3,895 research outputs found
Temperature dependence of circular DNA topological states
Circular double stranded DNA has different topological states which are
defined by their linking numbers. Equilibrium distribution of linking numbers
can be obtained by closing a linear DNA into a circle by ligase. Using Monte
Carlo simulation, we predict the temperature dependence of the linking number
distribution of small circular DNAs. Our predictions are based on flexible
defect excitations resulted from local melting or unstacking of DNA base pairs.
We found that the reduced bending rigidity alone can lead to measurable changes
of the variance of linking number distribution of short circular DNAs. If the
defect is accompanied by local unwinding, the effect becomes much more
prominent. The predictions can be easily investigated in experiments, providing
a new method to study the micromechanics of sharply bent DNAs and the thermal
stability of specific DNA sequences. Furthermore, the predictions are directly
applicable to the studies of binding of DNA distorting proteins that can
locally reduce DNA rigidity, form DNA kinks, or introduce local unwinding.Comment: 15 pages in preprint format, 4 figure
Sequence Dependent Structural Transition of Short DNA by Tensile Force
Del 25 de novembre de 2013 al 3 de febrer de 201
Prediction of superconducting properties of materials based on machine learning models
The application of superconducting materials is becoming more and more
widespread. Traditionally, the discovery of new superconducting materials
relies on the experience of experts and a large number of "trial and error"
experiments, which not only increases the cost of experiments but also prolongs
the period of discovering new superconducting materials. In recent years,
machine learning has been increasingly applied to materials science. Based on
this, this manuscript proposes the use of XGBoost model to identify
superconductors; the first application of deep forest model to predict the
critical temperature of superconductors; the first application of deep forest
to predict the band gap of materials; and application of a new sub-network
model to predict the Fermi energy level of materials. Compared with our known
similar literature, all the above algorithms reach state-of-the-art. Finally,
this manuscript uses the above models to search the COD public dataset and
identify 50 candidate superconducting materials with possible critical
temperature greater than 90 K
Drug repositioning for Alzheimer's disease with transfer learning
Deep Learning and DRUG-seq (Digital RNA with perturbation of genes) have
attracted attention in drug discovery. However, the public DRUG-seq dataset is
too small to be used for directly training a deep learning neural network from
scratch. Inspired by the transfer learning technique, we pretrain a drug
efficacy prediction neural network model with the Library of Integrated
Network-based Cell-Signature (LINCS) L1000 data and then use human neural cell
DRUG-seq data to fine-tune it. After training, the model is used for virtual
screening to find potential drugs for Alzheimer's disease (AD) treatment.
Finally, we find 27 potential drugs for AD treatment including Irsogladine
(PDE4 inhibitor), Tasquinimod (HDAC4 selective inhibitor), Suprofen (dual
COX-1/COX-2 inhibitor) et al.Comment: 13 pages, 1 figur
Effects of kink and flexible hinge defects on mechanical responses of short double stranded DNA molecules
We predict various detectable mechanical responses to the presence of local
DNA defects which are defined as short DNA segments exhibiting mechanical
properties obviously different from the 50 nm persistence length based
semiflexible polymer model. The defects discussed are kinks and flexible hinges
either permanently fixed on DNA or thermally excited. Their effects on
extension shift, the effective persistence length, the end-to-end distance
distribution, and the cyclization probability are computed using a
transfer-matrix method. Our predictions will be useful in future experimental
designs to study DNA nicks or mismatch base pairs, mechanics of specific DNA
sequences, and specific DNA-protein interaction using magnetic tweezer,
fluorescence resonance energy transfer or plasmon resonance technique, and the
traditional biochemistry cyclization probability measurements.Comment: 9 pages with 9 figures. Theoretical calculation based on transfer
matrix. Minor updates, a new figure and more discussions are adde
A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution
Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process
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