compositional information
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2021 ◽  
Author(s):  
Ben Ellis ◽  
et al.

Supplemental Figures S1–S8 (additional compositional information relevant to this study), and a supplemental dataset (all new data for this study and reference materials).<br>


2021 ◽  
Author(s):  
Ben Ellis ◽  
et al.

Supplemental Figures S1–S8 (additional compositional information relevant to this study), and a supplemental dataset (all new data for this study and reference materials).<br>


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ayari Takamura ◽  
Kaede Tsukamoto ◽  
Kenji Sakata ◽  
Jun Kikuchi

AbstractIntegrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of “measurement descriptors” with different compositional information. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. Herein, the stepwise model refinement via selection of adopted descriptors based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as α-helix keratins in cortex; and bounded water and thermal resistant components in cuticle. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair.


2021 ◽  
Author(s):  
Ayari Takamura ◽  
Kaede Tsukamoto ◽  
Kenji Sakata ◽  
Jun Kikuchi

Abstract Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of “measurement descriptors” with different compositional information. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. Herein, the stepwise model refinement based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as a-helix keratins in cortex; and bounded water and thermal resistant components in cuticle. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair.


2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Lambert T. Leong ◽  
Serghei Malkov ◽  
Karen Drukker ◽  
Bethany L. Niell ◽  
Peter Sadowski ◽  
...  

Abstract Background While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection. Methods Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology. Results The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74–0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60–0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues. Conclusion Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.


Author(s):  
Christoph Baumann ◽  
Mads Dam ◽  
Roberto Guanciale ◽  
Hamed Nemati

2019 ◽  
Vol 104 (11) ◽  
pp. 1638-1649 ◽  
Author(s):  
Daniele J. Cherniak ◽  
E. Bruce Watson

Abstract Diffusion of Al and Si has been measured in synthetic and natural rutile under anhydrous conditions. Experiments used Al2O3 or Al2O3-TiO2 powder mixtures for Al diffusant sources, and SiO2-TiO2 powder mixtures or quartz-rutile diffusion couples for Si. Experiments were run in air in crimped Pt capsules, or in sealed silica glass ampoules with solid buffers (to buffer at NNO or IW). Al profiles were measured with Nuclear Reaction Analysis (NRA) using the reaction 27Al(p,γ)28Si. Rutherford Backscattering spectrometry (RBS) was used to measure Si diffusion profiles, with RBS also used in measurements of Al to complement NRA profiles. We determine the following Arrhenius relations from these measurements: For Al diffusion parallel to c, for experiments buffered at NNO, over the temperature range 1100–1400 °C: D Al = 1.21 × 10 − 2 exp ⁡ ( − 531 ± 27 kJ/ mol − 1 / RT ) m 2 s − 1 . For Si diffusion parallel to c, for both unbuffered and NNO-buffered experiments, over the temperature range 1100–1450 °C: D Si = 8.53 × 10 − 13 exp ⁡ ( − 254 ± 31   kJ/ mol − 1 / RT ) m 2 s − 1 . Diffusion normal to (100) is similar to diffusion normal to (001) for both Al and Si, indicating little diffusional anisotropy for these elements. Diffusivities measured for synthetic and natural rutile are in good agreement, indicating that these diffusion parameters can be applied in evaluating diffusivities in rutile in natural systems Diffusivities of Al and Si for experiments buffered at IW are faster (by a half to three-quarters of a log unit) than those buffered at NNO. Si and Al are among the slowest-diffusing species in rutile measured thus far. Diffusivities of Al and Si are significantly slower than the diffusion of Pb and slower than the diffusion of tetravalent Zr and Hf and pentavalent Nb and Ta. These data indicate that Al compositional information will be strongly retained in rutile, providing evidence for the robustness of the recently developed Al in rutile thermobarometer. For example, at 900 °C, Al compositional information would be preserved over ~3 Gyr in the center of 250 μm radius rutile grains, but Zr compositional information would be preserved for only about 300 000 yr at this temperature. Al-in-rutile compositions will also be much better preserved during subsolidus thermal events subsequent to crystallization than those for Ti-in-quartz and Zr-in-titanite crystallization thermometers.


2019 ◽  
Vol 7 (2) ◽  
pp. 51-64
Author(s):  
Valentina Saccone ◽  
Marcelo Vieira ◽  
Alessandro Panunzi

This work presents a preliminary analysis for a prosodic description of two different spoken structures in spoken language within the theoretical framework of the Language into Act Theory (L-AcT): (i) chains of two or more Bound Comments (COB) that do not form a compositional informative and prosodic unit; (ii) compositional Information Units formed by two or more Multiple Comments (CMM) of the List type, linked together by a conventional prosodic model that implements a specific meta-illocutive structure . The goal of this study is to underline specific features of the COB units and the List-type CMM units, detecting prosodic properties of Italian and Brazilian Portuguese spoken language. Through a specific script for Praat software, different parameters are automatically calculated: f0 reset, slope and variation rate, pause duration, spectral emphasis. Our results highlighted a common prosodic behavior in COB-units in terms of f0 movement (rising in the stressed syllable before the break and falling in the unstressed one just before the break), and high similarity between the two COBs and Lists, but also the need to distinguish the effects connected to the position of the stress from the specific features of the unit as detectable Textual Unit.


Genomics ◽  
2019 ◽  
Vol 111 (5) ◽  
pp. 1167-1175 ◽  
Author(s):  
Guoqing Liu ◽  
Guo-Jun Liu ◽  
Jiu-Xin Tan ◽  
Hao Lin

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