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The identification of herbicide tolerance is essential for effective chemical weed control. According to whole-plant dose-response assays, none of 29 pond lovegrass [Eragrostis japonica (Thunb.) Trin.] populations were sensitive to penoxsulam. The effective dose values of penoxsulam to E. japonica populations causing 50% inhibition of fresh weight (GR50: 105.14 to 148.78 g ai ha-1) were much higher than the recommended dose of penoxsulam (15 to 30 g ai ha-1) in the field. This confirmed that E. japonica was tolerant to penoxsulam. Eragrostis japonica populations showed 52.83- to 74.76-fold higher tolerance to penoxsulam than the susceptible barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.]. The mechanisms of tolerance to penoxsulam in E. japonica were continued to be identified. In vitro activity assays revealed that the penoxsulam concentration required to inhibit 50% of the acetolactate synthase (ALS) activity (IC50) was 12.27-fold higher in E. japonica than that in E. crus-galli. However, differences in the ALS gene, previously found to endow target-site resistance in weeds, were not detected in the sequences obtained. Additionally, the expression level of genes encoding ALS in E. japonica was approximately 2-fold higher than that in E. crus-galli after penoxsulam treatment. Furthermore, penoxsulam tolerance can be significantly reversed by three cytochrome P450 monooxygenases (Cyt P450s) inhibitors (1-aminobenzotriazole, piperonyl butoxide, and malathion) and the activity of NADPH-dependent cytochrome P450 reductase toward penoxsulam in E. japonica increased significantly (approximately 7-fold higher) compared to that of treated E. crus-galli. Taken together, these results indicate that lower ALS sensitivity, relatively higher ALS expression levels, and stronger metabolism of Cyt P450s combined to bring about penoxsulam tolerance in E. japonica.
With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.
The increasing FDI in Africa from China and India in recent years has drawn the attention of scholars, policy makers, and the media. Africa is an arena where the two emerging giants compete on different bases. Indian firms have the advantage of institutional proximity and large diaspora communities in Africa, while Chinese firms rely on government-to-government relationship-building. The rivalry between India's soft power and Chinese hard power in Africa will continue for the foreseeable future.
To investigate the association between folate levels and the risk of gestational diabetes mellitus (GDM) risk during the whole pregnancy.
In this retrospective cohort study of pregnant women, serum folate levels were measured before 24 gestational weeks (GW). GDM was diagnosed between 24th and 28th GW based on the criteria of the International Association of Diabetes and Pregnancy Study Groups. General linear models were performed to examine the association of serum folate with plasma glucose (i.e. linear regressions) and risk of GDM (i.e. log-binomial regressions) after controlling for confounders. Restricted cubic spline regression was conducted to test the dosage–response relationship between serum folate and the risk of GDM.
A sigle, urban hospital in Shanghai, China.
A total of 42 478 women who received antenatal care from April 2013 to March 2017 were included.
Consistent positive associations were observed between serum folate and plasma glucose levels (fasting, 1-h, 2-h). The adjusted relative risks (RR) and 95 % CI of GDM across serum folate quartiles were 1·00 (reference), 1·15 (95 % CI (1·04, 1·26)), 1·40 (95 % CI (1·27, 1·54)) and 1·54 (95 % CI (1·40, 1·69)), respectively (P-for-trend < 0·001). The positive association between serum folate and GDM remained when stratified by vitamin B12 (adequate v. deficient groups) and the GW of serum folate measurement (≤13 GW v. >13 GWs)
The findings of this study may provide important evidence for the public health and clinical guidelines of pregnancy folate supplementation in terms of GDM prevention.
Investigations illustrate that the Internet of Things (IoT) can save costs, increase efficiency, improve quality, and provide data-driven preventative maintenance services. Intelligent sensors, dependable connectivity, and complete integration are essential for gathering real-time information. IoT develops home appliances for improved customer satisfaction, personalization, and enhanced big data analytics as a crucial Industry 4.0 enabler. Because the product design process is an important part of controlling manufacturing, there are constant attempts to improve and minimize product design time. Utilizing a hybrid algorithm, this research provides a novel method to schedule design products in production management systems to optimize energy usage and design time (combined particle optimization algorithm and shuffled frog leaping algorithm). The issue with particle optimization algorithms is that they might become stuck in local optimization and take a long time to converge to global optimization. The strength of the combined frog leaping algorithm local searching has been exploited to solve these difficulties. The MATLAB programming tool is used to simulate the suggested technique. The simulation findings were examined from three perspectives: energy usage, manufacturing time, and product design time. According to the findings, the recommended strategy performed better in minimizing energy use and product design time. These findings also suggest that the proposed strategy has a higher degree of convergence when discovering optimal solutions.
