or more of the following processes: cell survival, polarity, proliferation, differentiation, adhesion, and migration. Both three-dimensional culture models and genetic manipulations of the mouse mammary gland have been used to study the signaling pathways that affect these processes. However, the precise mechanisms of ECM-directed mammary morphogenesis are not well understood. Mammary morphogenesis involves epithelial 'invasion' of adipose tissue, a process akin to invasion by breast cancer cells, although the former is a highly regulated developmental process. How these morphogenic pathways are integrated in the normal gland and how they become dysregulated and subverted in the progression of breast cancer also remain largely unanswered questions
New insights into the genetic etiology of Alzheimer's disease and related dementias
Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele
Accounting for Individual Speaker Properties in Automatic Speech Recognition
In this work, speaker characteristic modeling has been applied in the fields of automatic speech recognition (ASR) and automatic speaker verification (ASV). In ASR, a key problem is that acoustic mismatch between training and test conditions degrade classification per- formance. In this work, a child exemplifies a speaker not represented in training data and methods to reduce the spectral mismatch are devised and evaluated. To reduce the acoustic mismatch, predictive modeling based on spectral speech transformation is applied. Follow- ing this approach, a model suitable for a target speaker, not well represented in the training data, is estimated and synthesized by applying vocal tract predictive modeling (VTPM). In this thesis, the traditional static modeling on the utterance level is extended to dynamic modeling. This is accomplished by operating also on sub-utterance units, such as phonemes, phone-realizations, sub-phone realizations and sound frames. Initial experiments shows that adaptation of an acoustic model trained on adult speech significantly reduced the word error rate of ASR for children, but not to the level of a model trained on children’s speech. Multi-speaker-group training provided an acoustic model that performed recognition for both adults and children within the same model at almost the same accuracy as speaker-group dedicated models, with no added model complexity. In the analysis of the cause of errors, body height of the child was shown to be correlated to word error rate. A further result is that the computationally demanding iterative recognition process in standard VTLN can be replaced by synthetically extending the vocal tract length distribution in the training data. A multi-warp model is trained on the extended data and recognition is performed in a single pass. The accuracy is similar to that of the standard technique. A concluding experiment in ASR shows that the word error rate can be reduced by ex- tending a static vocal tract length compensation parameter into a temporal parameter track. A key component to reach this improvement was provided by a novel joint two-level opti- mization process. In the process, the track was determined as a composition of a static and a dynamic component, which were simultaneously optimized on the utterance and sub- utterance level respectively. This had the principal advantage of limiting the modulation am- plitude of the track to what is realistic for an individual speaker. The recognition error rate was reduced by 10% relative compared with that of a standard utterance-specific estimation technique. The techniques devised and evaluated can also be applied to other speaker characteristic properties, which exhibit a dynamic nature. An excursion into ASV led to the proposal of a statistical speaker population model. The model represents an alternative approach for determining the reject/accept threshold in an ASV system instead of the commonly used direct estimation on a set of client and impos- tor utterances. This is especially valuable in applications where a low false reject or false ac- cept rate is required. In these cases, the number of errors is often too few to estimate a reli- able threshold using the direct method. The results are encouraging but need to be verified on a larger database.QC 20110502Pf-StarKOBR
Accounting for Individual Speaker Properties in Automatic Speech Recognition
In this work, speaker characteristic modeling has been applied in the fields of automatic speech recognition (ASR) and automatic speaker verification (ASV). In ASR, a key problem is that acoustic mismatch between training and test conditions degrade classification per- formance. In this work, a child exemplifies a speaker not represented in training data and methods to reduce the spectral mismatch are devised and evaluated. To reduce the acoustic mismatch, predictive modeling based on spectral speech transformation is applied. Follow- ing this approach, a model suitable for a target speaker, not well represented in the training data, is estimated and synthesized by applying vocal tract predictive modeling (VTPM). In this thesis, the traditional static modeling on the utterance level is extended to dynamic modeling. This is accomplished by operating also on sub-utterance units, such as phonemes, phone-realizations, sub-phone realizations and sound frames. Initial experiments shows that adaptation of an acoustic model trained on adult speech significantly reduced the word error rate of ASR for children, but not to the level of a model trained on children’s speech. Multi-speaker-group training provided an acoustic model that performed recognition for both adults and children within the same model at almost the same accuracy as speaker-group dedicated models, with no added model complexity. In the analysis of the cause of errors, body height of the child was shown to be correlated to word error rate. A further result is that the computationally demanding iterative recognition process in standard VTLN can be replaced by synthetically extending the vocal tract length distribution in the training data. A multi-warp model is trained on the extended data and recognition is performed in a single pass. The accuracy is similar to that of the standard technique. A concluding experiment in ASR shows that the word error rate can be reduced by ex- tending a static vocal tract length compensation parameter into a temporal parameter track. A key component to reach this improvement was provided by a novel joint two-level opti- mization process. In the process, the track was determined as a composition of a static and a dynamic component, which were simultaneously optimized on the utterance and sub- utterance level respectively. This had the principal advantage of limiting the modulation am- plitude of the track to what is realistic for an individual speaker. The recognition error rate was reduced by 10% relative compared with that of a standard utterance-specific estimation technique. The techniques devised and evaluated can also be applied to other speaker characteristic properties, which exhibit a dynamic nature. An excursion into ASV led to the proposal of a statistical speaker population model. The model represents an alternative approach for determining the reject/accept threshold in an ASV system instead of the commonly used direct estimation on a set of client and impos- tor utterances. This is especially valuable in applications where a low false reject or false ac- cept rate is required. In these cases, the number of errors is often too few to estimate a reli- able threshold using the direct method. The results are encouraging but need to be verified on a larger database.QC 20110502Pf-StarKOBR
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