Viscoelastic properties of green wood across the grain measured by harmonic tests in the range of 0\degree C to 95\degree C. Hardwood vs. softwood and normal wood vs. reaction wood

The viscoelastic properties of wood have been investigated with a dynamic mechanical analyser (DMA) specifically conceived for wooden materials, the WAVET device (environmental vibration analyser for wood). Measurements were carried out on four wood species in the temperature range of 0\degree C to 100\degree C at frequencies varying between 5 mHz and 10 Hz. Wood samples were tested in water-saturated conditions, in radial and tangential directions. As expected, the radial direction always revealed a higher storage modulus than the tangential direction. Great differences were also observed in the loss factor. The tan\delta peak and the internal friction are higher in tangential direction than in radial direction. This behaviour is attributed to the fact that anatomical elements act depending on the direction. Viscoelastic behaviour of reaction wood differs from that of normal or opposite wood. Compression wood of spruce, which has higher lignin content, is denser and stiffer in transverse directions than normal wood, and has lower softening temperature (Tg). In tension wood, the G-layer is weakly attached to the rest of the wall layers. This may explain why the storage modulus and the softening temperature of tension wood are lower than those for the opposite wood. In this work, we also point out that the time-temperature equivalence fits only around the transition region, i.e. between Tg and Tg + 30\degree C. Apart from these regions, the wood response combines the effect of all constitutive polymers, so that the equivalence is not valid anymore

Identification and transfer of spatial transcriptomics signatures for cancer diagnosis

Background: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. Methods: We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles. Results: We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC. Conclusions: This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future

Origin of micro-scale heterogeneity in polymerisation of photo-activated resin composites

Photo-activated resin composites are widely used in industry and medicine. Despite extensive chemical characterisation, the micro-scale pattern of resin matrix reactive group conversion between filler particles is not fully understood. Using an advanced synchrotron-based wide-field IR imaging system and state-of-the-art Mie scattering corrections, we observe how the presence of monodispersed silica filler particles in a methacrylate based resin reduces local conversion and chemical bond strain in the polymer phase. Here we show that heterogeneity originates from a lower converted and reduced bond strain boundary layer encapsulating each particle, whilst at larger inter-particulate distances light attenuation and monomer mobility predominantly influence conversion. Increased conversion corresponds to greater bond strain, however, strain generation appears sensitive to differences in conversion rate and implies subtle distinctions in the final polymer structure. We expect these findings to inform current predictive models of mechanical behaviour in polymer-composite materials, particularly at the resin-filler interface

Modeling of negative Poisson’s ratio (auxetic) crystalline cellulose Iβ

Energy minimizations for unstretched and stretched cellulose models using an all-atom empirical force field (Molecular Mechanics) have been performed to investigate the mechanism for auxetic (negative Poisson’s ratio) response in crystalline cellulose Iβ from kraft cooked Norway spruce. An initial investigation to identify an appropriate force field led to a study of the structure and elastic constants from models employing the CVFF force field. Negative values of on-axis Poisson’s ratios nu31 and nu13 in the x1-x3 plane containing the chain direction (x3) were realized in energy minimizations employing a stress perpendicular to the hydrogen-bonded cellobiose sheets to simulate swelling in this direction due to the kraft cooking process. Energy minimizations of structural evolution due to stretching along the x3 chain direction of the ‘swollen’ (kraft cooked) model identified chain rotation about the chain axis combined with inextensible secondary bonds as the most likely mechanism for auxetic response