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This study explored the interaction between learning conditions, linguistic complexity, and first language (L1) syntactic transfer in semiartificial grammar learning by conceptually replicating and extending Tagarelli et al. (2016). We changed the L1 background, elicited production data during debriefing, and added a binary mixed-effects logistic regression analysis to compare variability at learner and item levels with group-level variation on exposure condition, linguistic complexity, and their interaction. Our results replicated those of the original study regarding the comparative efficacy of explicit instruction; however, we also found a condition × complexity interaction absent in the original study. Debriefing sentence-production data suggest that the changed L1-L2 typological distance may have leveled off the advantage of explicit instruction in the learning of the complex V2-VF structure. Finally, our mixed-effects modeling analysis revealed that variability at learner and item levels accounted for a larger proportion of the variance of the outcomes than all the predictors combined.
Shifts in the maternal gut microbiota have been implicated in the development of gestational diabetes mellitus (GDM). Understanding the interaction between gut microbiota and host glucose metabolism will provide a new target of prediction and treatment. In this nested case-control study, we aimed to investigate the causal effects of gut microbiota from GDM patients on the glucose metabolism of germ-free (GF) mice. Stool and peripheral blood samples, as well as clinical information, were collected from 45 GDM patients and 45 healthy controls (matched by age and prepregnancy body mass index (BMI)) in the first and second trimester. Gut microbiota profiles were explored by next-generation sequencing of the 16S rRNA gene, and inflammatory factors in peripheral blood were analyzed by enzyme-linked immunosorbent assay. Fecal samples from GDM and non-GDM donors were transferred to GF mice. The gut microbiota of women with GDM showed reduced richness, specifically decreased Bacteroides and Akkermansia, as well as increased Faecalibacterium. The relative abundance of Akkermansia was negatively associated with blood glucose levels, and the relative abundance of Faecalibacterium was positively related to inflammatory factor concentrations. The transfer of fecal microbiota from GDM and non-GDM donors to GF mice resulted in different gut microbiota colonization patterns, and hyperglycemia was induced in mice that received GDM donor microbiota. These results suggested that the shifting pattern of gut microbiota in GDM patients contributed to disease pathogenesis.
This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.
Previous studies have shown conflicting findings regarding the relationship between maternal vitamin D deficiency (VDD) and fetal growth restriction (FGR). We hypothesised that parathyroid hormone (PTH) may be an underlying factor relevant to this potential association. In a prospective birth cohort study, descriptive statistics were evaluated for the demographic characteristics of 3407 pregnancies in the second trimester from three antenatal clinics in Hefei, China. The association of the combined status of vitamin D and PTH with birth weight and the risk of small for gestational age (SGA) was assessed by a multivariate linear and binary logistic regression. We found that declined status of 25-hydroxyvitamin D is associated with lower birth weight (for moderate VDD: adjusted β = −49·4 g, 95 % CI −91·1, −7·8, P < 0·05; for severe VDD: adjusted β = −79·8 g, 95 % CI −127·2, −32·5, P < 0·01), as well as ascended levels of PTH (for elevated PTH: adjusted β = −44·5 g, 95 % CI −82·6, −6·4, P < 0·05). Compared with the non-VDD group with non-elevated PTH, pregnancies with severe VDD and elevated PTH had the lowest neonatal birth weight (adjusted β = −124·7 g, 95 % CI −194·6, −54·8, P < 0·001) and the highest risk of SGA (adjusted risk ratio (RR) = 3·36, 95 % CI 1·41, 8·03, P < 0·01). Notably, the highest risk of less Ca supplementation was founded in severe VDD group with elevated PTH (adjusted RR = 4·67, 95 % CI 2·78, 7·85, P < 0·001). In conclusion, elevated PTH induced by less Ca supplementation would further aggravate the risk of FGR in pregnancies with severe VDD through impaired maternal Ca metabolism homoeostasis.
Grain production potential (GrPP) is the maximum production in 1 year that can be achieved by land use under the limitations of climate conditions and in the absence of pests and diseases and other factors. Regional GrPP can change over time and there is an urgent need to identify the main factors affecting regional differences in such changes. Therefore, changes in GrPP were studied for six geographical units in Shaanxi Province, with summer maize and winter wheat as the main grain crops. Changes of GrPP during 2000–2015 were simulated by the global aro-ecological zone model. Analysis of modelled GrPP driven by observed changes in climate and land use suggest that over this period GrPP increased to the north but declined to the south of the Qinling Mountains. This is driven mainly by past changes in climate, with modelled GrPP more sensitive to changes in precipitation than temperature in all geographical units except one. Climate change was the main factor affecting GrPP in all geographical units except one; however, model prediction suggests that land use changes had a clear yield-reducing effect in three of the units. It is the conversion from cultivated land to construction land, grassland and woodland that led to the greatest declines in GrPP in these three geographical units. In order to ensure the stable development of regional agriculture and food security, Shaanxi Province should focus on tapping GrPP north of the Qinling Mountains and increasing the conversion rate of GrPP to actual production.
Japanese brome is a winter annual weed commonly found in wheat fields in China. Laboratory and greenhouse experiments were carried out to determine the effect of temperature, light, pH, osmotic stress, salt stress, and burial depth on the germination and emergence of Japanese brome. Germination was greater than 98% under a wide temperature range of 5 to 30 C and onset of germination was shortened as temperature increased. Light was not required for germination to occur and pH values from 5 to 10 had insignificant effect on germination. Germination was reduced by osmotic stress or salt stress and no germination occurred at −1.3 MPa or 360 mM, suggesting that Japanese brome seed was quite tolerant to osmotic potential and salinity. Seedling emergence was greatest (98%) when seeds were placed on the soil surface but decreased with increasing of burial depth. Only 7% of seedlings emerged at a depth of 5 cm. The results of this study have contributed to our understanding of the germination and emergence of Japanese brome and should enhance our ability to develop better control strategies in wheat farming systems of the Huang-Huai-Hai Plain of China.
To better understand the genus Tripterygium for the Flora of China, the history of the genus and its species is summarized and characters traditionally used to divide the genus re-examined. Because no reliable differentiating characters were found, all of the previously named taxa in the genus are reduced to the single species, Tripterygium wilfordii Hook.f. The range of this monospecific genus is typical of many in the East Asian flora.
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