Effects of Cactus Polysaccharide on Pasting, Rheology, Structural Properties, In Vitro Digestibility, and Freeze-Thaw Stability of Rice Starch.

This study combined rice starch (RS) with cactus polysaccharide (CP) at different composites (0.6%, 1.2%, 1.8%, 2.4%, and 3.0%, w/w), and analyzed the variations in the complex gelatinization properties, rheological properties, thermal properties, structural properties, digestibility, and freeze-thaw stability. As a result, the pasting parameters (p < 0.05) and storage modulus (G') together with the loss modulus (G″) decreased as the CP concentration increased; meanwhile, the RS and the CP-RS gels were pseudoplastic fluids. As revealed by differential scanning calorimetry (DSC), incorporating CP into the starch elevated the starch gelatinization temperature while decreasing gelatinization enthalpy, revealing that CP effectively retarded long-term retrogradation in RS. The gel microstructure and crystallization type altered after adding CP. Typically, CP inclusion could enhance the proportion of resistant starch and slowly digestible starch (SDS), thereby slowing RS hydrolysis. Concurrently, adding CP promoted the RS freeze-thaw stability. These findings could potentially aid in the innovation of CP-based food products.

A background parenchymal enhancement quantification framework of breast magnetic resonance imaging.

Background: Background parenchymal enhancement (BPE) is defined as the enhanced proportion of normal fibroglandular tissue on enhanced magnetic resonance imaging. BPE shows promise as a quantitative imaging biomarker (QIB). However, the lack of consensus among radiologists in their semi-quantitative grading of BPE limits its clinical utility. Methods: The main objective of this study was to develop a BPE quantification model according to clinical expertise, with the BPE integral being used as a QIB to incorporate both the volume and intensity of the enhancement metrics. The model was applied to 2,786 cases to compare our quantitative results with radiologists' semi-quantitative BPE grading to evaluate the effectiveness of using the BPE integral as a QIB for analyzing BPE. Comparisons between multiple groups of nonnormally distributed BPE integrals were performed using the Kruskal-Wallis test. Results: Our study found a considerable degree of concordance between our BPE quantitative integral and radiologists' semi-quantitative assessments. Specifically, our research results revealed significant variability in BPE integral attained through the BPE quantification framework among all semi-quantitative BPE grading groups labeled by experienced radiologists, including mild-moderate (P<0.001), mild-marked (P<0.001), and moderate-marked (P<0.001). Furthermore, there was an apparent correlation between BPE integral and BPE grades, with marked BPE displaying the highest BPE integral, followed by moderate BPE, with mild BPE exhibiting the lowest BPE integral value. Conclusions: The study developed and implemented a BPE quantification framework, which incorporated both the volume and intensity of enhancement and which could serve as a QIB for BPE.

Correction: One-step mild preparation of tough and thermo-reversible poly(vinyl alcohol) hydrogels induced by small molecules.

Correction for 'One-step mild preparation of tough and thermo-reversible poly(vinyl alcohol) hydrogels induced by small molecules' by Chuang Dong et al., Chem. Commun., 2021, 57, 3789-3792, https://doi.org/10.1039/D1CC00578B.

Light-Transmitting Lithium Aluminosilicate Glass-Ceramics with Excellent Mechanical Properties Based on Cluster Model Design.

In this study, for the first time, a cluster-plus-glue-atom model was used to optimize the composition of lithium aluminosilicate glass-ceramics. Basic glass in glass-ceramics was considered to be a 16-unit combination of three-valence {M2O3} and one-valence {Li2O} units. By adjusting the ratio of {M2O3} and {Li2O}, the composition of basic glass could be optimized. After optimization, the average cation valence of the base glass was increased to 2.875. After heat treatment of the optimized base glass, it is found that the crystal size, proportion, and crystallinity changed obviously compared with that before optimization. The main crystalline phases of all the lithium aluminosilicate glass-ceramics prepared in this work were Li2Si2O5 and LiAlSi4O10. All optimized glass-ceramics had an obvious improvement in the crystallinity, with one of the largest having a crystallinity of over 90%. Furthermore, its bending strength was 159 MPa, the microhardness was 967 Hv, and the visible light transmission rate exceeded 90%. Compared with the widely used touch panel cover glass, the optical properties were close, and the mechanical properties were greatly improved. Due to its excellent performance, it could be used in microelectronics, aerospace, deep-sea exploration, and other fields.

Native Oxidation and Complex Magnetic Anisotropy-Dominated Soft Magnetic CoCrFeNi-Based High-Entropy Alloy Thin Films.

Soft magnetic high-entropy alloy thin films (HEATFs) exhibit remarkable freedom of material-structure design and physical-property tailoring, as well as, high cut-off frequencies and outstanding electrical resistivities, making them potential candidates for high-frequency magnetic devices. In this study, a CoCrFeNi film with excellent soft magnetic properties is developed by forming a novel core-shell structure via native oxidation, with ferromagnetic elements Fe, Co, and Ni as the core and the Cr oxide as the shell layer. The core-shell structure enables a high saturation magnetization, enhances the electrical resistivity, and thus reduces the eddy-current loss. For further optimizing the soft magnetic properties, O is deliberately introduced into the HEATFs, and the O-incorporated HEATFs exhibit an electrical resistivity of 237 µΩ·cm, a saturation magnetization of 535 emu cm-3 , and a coercivity of 23 A m-1 . The factors that determine the ferromagnetism and coercivity of the CoCrFeNi-based HEATFs are examined in detail by evaluating the microstructures, magnetic domains, chemical valency, and 3D microscopic compositional distributions of the prepared films. These results are anticipated to provide insights into the magnetic behaviors of soft magnetic HEATFs, as well as aid in the construction of a promising material-design strategy for these unique materials.

