Devitrification and recrystallization of nanoparticle-containing glycerol and PEG-600 solutions

Nanoparticles in solution offer unique electrical, mechanical and thermal properties due to their physical presence and interaction with the state of dispersion. This work is aimed to study the effects of hydroxyapatite (HA) nanoparticles on the devitrification and recrystallization events of two important cryoprotective solutions used in cell and tissue preservation namely glycerol (60% w/w) and PEG-600 (50% w/w). HA nanoparticles (20, 40 or 60 nm) were incorporated into solutions at the content of 0.1% or 0.5% (w/w), and were studied by differential scanning calorimeter (DSC) and cryomicroscopy. The presence of nanoparticles does not change the glass transition temperatures and melting temperatures of quenched solutions, but significantly affects the behavior of devitrification and recrystallization upon warming. Cryomicroscopic investigation showed the complex interactions among solution type, nanoparticle size and nanoparticle content, which apparently influence ice crystal growth or recrystallization in the quenched dispersions. These findings have significant implications for biomaterial cryopreservation, cryosurgery, and food manufacturing. The complexity of ice crystal growth kinetics in nanoparticle-containing dispersions remains to be poorly understood at the moment.     

Morpho-anatomical and physiological responses to waterlogging stress in different barley (<Emphasis Type="Italic">Hordeum vulgare</Emphasis> L.) genotypes

Waterlogging is one of the major stresses limiting crop production worldwide. The understanding of the mechanisms of plant adaptations to waterlogging stress helps improve plant tolerance to stress. In this study, physiological responses and morpho-anatomical adaptations of seven different barley genotypes were investigated under waterlogging stress. The results showed that the waterlogging-tolerant varieties (TX9425, Yerong, TF58) showed less reduction in plant height, SPAD (soil–plant analyses development analyses) value, tillers, shoot and root biomasses than did the waterlogging-sensitive varieties (Franklin, Naso Nijo, TF57). Under waterlogging stress condition, the tolerant genotypes also showed a much larger number of adventitious roots than did the sensitive genotypes. More intercellular spaces and better integrated chloroplast membrane structures were observed in the leaves of the waterlogging-tolerant cultivars, which is likely due to increased ethylene content, decreased ABA content and less accumulation of O₂^.?. The ability to form new adventitious roots and intercellular spaces in shoots can also be used as selection criteria in breeding barley for waterlogging tolerance.     

α‐ketoglutarate delays age‐related fertility decline in mammals

The fecundity reduction with aging is referred as the reproductive aging which comes earlier than that of chronological aging. Since humans have postponed their childbearing age, to prolong the reproductive age becomes urgent agenda for reproductive biologists. In the current study, we examined the potential associations of α‐ketoglutarate (α‐KG) and reproductive aging in mammals including mice, swine, and humans. There is a clear tendency of reduced α‐KG level with aging in the follicle fluids of human. To explore the mechanisms, mice were selected as the convenient animal model. It is observed that a long term of α‐KG administration preserves the ovarian function, the quality and quantity of oocytes as well as the telomere maintaining system in mice. α‐KG suppresses ATP synthase and alterations of the energy metabolism trigger the nutritional sensors to down‐regulate mTOR pathway. These events not only benefit the general aging process but also maintain ovarian function and delay the reproductive decline. Considering the safety of the α‐KG as a naturally occurring molecule in energy metabolism, its utility in reproduction of large mammals including humans deserves further investigation.     

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high‐dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA‐SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm^?1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.