The influence of cognitive ability in Chinese reading comprehension: can working memory updating change Chinese primary school students’ reading comprehension performance?

With the development of educational cognitive neuroscience, language instruction is no longer perceived as mechanical teaching and learning. Individual cognitive proficiency has been found to play a crucial role in language acquisition, particularly in the realm of reading comprehension. The primary objective of this study was to investigate two key aspects: firstly, to assess the predictive effects of the central executive (CE) on the Chinese reading comprehension scores of Chinese primary school students, and secondly, to explore the influence of CE training on the Chinese reading comprehension performance of Chinese primary school students. Chinese primary school students were recruited as participants. Experiment 1 used a Chinese N-back task, a Chinese Stroop task, and a number-pinyin conversion task to investigate the predictive effect of the CE components on Chinese reading comprehension. Experiment 2, based on the results of Experiment 1, used the Chinese character N-back training to explore the influence of updating training on Chinese reading comprehension. The findings from Experiment 1 underscored that CE had a predictive effect on Chinese reading comprehension scores. And updating had a prominent role in it. Experiment 2 revealed that the experimental group exhibited an enhancement in their updating performance following N-back training. Although the reading comprehension performance of the two groups after training did not produce significant differences in total scores, the experimental group showed maintained and higher microscopic reading comprehension scores than the control group in the more difficult post-test. In summary, this study yields two primary conclusions: (1) CE was able to predict Chinese reading comprehension scores. Updating has an important role in prediction. (2) Updating training enhances students’ updating performance and positively influences students’ Chinese microscopic reading comprehension performance

An ancestral NB-LRR with duplicated 3'UTRs confers stripe rust resistance in wheat and barley.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a global threat to wheat production. Aegilops tauschii, one of the wheat progenitors, carries the YrAS2388 locus for resistance to Pst on chromosome 4DS. We reveal that YrAS2388 encodes a typical nucleotide oligomerization domain-like receptor (NLR). The Pst-resistant allele YrAS2388R has duplicated 3' untranslated regions and is characterized by alternative splicing in the nucleotide-binding domain. Mutation of the YrAS2388R allele disrupts its resistance to Pst in synthetic hexaploid wheat; transgenic plants with YrAS2388R show resistance to eleven Pst races in common wheat and one race of P. striiformis f. sp. hordei in barley. The YrAS2388R allele occurs only in Ae. tauschii and the Ae. tauschii-derived synthetic wheat; it is absent in 100% (n = 461) of common wheat lines tested. The cloning of YrAS2388R will facilitate breeding for stripe rust resistance in wheat and other Triticeae species

Automatic Detection, Validation and Repair of Race Conditions in Interrupt-Driven Embedded Software

Interrupt-driven programs are widely deployed in safety-critical embedded systems to perform hardware and resource dependent data operation tasks. The frequent use of interrupts in these systems can cause race conditions to occur due to interactions between application tasks and interrupt handlers (or two interrupt handlers). Numerous program analysis and testing techniques have been proposed to detect races in multithreaded programs. Little work, however, has addressed race condition problems related to hardware interrupts. In this paper, we present SDRacer, an automated framework that can detect, validate and repair race conditions in interrupt-driven embedded software. It uses a combination of static analysis and symbolic execution to generate input data for exercising the potential races. It then employs virtual platforms to dynamically validate these races by forcing the interrupts to occur at the potential racing points. Finally, it provides repair candidates to eliminate the detected races. We evaluate SDRacer on nine real-world embedded programs written in C language. The results show that SDRacer can precisely detect and successfully fix race conditions.Comment: This is a draft version of the published paper. Ke Wang provides suggestions for improving the paper and README of the GitHub rep

Targeted Detection of 76 Carnitine Indicators Combined with a Machine Learning Algorithm Based on HPLC-MS/MS in the Diagnosis of Rheumatoid Arthritis.

BACKGROUND/OBJECTIVES: Early diagnosis and treatment of rheumatoid arthritis (RA) are essential to reducing disability. However, the diagnostic criteria remain unclear, relying on clinical symptoms and blood markers. METHODS: Using high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) targeted detection, we evaluated 76 carnitine indicators (55 carnitines and 21 corresponding ratios) in the serum of patients with RA to investigate the role of carnitine in RA. A total of 359 patients (207 patients with RA and 152 healthy controls) were included in the study. Screening involved three methods and integrated 76 carnitine indicators and 128 clinical indicators to identify candidate markers to establish a theoretical basis for RA diagnosis and new therapeutic targets. The diagnostic model derived from the screened markers was validated using three machine learning algorithms. RESULTS: The model was refined using eight candidate indicators (C0, C10:1, LYMPH, platelet distribution width, anti-keratin antibody, glucose, urobilinogen, and erythrocyte sedimentation rate (ESR)). The receiver operating characteristic curve, sensitivity, specificity, and accuracy of the V8 model obtained from the training set were >0.948, 79.46%, 92.99%, and 89.18%, whereas those of the test set were >0.925, 78.89%, 89.22%, and 85.87%, respectively. Twenty-four carnitines were identified as risk factors of RA, with three significantly correlating with ESR, four with anti-cyclic citrullinated peptide antibody activity, two with C-reactive protein, five with immunoglobulin-G, eight with immunoglobulin-A levels, and eleven with immunoglobulin-M levels. CONCLUSIONS: Carnitine is integral in the progression of RA. The diagnostic model developed shows excellent diagnostic capacity, improving early detection and enabling timely intervention to minimize disability associated with RA