This study aimed to evaluate the efficacy and safety of high-frequency oscillation ventilation combined with intermittent mandatory ventilation in infants with acute respiratory distress syndrome after congenital heart surgery.
We retrospectively analysed the clinical data of 32 infants who were ventilated due to acute respiratory distress syndrome after congenital heart surgery between January, 2020 and January, 2022. We adopted high-frequency oscillation ventilation combined with intermittent mandatory ventilation as the rescue ventilation mode for infants who were failing conventional mechanical ventilation.
After rescue high-frequency oscillation ventilation combined with intermittent mandatory ventilation, the dynamic compliance (Cdyn), PaO2 and PaO2/FiO2 ratio of the infants improved compared with conventional mechanical ventilation (p < 0.05). Moreover, high-frequency oscillation ventilation combined with intermittent mandatory ventilation resulted in a significant decrease in arterial-alveolar oxygen difference (AaDO2), FiO2, and oxygenation index (p < 0.05). No significant effect on haemodynamic parameters was observed. Moreover, no serious complications occurred in the two groups.
Rescue high-frequency oscillation ventilation combined with intermittent mandatory ventilation significantly improved oxygenation in infants who failed conventional mechanical ventilation for acute respiratory distress syndrome after congenital heart surgery. Thus, this strategy is considered safe and feasible. However, further studies must be conducted to confirm the efficacy and safety of high-frequency oscillation ventilation combined with intermittent mandatory ventilation as a rescue perioperative respiratory support strategy for CHD.
Maternal gestational weight gain (GWG) is an important determinant of infant birth weight, and having adequate total GWG has been widely recommended. However, the association of timing of GWG with birth weight remains controversial. We aimed to evaluate this association, especially among women with adequate total GWG. In a prospective cohort study, pregnant women’s weight was routinely measured during pregnancy, and their GWG was calculated for the ten intervals: the first 13, 14–18, 19–23, 24–28, 29–30, 31–32, 33–34, 35–36, 37–38 and 39–40 weeks. Birth weight was measured, and small-for-gestational-age (SGA) and large-for-gestational-age were assessed. Generalized linear and Poisson models were used to evaluate the associations of GWG with birth weight and its outcomes after multivariate adjustment, respectively. Of the 5049 women, increased GWG in the first 30 weeks was associated with increased birth weight for male infants, and increased GWG in the first 28 weeks was associated with increased birth weight for females. Among 1713 women with adequate total GWG, increased GWG percent between 14 and 23 weeks was associated with increased birth weight. Moreover, inadequate GWG between 14 and 23 weeks, compared with the adequate GWG, was associated with an increased risk of SGA (43 (13·7 %) v. 42 (7·2 %); relative risk 1·83, 95 % CI 1·21, 2·76). Timing of GWG may influence infant birth weight differentially, and women with inadequate GWG between 14 and 23 weeks may be at higher risk of delivering SGA infants, despite having adequate total GWG.
In the United States, cardiovascular disease is the leading cause of death and the rate of maternal mortality remains among the highest of any industrialized nation. Maternal cardiometabolic health throughout gestation and postpartum is representative of placental health and physiology. Both proper placental functionality and placental microRNA expression are essential to successful pregnancy outcomes, and both are highly sensitive to genetic and environmental sources of variation. Placental pathologies, such as preeclampsia, are associated with maternal cardiovascular health but may also contribute to the developmental programming of chronic disease in offspring. However, the role of more subtle alterations to placental function and microRNA expression in this developmental programming remains poorly understood. We performed small RNA sequencing to investigate microRNA in placentae from the Rhode Island Child Health Study (n = 230). MicroRNA counts were modeled on maternal family history of cardiovascular disease using negative binomial generalized linear models. MicroRNAs were considered to be differentially expressed at a false discovery rate (FDR) less than 0.10. Parallel mRNA sequencing data and bioinformatic target prediction software were then used to identify potential mRNA targets of differentially expressed microRNAs. Nine differentially expressed microRNAs were identified (FDR < 0.1). Bioinformatic target prediction revealed 66 potential mRNA targets of these microRNAs, many of which are implicated in TGFβ signaling pathway but also in pathways involving cellular metabolism and immunomodulation. A robust association exists between familial cardiovascular disease and placental microRNA expression which may be implicated in both placental insufficiencies and the developmental programming of chronic disease.
