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Background: CD72, a co-receptor of B cell receptor (BCR), has been reported to have both positive and negative effects on B cell functions in several immunological diseases. The B cell plays an important role in the pathogenesis of primary Sjogren's syndrome (pSS). However, whether CD72 is involved in the process remains unknown. This study aimed to observe the possible role of CD72 in the pathogenesis of pSS. Results: A total of 60 cases who fulfilled the American-European Consensus Group (AECG) criteria for the diagnosis of pSS and 61 gender and age-matched healthy controls were recruited in this study. The percentage of CD72+ B cells was 85.31 ± 8.37% in pSS patients and 76.91 ± 8.50% in healthy controls(p < 0.001). The percentage of CD72+ B cells was correlated to serum IgG levels in patients [β = 0.018(0.001–0.036), p = 0.034]. The level of serum soluble CD72 was significantly higher in pSS patients than the one in healthy controls (0.41 (0.29) vs 0.07 (0.08) ng/mL, p < 0.001). Conclusions: The percentage of CD72+ B cells was upregulated in pSS patients and was correlated to the serum IgG level, which revealed the hyperactivity of B cells in this disease. The serum soluble CD72 level was also increased in pSS patients. These results indicated a potential role of CD72 in the pathogenesis of pSS. [ABSTRACT FROM AUTHOR]

Despite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high-dimensional, noisy data, etc. The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data. In this context, it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model. Ensemble learning, as one research hot spot, aims to integrate data fusion, data modeling, and data mining into a unified framework. Specifically, ensemble learning firstly extracts a set of features with a variety of transformations. Based on these learned features, multiple learning algorithms are utilized to produce weak predictive results. Finally, ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way. In this paper, we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics. In addition, we present challenges and possible research directions for each mainstream approach of ensemble learning, and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning, reinforcement learning, etc. [ABSTRACT FROM AUTHOR]

Kiasu-BC and Joltik-BC are internal tweakable block ciphers of authenticated encryption algorithms Kiasu and Joltik submitted to the CAESAR competition. Kiasu-BC is a 128-bit block cipher, of which tweak and key sizes are 64 and 128 bits, respectively. Joltik-BC-128 is a 64-bit lightweight block cipher supporting 128 bits tweakey. Its designers recommended the key and tweak sizes are both 64 bits. In this paper, we propose improved meet-in-the-middle attacks on 8-round Kiasu-BC, 9-round and 10-round Joltik-BC-128 by exploiting properties of their structures and using precomputation tables and the differential enumeration. For Kiasu-BC, we build a 5-round distinguisher to attack 8-round Kiasu-BC with |$2^{109}$| plaintext–tweaks, |$2^{112.8}$| encrytions and |$2^{92.91}$| blocks. Compared with previously best known cryptanalytic results on 8-round Kiasu-BC under chosen plaintext attacks, the data and time complexities are reduced by |$2^{7}$| and |$2^{3.2}$| times, respectively. For the recommended version of Joltik-BC-128, we construct a 6-round distinguisher to attack 9-round Joltik-BC-128 with |$2^{53}$| plaintext–tweaks, |$2^{56.6}$| encryptions and |$2^{52.91}$| blocks, respectively. Compared with previously best known results, the data and time complexities are reduced by |$2^7$| and |$2^{5.1}$| times, respectively. In addition, we present a 6.5-round distinguisher to attack 10-round Joltik-BC-128 with |$2^{53}$| plaintext–tweaks, |$2^{101.4}$| encryptions and |$2^{76.91}$| blocks. [ABSTRACT FROM AUTHOR]

Enhanced ultraviolet‐B (UV‐B) radiation is induced by the depletion of the stratospheric ozone layer. Silicate is beneficial to rice growth and can increase the resistance of rice plant to UV‐B radiation, but so far few reports have been available on whether silicate application can reduce CH4 and N2O emissions from paddy soils under enhanced UV‐B radiation. A field experiment was conducted to investigate the effects of silicate application on CH4 and N2O emissions and global warming potentials (GWPs) under enhanced UV‐B radiation in a paddy soil. The experiment with rice was designed with two UV‐B radiation levels, that is, ambient UV‐B (A, ambient) and enhanced UV‐B radiation (E, enhanced by 20%); and two silicate application levels, that is, control (Si0, 0 kg SiO2·ha−1) and silicate application (Si1, 200 kg SiO2·ha−1). CH4 and N2O fluxes were determined by closed chamber method at one‐week interval during rice growth period. The results show that, compared with ambient UV‐B radiation, enhanced UV‐B radiation clearly decreased the dry matter weights of shoot, root, and whole plant by 13.12%, 53.31%, and 25.85%, respectively, in the treatment without silicate application, and by 1.47%, 34.49%, and 11.12%, respectively, in the treatment with silicate application. Enhanced UV‐B radiation significantly increased the flux and cumulative emission of N2O and stimulated the GWPs of CH4 and N2O. Silicate application significantly reduced flux and cumulative emission of CH4, promoted the flux and cumulative emission of N2O, and reduced the GWPs of CH4 and N2O. This study suggests that silicate application can reduce the contribution of enhanced UV‐B radiation to global warming potentials. [ABSTRACT FROM AUTHOR]

Transglutaminase (TGase)-mediated crosslinking serves as a promising strategy to mitigate soy antigenicity. β-Conglycinin is recognized as a key allergen among soy proteins. Nonetheless, our understanding of the underlying relationship between immunoreactivity and protein structure remains inadequate. This study aimed to examine the impact of TGase-mediated crosslinking on the immunoreactivity of β-conglycinin in a dual-protein system consisting of soy protein isolate (SPI) and whey protein isolate (WPI) (with SPI-WPI ratios of 10:0, 7:3, 5:5, 3:7 (w/w)). Conformational changes were also examined. Results revealed that β-lactoglobulin and α-lactalbumin in WPI were less susceptible to TGase crosslinking compared to the 7S and 11S subunits in SPI. The conformation of proteins in the dual-protein system after TGase crosslinking were altered, as demonstrated by increased ultraviolet absorption intensity, fluctuant intrinsic fluorescence intensity and surface hydrophobicity, and decreased free sulfhydryl content. As the crosslinking time of TGase increased from 1 h to 18 h, the allergenicity of β-conglycinin gradually decreased in all four SPI-WPI ratios. Notably, the SPI3-WPI7 ratio exhibited the lowest allergenicity of 77.21 ± 1.89% after 18 h of TGase catalyzation. Overall, the dual-protein system proved to be more effective in reducing the allergenicity of β-conglycinin. [Display omitted] • TGase treatment can alter the conformation of soy-cow milk allergenic proteins. • Using TGase can reduce the antigenicity of β-conglycinin, especially in dual-protein system. • SPI3-WPI7 treated by TGase for 18 h had the lowest antigenicity. • TGase crosslinking may be efficient to produce hypoallergenic dual-protein products. [ABSTRACT FROM AUTHOR]

This paper describes the assembly of an updated dataset of detrital zircon geochronology and Lu–Hf isotopes for Turkey, Cyprus and Greek peninsula. This first version of the dataset documented 286 samples with detrital zircon U–Pb data and 70 samples with zircon Lu–Hf isotopes from 42 published articles. These samples are mainly distributed in seven geologic‐tectonic units in the Eastern Mediterranean Tethyan region. The compilation of dataset will be periodically accessed in the Deep‐Time Digital Earth repository, containing more updated raw data of (un)published scientific research. We believe that the construction of such a dataset is fundamental to studies of clastic strata and also to understanding of crustal evolution in the Eastern Mediterranean region. [ABSTRACT FROM AUTHOR]