The image characteristics in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) depend on the partial Fourier fraction and contrast medium concentration. These characteristics were assessed and the modulation transfer function (MTF) was calculated by computer simulation. A digital phantom was created from signal intensity data acquired at different contrast medium concentrations on a breast model. The frequency images [created by fast Fourier transform (FFT)] were divided into 512 parts and rearranged to form a new image. The inverse FFT of this image yielded the MTF. From the reference data, three linear models (low, medium, and high) and three exponential models (slow, medium, and rapid) of the signal intensity were created. Smaller partial Fourier fractions, and higher gradients in the linear models, corresponded to faster MTF decline. The MTF more gradually decreased in the exponential models than in the linear models. The MTF, which reflects the image characteristics in DCE-MRI, was more degraded as the partial Fourier fraction decreased.
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http://dx.doi.org/10.1007/s13246-016-0474-6 | DOI Listing |
J Proteome Res
January 2025
University of Santo Amaro (UNISA), Rua Isabel Schmidt 349, São Paulo 04743-030, Brazil.
Background: Peri-implantitis is characterized as a pathological change in the tissues around dental implants. Fourier-transform infrared spectroscopy (FTIR) provides molecular information from optical phenomena observed by the vibration of molecules, which is used in biological studies to characterize changes and serves as a form of diagnosis.
Aims: this case-control study evaluated the peri-implant disease by using FTIR spectroscopy with attenuated total reflectance in the fingerprint region.
J Dairy Sci
January 2025
Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China. Electronic address:
Accurate identification of cows' likelihood of conception during the period from recent calving to the first artificial insemination (AI) will provide assistance in managing the fertility of dairy cows and contribute to the economic prosperity and sustainability of the farm. The purpose of this study was to use FTIR spectroscopy collected from recent calving to the first artificial insemination (AI) to predict the cow's likelihood of conception to first AI, first or second AI. This study specifically focused on the role of FTIR spectral and farm data collected at different time windows in improving the accuracy of model for predicting the cow's likelihood of conception to first AI, first or second AI.
View Article and Find Full Text PDFPolymers (Basel)
December 2024
N.N. Semenov Federal Research Center for Chemical Physics Russian Academy of Sciences, 119991 Moscow, Russia.
Glycerol-(9,10-trioxolane) trioleate (OTOA) is a promising material that combines good plasticizing properties for PLA with profound antimicrobial activity, which makes it suitable for application in state-of-the-art biomedical and packaging materials with added functionality. On the other hand, application of OTOA in PLA-based antibacterial materials is hindered by a lack of knowledge on kinetics of the OTOA release. In this work, the release of glycero-(9,10-trioxolane) trioleate (OTOA) from PLA films with 50% OTOA content was studied during incubation in normal saline solution, and for the first time, the kinetics of OTOA release from PLA film was evaluated.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Department of Applied Mathematics, University of Washington, Seattle, WA 98195-3925, USA.
Statistical counting is the holographic observable to a statistical dynamics with finite states under independent and identically distributed sampling. Entropy provides the infinitesimal probability for an observed empirical frequency ν^ with respect to a probability prior p, when ν^≠p as N→∞. Following Callen's postulate and through Legendre-Fenchel transform, without help from mechanics, we show that an internal energy u emerges; it provides a linear representation of real-valued observables with full or partial information.
View Article and Find Full Text PDFSci Rep
December 2024
Departamento de Física, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911, Leganés, Spain.
Considering a universal deep neural network organized as a series of nested qubit rotations, accomplished by adjustable data re-uploads we analyze its expressivity. This ability to approximate continuous functions in regression tasks is quantified making use of a partial Fourier decomposition of the generated output and systematically benchmarked with the aid of a teacher-student scheme. While the maximal expressive power increases with the depth of the network and the number of qubits, it is fundamentally bounded by the data encoding mechanism.
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