N-acetylcysteine (NAC) possesses a strong capability to ameliorate high-fat diet (HFD)-induced non-alcoholic fatty liver disease (NAFLD) in mice, but the underlying mechanism is still unknown. Our study aimed to clarify the involvement of long non-coding RNA (lncRNA) in the beneficial effects of NAC on HFD-induced NAFLD. C57BL/6J mice were fed a normal-fat diet (10 % fat), a HFD (45 % fat) or a HFD plus NAC (2 g/l). After 14-week of intervention, NAC rescued the deleterious alterations induced by HFD, including the changes in body and liver weights, hepatic TAG, plasma alanine aminotransferase, plasma aspartate transaminase and liver histomorphology (haematoxylin and eosin and Oil red O staining). Through whole-transcriptome sequencing, 52 167 (50 758 known and 1409 novel) hepatic lncRNA were detected. Our cross-comparison data revealed the expression of 175 lncRNA was changed by HFD but reversed by NAC. Five of those lncRNA, lncRNA-NONMMUT148902·1 (NO_902·1), lncRNA-XR_001781798·1 (XR_798·1), lncRNA-NONMMUT141720·1 (NO_720·1), lncRNA-XR_869907·1 (XR_907·1), and lncRNA-ENSMUST00000132181 (EN_181), were selected based on an absolute log2 fold change value of greater than 4, P-value < 0·01 and P-adjusted value < 0·01. Further qRT-PCR analysis showed the levels of lncRNA-NO_902·1, lncRNA-XR_798·1, and lncRNA-EN_181 were decreased by HFD but restored by NAC, consistent with the RNA sequencing. Finally, we constructed a ceRNA network containing lncRNA-EN_181, 3 miRNA, and 13 mRNA, which was associated with the NAC-ameliorated NAFLD. Overall, lncRNA-EN_181 might be a potential target in NAC-ameliorated NAFLD. This finding enhanced our understanding of the biological mechanisms underlying the beneficial role of NAC.
This study is performed to figure out how the presence of diabetes affects the infection, progression and prognosis of 2019 novel coronavirus disease (COVID-19), and the effective therapy that can treat the diabetes-complicated patients with COVID-19. A multicentre study was performed in four hospitals. COVID-19 patients with diabetes mellitus (DM) or hyperglycaemia were compared with those without these conditions and matched by propensity score matching for their clinical progress and outcome. Totally, 2444 confirmed COVID-19 patients were recruited, from whom 336 had DM. Compared to 1344 non-DM patients with age and sex matched, DM-COVID-19 patients had significantly higher rates of intensive care unit entrance (12.43% vs. 6.58%, P = 0.014), kidney failure (9.20% vs. 4.05%, P = 0.027) and mortality (25.00% vs. 18.15%, P < 0.001). Age and sex-stratified comparison revealed increased susceptibility to COVID-19 only from females with DM. For either non-DM or DM group, hyperglycaemia was associated with adverse outcomes, featured by higher rates of severe pneumonia and mortality, in comparison with non-hyperglycaemia. This was accompanied by significantly altered laboratory indicators including lymphocyte and neutrophil percentage, C-reactive protein and urea nitrogen level, all with correlation coefficients >0.35. Both diabetes and hyperglycaemia were independently associated with adverse prognosis of COVID-19, with hazard ratios of 10.41 and 3.58, respectively.