Oxidation Behavior of Carbon Fibers in Ceramizable Phenolic Resin Matrix Composites at Elevated Temperatures.

Carbon fiber fabric-reinforced phenolic resin composites are widely used as thermal protection materials for thermal protection systems in hypersonic vehicles and capsules. In this work, carbon fiber fabric-reinforced boron phenolic resin composites modified with MoSi2 and B4C were prepared via a compression molding technique. The high-temperature performance of the composites as well as the oxidation behavior of the carbon fibers was studied. The results indicate that the incorporation of B4C improves the performance of composites at high temperatures. The residual weight rate of composites with 15 phr B4C (BP-15) sufficiently increased from 23.03% to 32.91% compared with the composites without B4C (BP-0). After being treated at 1400 °C for 15 min, the flexural strength of BP-15 increased by 17.79% compared with BP-0. Compared with BP-0, the line ablation rate and mass ablation rate of BP-15 were reduced by 53.96% and 1.56%, respectively. In addition, MoSi2 and B4C particles had a positive effect on the oxidation of carbon fibers in the composites. After treatment at 1400 °C, the diameter of the as-received carbon fiber was reduced by 31.68%, while the diameter of the carbon fiber in BP-0 and BP-15 decreased by 15.12% and 6.14%, respectively. At high temperatures, the liquid B2O3 from B4C and MoSi2-derived complex-phase ceramics (MoB, MoB2, Mo2C, Mo4.8Si3C0.6) acted as an oxygen barrier, effectively mitigating the oxidation degree of the carbon fibers.

Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism.

Lung-resident memory B cells (MBCs) provide localized protection against reinfection in respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class switched, somatically mutated, and intrinsically biased in their differentiation fate toward plasma cells. Combined analysis of antigen specificity and B cell receptor repertoire segregated these subsets into "bona fide" virus-specific MBCs and "bystander" MBCs with no apparent specificity for eliciting viruses generated through an alternative permissive process. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.

External validation study on the value of deep learning algorithm for the prediction of hematoma expansion from noncontrast CT scans.

BACKGROUND: Hematoma expansion is an independent predictor of patient outcome and mortality. The early diagnosis of hematoma expansion is crucial for selecting clinical treatment options. This study aims to explore the value of a deep learning algorithm for the prediction of hematoma expansion from non-contrast computed tomography (NCCT) scan through external validation. METHODS: 102 NCCT images of hypertensive intracerebral hemorrhage (HICH) patients diagnosed in our hospital were retrospectively reviewed. The initial computed tomography (CT) scan images were evaluated by a commercial Artificial Intelligence (AI) software using deep learning algorithm and radiologists respectively to predict hematoma expansion and the corresponding sensitivity, specificity and accuracy of the two groups were calculated and compared. Comparisons were also conducted among gold standard hematoma expansion diagnosis time, AI software diagnosis time and doctors' reading time. RESULTS: Among 102 HICH patients, the sensitivity, specificity, and accuracy of hematoma expansion prediction in the AI group were higher than those in the doctor group(80.0% vs 66.7%, 73.6% vs 58.3%, 75.5% vs 60.8%), with statistically significant difference (p < 0.05). The AI diagnosis time (2.8 ± 0.3 s) and the doctors' diagnosis time (11.7 ± 0.3 s) were both significantly shorter than the gold standard diagnosis time (14.5 ± 8.8 h) (p < 0.05), AI diagnosis time was significantly shorter than that of doctors (p < 0.05). CONCLUSIONS: Deep learning algorithm could effectively predict hematoma expansion at an early stage from the initial CT scan images of HICH patients after onset with high sensitivity and specificity and greatly shortened diagnosis time, which provides a new, accurate, easy-to-use and fast method for the early prediction of hematoma expansion.

Composition formulas of solid-solution alloys derived from chemical-short-range orders.

Solid solutions are the basis for most industrial alloys. However, the relationships between their characteristic short-range orders and chemical compositions have not been established. The present work combines Cowley parameter α with our cluster-plus-glue-atom model to accurately derive the chemical units of binary solid-solution alloys of face-centered cubic type. The chemical unit carries information on atomic structure and chemical composition, which explains prevailing industrial alloys. For example, chemical units in Cu68.9Zn31.1 alloy with α1 = - 0.137 are formulated as [Zn-Cu10Zn2]Zn2Cu2 and [Zn-Cu10Zn2]Zn3Cu1, with 64.0-70.0 wt% Cu corresponding to the most widely used cartridge brass C26000 (68.5-71.5 Cu). This work answers the long-standing question on the composition origin of solid-solution-based industrial alloys, by tracing to the molecule-like chemical units implied in chemical short-range ordering in solid solutions.

Structure of an Al64Cu22Co14 decagonal quasicrystal studied by Cs-corrected STEM.

During the last several decades, since the discovery of a decagonal quasicrystal, a 2 nm cluster model has been widely accepted as its basic quasi-unit-cell (QUC). Instead of the traditional 2 nm QUC, a 3.2 nm QUC is proposed in this paper. The 3.2 nm QUC can fill all the blank areas. The 3.2 nm QUC consists of 251 atoms. The element type and position of each atom are determined using high-angle annular detector dark-field (HAADF) images taken along three projection directions, i.e., one along the ten-fold symmetry and the other two along the two-fold symmetry with an intersection angle of 18 degrees. The proposed model opens an avenue for further investigation of the aperiodic atomic structure of other quasicrystals.