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In this paper, 66 samples belonging to 12 varieties of domestic high-gluten wheat in 2021 were collected, and their physicochemical, rheological and bread sensory properties were analyzed. The results of the main component analysis showed that the total sensory score of bread had a strong correlation with gluten index, maximum tensile resistance, mixing time, stretching area and stabilization time, etc. At the same time, the screening of wheat varieties with high similarity through cluster analysis could play a key guiding role in the development and maintenance of production stability of baking special flour. Comparing the fitting quality of the bread total sensory score prediction model established by the three methods of stepwise regression, partial least squares and neural network model, the model built using artificial neural network was significantly better than other models. Using neural network models, the baking characteristics of different varieties of wheat could be quickly predicted, ensuring product stability, while also facilitating the development of more targeted baking special flours.

Sorafenib resistance has been a major factor limiting its clinical use as a targeted drug in liver cancer. The present study aimed to investigate whether cryptotanshinone can enhance the sensitivity of liver cancer and reduce the resistance to sorafenib.

Objectives: To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL. Methods: 83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman's correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models. Results: Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966–0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895–0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953–0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828. Conclusion: rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL. [ABSTRACT FROM AUTHOR]

It is vital to measure combustion temperature to define combustion models accurately. For single fuel particles in powder-fueled ramjets, their size distribution ranges from submicron to submillimeter, and their burn time is short to millisecond order. Moreover, the radiation intensity of different types of fuel particles significantly oscillated with several orders of magnitude. Current temperature measurement technology is facing this challenge. This paper proposes a highly spatiotemporal resolved pyrometry to measure the combustion temperature of fuel particles by coupling single-point photomultiplier tube (PMT)-based and two-dimensional complementary metal oxide semiconductor (CMOS)-based photoelectric devices. Both the offline calibration by blackbody furnace and online calibration by standard lamp confirmed the measurement accuracy of the pyrometry. Then, the pyrometry was used to measure the combustion temperature of fuel particles including micro-Al, nano-Al, micro-Mg, nano-B, and micro-B4C. The temperature evolution and distribution of burning fuel particles were complementarily obtained, especially the interfacial flame temperature near the particle surface. Based on the obtained combustion temperature, the combustion characteristics and the energy release efficiencies among these fuels were evaluated and compared in detail, which are helpful to recognize, in depth, the combustion behavior and reveal the combustion mechanism of fuel particles in powder-fueled ramjets. [ABSTRACT FROM AUTHOR]

As an important physiological indicator, wheat canopy temperature (CT) can be observed after flowering in an attempt to predict wheat yield and quality. However, the relationship between CT and wheat yield and quality is not clear. In this study, the CT, photosynthetic rate (Pn), filling rate, wheat yield, and wheat quality of 68 wheat lines were measured, in an attempt to establish a connection between CT and yield and quality and accelerate the selection of new varieties. This experiment used an infrared imaging camera to measure the CT of wheat materials planted in the field in 2022. Twenty materials with significant temperature differences were selected for planting in 2023. By comparing the temperature trends in 2022 and 2023, it is believed that materials 4 and 13 were cold-type materials, while materials 3 and 11 were warm-type materials. The main grain filling period of cold-type materials occurs in the middle and late stages of the grain filling period and the Pn and the thousand-grain weights of cold-type materials were higher than those of warm-type materials. Similarly, under continuous rainy conditions, cold-type materials had a higher protein and wet gluten contents, while warm-type materials had higher sedimentation values and shorter formation times. [ABSTRACT FROM AUTHOR]

Climate change has led to significant changes in water and heat conditions in crop production areas, which have in turn affected the spring maize growth and yield. This study analyzed the spatiotemporal variation characteristics of the water and heat conditions, such as the growth degree-day (GDD), killing degree-day (KDD), sunshine hours (SD), effective precipitation (Pe), and irrigation water requirement (IR), of spring maize in Inner Mongolia based on data from 50 meteorological stations. The relationship between hydrothermal conditions and yield was revealed using methods that included stepwise regression analysis. The results showed that the GDD during the spring maize growth period ranged from 513 to 2011 °C, with high-value areas concentrated in western and southeastern regions of Inner Mongolia. The GDD showed an increasing trend during all the growth periods. High-KDD areas were mainly distributed in the Alxa League region in western Inner Mongolia, and the KDD showed an increasing trend during all periods except the rapid fertility period. The spatial distribution of the SD was consistent with that of the GDD, and the SD values for each reproductive period showed a decreasing trend. The average Pe and IR values in the last 60 years were 111 mm and 386 mm, respectively, and showed opposite spatial distribution trends. The overall Pe trend was decreasing and that of the IR was increasing, which will aggravate future water resource consumption in the region. Stepwise regression analysis showed that the Pe was the main factor affecting the spring maize yield. Overall, the spring maize fertility period in various regions of Inner Mongolia was extremely uneven in terms of the hydrothermal conditions. This study provides a basis for the regional spatial spring maize layout and the sustainable use of water and heat resources in the region. [ABSTRACT FROM AUTHOR]

Freshwater fungi consist of a highly diverse group of organisms in freshwater habitats worldwide. During a survey of fungal diversity in freshwater habitats across different regions of Jiangxi Province, China, four freshwater fungi were collected. To study their phylogenetic relationships, the internal transcribed spacer (ITS1-5.8S-ITS2), large subunit (28S, LSU), small subunit (18S, SSU), and RNA polymerase II subunit (RPB2) genes were selected for phylogenetic analyses. Based on morphology coupled with phylogenetic analysis, these strains were confirmed to belong to Phaeoisaria, Pleurothecium, and Pseudodactylaria. Among them, three fungi were confirmed as the new species, namely, Pleurothecium lignicola (Pleurotheciaceae), Pseudodactylaria jiangxiensis (Pseudodactylariaceae), and Ps. lignicola (Pseudodactylariaceae). One species was identified as Phaeoisaria filiformis (Pleurotheciaceae), a new record of this species in China. All species were compared with other similar species, and detailed descriptions, illustrations, and phylogenetic data were provided. [ABSTRACT FROM AUTHOR]