Video materials require learners to manage concurrent verbal and pictorial processing. To facilitate second language (L2) learners’ video comprehension, the amount of presented information should thus be compatible with human beings’ finite cognitive capacity. In light of this, the current study explored whether a reduction in multimodal comprehension scaffolding would lead to better L2 comprehension gain when viewing captioned videos and, if so, which type of reduction (verbal vs. nonverbal) is more beneficial. A total of 62 L2 learners of English were randomly assigned to one of the following viewing conditions: (1) full captions + animation, (2) full captions + static key frames, (3) partial captions + animation, and (4) partial captions + static key frames. They then completed a comprehension test and cognitive load questionnaire. The results showed that while viewing the video with reduced nonverbal visual information (static key frames), the participants had well-rounded performance in all aspects of comprehension. However, their local comprehension (extraction of details) was particularly enhanced after viewing a key-framed video with full captions. Notably, this gain in local comprehension was not as manifest after viewing animated video content with full captions. The qualitative data also revealed that although animation may provide a perceptually stimulating viewing experience, its transient feature most likely taxed the participants’ attention, thus impacting their comprehension outcomes. These findings underscore the benefit of a reduction in nonverbal input and the interplay between verbal and nonverbal input. The findings are discussed in relation to the use of verbal and nonverbal input for different pedagogical purposes.
Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.
To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.
We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.
We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.
These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.
Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy.
The resting functional Magnetic Resonance Imaging (fMRI) dataset comprised 220 patients with schizophrenia and 220 healthy controls. Brain-wise FCs was calculated for each participant and linear SVMs were developed for automatic classification of patients and controls. First, we evaluated the SVMs based on all participants and homogeneous subsamples of men, women, younger (18–30 years), and older (31–50 years) participants by 10-fold nested cross-validation. Then, we hold out a fixed test set of 40 participants (20 patients and 20 controls) and evaluated the SVMs based on incremental training sample sizes (N = 40, 80, …, 400).
We found that the SVMs based on all participants had accuracy of 85.05%. The SVMs based on male, female, young, and older participants yielded accuracy of 84.66, 81.56, 80.50, and 86.13%, respectively. Although the SVMs based on older subsamples had better performance than those based on all participants, they generalized poorly to younger participants (77.24%). For incremental training sizes, the classification accuracy increased stepwise from 72.6 to 83.3%, with >80% accuracy achieved with sample size >240.
The findings indicate that SVMs based on a large dataset yield high classification accuracy and establish models using a large sample size with heterogeneous properties are recommended for single subject prediction of schizophrenia.
While a significant amount of literature has been published on the theoretical and empirical basis of task-based language teaching (TBLT) as an educational framework for teaching second and foreign languages, few studies have described entire task-based programs. This chapter reports on a case study in which we describe the inception, design, implementation and evaluation of a task-based, Spanish foreign language program at Qingdao University in China. The program is the result of an international partnership between an American university and a Chinese university, whereby Chinese students receive a dual degree in Spanish as a foreign language. A detailed needs analysis was conducted and informed the design of the program, which includes the application process, tasks, and several community-based initiatives. We also report on how we do teacher-training and professional development collaboration, our challenges, and how we have worked to overcome those challenges. All in all, teacher and student satisfaction, student job placement, and community engagement indicate that the program is meeting students’ real-world needs for Spanish and is serving the Qingdao community. We conclude by discussing implications for implementing a fully task-based program in China, the nation’s first university-level TBLT program for Spanish foreign language learning and teaching.
Noncompressible torso hemorrhage (NCTH) is a major challenge in prehospital bleeding control and is associated with high mortality. This study was performed to estimate medical knowledge and the perceived barriers to information acquisition among health-care workers (HCWs) regarding NCTH in China.
A self-administered and validated questionnaire was distributed among 11 WeChat groups consisting of HCWs engaged in trauma, emergency, and disaster rescue.
A total of 575 HCWs participated in this study. In the knowledge section, the majority (87.1%) denied that successful hemostasis could be obtained by external compression. Regarding attitudes, the vast majority of HCWs exhibited positive attitudes toward the important role of NCTH in reducing prehospital preventable death (90.4%) and enthusiasm for continuous learning (99.7%). For practice, fewer than half of HCWs (45.7%) had heard of NCTH beforehand, only a minority (14.3%) confirmed they had attended relevant continuing education, and 16.3% HCWs had no access to updated medical information. The most predominant barrier to information acquisition was the lack of continuing training (79.8%).
Knowledge and practice deficiencies do exist among HCWs. Obstacles to update medical information warrant further attention. Furthermore, education program redesign is also needed.
Understanding factors associated with post-discharge sleep quality among COVID-19 survivors is important for intervention development.
This study investigated sleep quality and its correlates among COVID-19 patients 6 months after their most recent hospital discharge.
