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Abstract: Background and aims: Both drought and vegetation restoration can have dramatic effects on plant community composition, but how they influence soil microbial community diversity, structure, and co-occurrence networks remain less well known. Methods: To better understand the regulatory mechanisms of drought and vegetation restoration on soil microorganisms, we planted 12 native species in precipitation manipulation experimental plots in an invaded coastal grassland in California, USA. We measured soil bacterial and fungal community composition by amplicon sequencing, and quantified plant species richness and coverage in the third experimental year. Results: Our results showed that drought significantly altered soil bacterial diversity and composition; however, neither drought nor vegetation restoration had significant effects on fungal diversity and composition. The control plots had the most cooperative interactions (greatest number of correlations) among bacterial and/or fungal species, while drought plots yielded the most complex co-occurrence network with the highest modularity and clustering coefficient. Structural equation modeling revealed that plant species richness, net gains, and soil moisture played dominant roles in shaping bacterial community structure. Drought and bacterial community structure directly affected fungal community structure. Plant dominant species cover, common species cover, and bacterial diversity were the key drivers in regulating the microbial co-occurrence network complex. Conclusion: We conclude that soil bacterial and fungal communities differ in their responses to abiotic and biotic environmental changes, which may weaken the interspecies interactions among soil microorganisms.

In order to improve soy sauce koji making technology and to stabilize its quality, high-throughput sequencing and headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) were used to analyze the microbial community succession and the changes of total acid, moisture, protease, glucoamylase, amylase and volatile flavor substances during the fermentation of soy sauce koji, and the correlation between microbial community succession and volatile flavor substances was investigated. The results showed that 10 bacterial genera with relative abundance greater than 1% including Weissella, Staphylococcus and Lactobacillus, and three fungal genera with relative abundance greater than 1% including Kodamaea, Aspergillus and Candida were identified, with Weissella, Kodamaea and Aspergillus being dominant throughout the whole fermentation period. With the increase in fermentation time, the moisture content decreased, the total acid content and the activities of protease, amylase and glucoamylase increased, and the total content of volatile flavor substances showed an increasing trend, reaching 4 381.31 μg/100 g at the end of fermentation (48 h). Correlation analysis showed that dominant microorganisms, such as Weissella, Lactobacillus and Kodamaea, affected the activities of protease, glucoamylase and amylase, and significantly influenced the formation of various volatile flavor substances, such as alcohols, aldehydes, esters, acids and pyrazines. This study provides a scientific basis and theoretical foundation for the improvement of the quality of soy sauce koji and the development of koji making technology.

Purposes How to perform bias-correction for precipitation data based on terrain information has been an important problem in meteorological big data research. However, the existing precipitation terrain-based bias-correction methods mostly suffer from two limitations. First, most of them are designed based on statistical models or conventional machine learning models, thus fails to go beyond to the deep learning models with more powerful feature learning ability. Second, they also lack the ability to incorporate the spatiotemporal correlation information in meteorological data for enhancing the bias-correction quality. Methods To address these limitations, in this paper, a terrain-based bias-correction method for precipitation data based on deep learning and spatiotemporal correlation modeling is presented. First, the precipitation data and multi-source terrain information with alignment and encoding based on the longitude and latitude are preprocessed, and thus the corresponding data matrices are constructed. Then the precipitation and terrain data in the spatial and temporal neighborhood of each grid are adopted to achieve the multi-source spatiotemporal information modeling, and a down-sampling strategy is used to alleviate the imbalance between the precipitation grids and the non-precipitation grids. Finally, the deep neural network is constructed for regression and bias-correction. The experiments are conducted on the real meteorological data from over 4 000 meteorological observation stations and over 150 thousand meteorological grids in Guangdong Province. Findings Experimental results have verified the significance influence of the spatiotemporal modeling strategy over precipitation bias correction quality and performance advantage of the proposed method over the baseline methods.

This study, taking into consideration of teachers' as well as students' judgments, explored the generalizability and consistency of the results of linking speaking test of IELTS to China's Standards of English Language Ability (CSE). Nine college English teachers and 81 undergraduate students judged the degree of congruence between the IELTS speaking test and 72 relevant CSE descriptors to generate evidence of generalizability; 2 teachers judged the CSE levels of 11 videos of IELTS speaking test and 113 students assessed themselves based on the self-assessment scale, to provide data for exploring consistency from three perspectives: consistency of teachers' judgments, consistency between teachers' judgments and empirical scores, and consistency between students' actual ability and the ability demonstrated in the self-assessment scale. The evidence showed that the linking results performed well both in generalizability and consistency from the perspective of both teachers and students, but students rated comparatively low than teachers both in the two facets. However, considering that certain descriptors are relatively abstract and separate from daily life situations in IELTS speaking test, it is understandable that students rate low in generalizability; and considering students' insufficient self-assessment ability, their relative low recognition in consistency also get explained. In general, therefore, the linking results show good generalizability and consistency.

Rating scales for writing assessment are critical in that they determine directly the quality and fairness of such performance tests. However, in many EFL contexts, rating scales are made, to certain extent, based on the intuition of teachers who strongly need a feasible and scientific route to guide their construction of rating scales. This study aims to design an operational model of rating scale construction with English summary writing as an example. Altogether 325 university English teachers, 4 experts in language assessment and 60 English majors in China participated in the study. 20 textual attributes were extracted, through text analysis, from China's Standards of English Language Ability (CSE), theoretical construct of summary writing, comments on sample summary writing essays from 8 English teachers and their personal judgement. The textual attributes were then investigated through a large-scale questionnaire survey. Exploratory factor analysis and expert judgement were employed to determine rating scale dimensions. Regression analysis and expert judgement were conducted to determine the weighting distribution across all dimensions. Based on such endeavors, a tentative operational model of rating scale construction was established, which can also be applied and adapted to develop rating scales in other writing assessment.

CD44 is associated with a high risk of metastasis, recurrence, and drug resistance in various cancers. Here we report that platelet endothelial aggregation receptor 1 (PEAR1) is a CD44 chaperone protein that protected CD44 from endocytosis-mediated degradation and enhances cleavage of the CD44 intracellular domain (CD44-ICD). Furthermore, we found that lysyl oxidase-like protein 2 (LOXL2), an endogenous ligand of PEAR1, bound to the PEAR1-EMI domain and facilitated the interaction between PEAR1 and CD44 by inducing PEAR1 Ser891 phosphorylation in a manner that was independent of its enzyme activity. Levels of PEAR1 protein and PEAR1 phosphorylation at Ser891 were increased in patients with triple-negative breast cancer (TNBC), were positively correlated with expression of LOXL2 and CD44, and were negatively correlated with overall survival. The level of PEAR1 Ser891 phosphorylation was identified as the best independent prognostic factor in TNBC patients. The prognostic efficacy of the combination of PEAR1 phosphorylation at Ser891 and CD44 expression was superior to that of PEAR1 phosphorylation at Ser891 alone. Blocking the interaction between LOXL2 and PEAR1 with monoclonal antibodies significantly inhibited TNBC metastasis, representing a promising therapeutic strategy for TNBC., Introduction Breast cancer is the most prevalent malignancy in women, and its incidence is increasing (1). Of particular concern is triple-negative breast cancer (TNBC), which is an exceptionally aggressive and [...]