Healthcare providers at hospitals located in five different Chinese cities contacted adult COVID-19 patients discharged between 1 February and 30 March 2020. A total of 199 eligible patients provided verbal informed consent and completed the interview. Using score on the single-item Sleep Quality Scale as the dependent variable, multiple linear regression models were fitted.
Among all participants, 10.1% reported terrible or poor sleep quality, and 26.6% reported fair sleep quality, 26.1% reported worse sleep quality when comparing their current status with the time before COVID-19, and 33.7% were bothered by a sleeping disorder in the past 2 weeks. After adjusting for significant background characteristics, factors associated with sleep quality included witnessing the suffering (adjusted B = −1.15, 95% CI = −1.70, −0.33) or death (adjusted B = −1.55, 95% CI = −2.62, −0.49) of other COVID-19 patients during hospital stay, depressive symptoms (adjusted B = −0.26, 95% CI = −0.31, −0.20), anxiety symptoms (adjusted B = −0.25, 95% CI = −0.33, −0.17), post-traumatic stress disorders (adjusted B = −0.16, 95% CI = −0.22, −0.10) and social support (adjusted B = 0.07, 95% CI = 0.04, 0.10).
COVID-19 survivors reported poor sleep quality. Interventions and support services to improve sleep quality should be provided to COVID-19 survivors during their hospital stay and after hospital discharge.
Several Drosophila species (Diptera: Drosophilidae) have become serious economic pests of berry and soft-skinned stone fruits around the world. Prominent examples are Drosophila suzukii (Matsumura), D. melanogaster (Meigen), D. hydei (Sturtevant), and D. immigrans (Sturtevant). Information on the biology and ecology of Drosophila is important for a better understanding of these important fruit pests and, ultimately, for fruit protection. In this study, the gut bacteriomes of these four Drosophila species were surveyed and the differences among bacterial communities were characterised. The 16S rRNA genes of gut microbes were sequenced by Illumina MiSeq technology (Illumina, San Diego, California, United States of America), followed by α-diversity and β-diversity analyses. The results show that bacteria of the family Enterobacteriaceae (Kluyvera and Providencia; phylum Proteobacteria) dominated all four Drosophila species. Specific dominant gut bacterial communities were found in each Drosophila species. The dominant families in D. melanogaster and D. suzukii were Enterobacteriaceae, Comamonadaceae, and Acetobacteraceae. In the intestine of D. hydei, Enterobacteriaceae had a proportion of 56.99%, followed by Acetobacteraceae, Spiroplasmataceae, and Bacillales Incertae Sedis XII. In D. immigrans, besides Enterobacteriaceae, Alcaligenaceae, Flavobacteriaceae, Xanthomonadaceae, Comamonadaceae, and Sphingobacteriaceae also had high relative abundance. These data expand current knowledge about the putative function related to gut microbes – for example, the metabolism of carbohydrates, amino acids, inorganic ions, lipids, and secondary metabolites. This knowledge provides a basis for further metatranscriptomic and metaproteomic investigations.
Chronic inflammation exerts pleiotropic effects in the aetiology and progression of chronic obstructive pulmonary disease (COPD). Glucosamine is widely used in many countries and may have anti-inflammatory properties. We aimed to prospectively evaluate the association of regular glucosamine use with incident COPD risk and explore whether such association could be modified by smoking in the UK Biobank cohort, which recruited more than half a million participants aged 40–69 years from across the UK between 2006 and 2010. Cox proportional hazards models with adjustment for potential confounding factors were used to calculate hazard ratios (HR) as well as 95 % CI for the risk of incident COPD. During a median follow-up of 8·96 years (interquartile range 8·29–9·53 years), 9016 new-onset events of COPD were documented. We found that the regular use of glucosamine was associated with a significantly lower risk of incident COPD with multivariable adjusted HR of 0·80 (95 % CI, 0·75, 0·85; P < 0·001). When subgroup analyses were performed by smoking status, the adjusted HR for the association of regular glucosamine use with incident COPD were 0·84 (0·73, 0·96), 0·84 (0·77, 0·92) and 0·71 (0·62, 0·80) among never smokers, former smokers and current smokers, respectively. No significant interaction was observed between glucosamine use and smoking status (Pfor interaction = 0·078). Incident COPD could be reduced by 14 % to 84 % through a combination of regular glucosamine use and smoking cessation.