Application of Eight Machine Learning Algorithms in the Establishment of Infertility and Pregnancy Diagnostic Models: A Comprehensive Analysis of Amino Acid and Carnitine Metabolism.

To explore the effects of altered amino acids (AAs) and the carnitine metabolism in non-pregnant women with infertility (NPWI), pregnant women without infertility (PWI) and infertility-treated pregnant women (ITPW) compared with non-pregnant women (NPW, control), and develop more efficient models for the diagnosis of infertility and pregnancy, 496 samples were evaluated for levels of 21 AAs and 55 carnitines using targeted high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Three methods were used to screen the biomarkers for modeling, with eight algorithms used to build and validate the model. The ROC, sensitivity, specificity, and accuracy of the infertility diagnosis training model were higher than 0.956, 82.89, 66.64, and 82.57%, respectively, whereas those of the validated model were higher than 0.896, 77.67, 69.72, and 83.38%, respectively. The ROC, sensitivity, specificity, and accuracy of the pregnancy diagnosis training model were >0.994, 96.23, 97.79, and 97.69%, respectively, whereas those of the validated model were >0.572, 96.39, 93.03, and 94.71%, respectively. Our findings indicate that pregnancy may alter the AA and carnitine metabolism in women with infertility to match the internal environment of PWI. The developed model demonstrated good performance and high sensitivity for facilitating infertility and pregnancy diagnosis

Flammulina velutipes mycorrhizae dietary fiber attenuates the development of obesity via regulating lipid metabolism in high-fat diet-induced obese mice

IntroductionMounting evidence has shown that Flammulina velutipes mycorrhizae dietary fiber (Fv-DF) has the potential to significantly improve health outcomes by addressing lipid metabolic disorders. However, the mechanism underlying Fv-DF in regulating liver lipid metabolism of high-fat diet (HFD)-induced obese mice still merits to be systematically elaborated.MethodsHerein, we conducted a comprehensive study utilizing HFD-induced C57BL/6J mice as an obesity model to investigate the impact of Fv-DF on liver lipid accumulation.ResultsThe study, which included an evaluation of Fv-DF on a high-fat diet (HFD)-induced obese mice, revealed that Fv-DF supplementation can effectively decrease weight gain, improve serum lipid levels, and reduce fat deposition in adipose tissues. The estimation of Fv-DF on liver tissues demonstrated that Fv-DF supplementation significantly ameliorated lipid metabolism and hepatic injury in HFD-induced obese mice. Furthermore, Fv-DF improved lipid metabolism in obese mice by modifying the abundance and related pathways of TG, PC, PE, and other lipid metabolites. Mechanistically, Fv-DF supplementation significantly suppressed the expression of lipid synthesis-related genes while promoting lipid oxidation-related genes.DiscussionCollectively, the findings could inspire significant implications for Fv-DF in developing novel treatments for obesity-related metabolic disorders management

Impact of mean arterial pressure on reproductive endocrine characteristics in infertile patients with polycystic ovary syndrome: a secondary analysis of a randomized clinical trial

ObjectiveThis study aims to evaluate the association between mean arterial pressure (MAP) and anthropometric, metabolic, and endocrine parameters in Chinese infertile women with polycystic ovary syndrome (PCOS).MethodsA total of 1,000 PCOS subjects were enrolled in the clinical trial project of Acupuncture and Clomiphene in the treatment of PCOS infertility patients (PCOSAct). Of these, 998 patients were selected for this study. Linear trends and regression analyses were conducted to evaluate the association between MAP and anthropometric, metabolic, and endocrine parameters. Logistic regression was employed to estimate the association between MAP and risk of insulin resistance (IR), nonalcoholic fatty liver disease (NAFLD) and hyperlipidemia. The receiver operating characteristics (ROC) curve was used to determine the predictive value of the MAP for IR, NAFLD and hyperlipidemia.ResultsLinear trends revealed that the MAP was positively associated with age, height, body weight, body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), systolic blood pressure (SBP) and diastolic blood pressure (DBP), hirsutism score, and acanthosis nigricans score, fasting blood glucose (FBG), fasting insulin (FINS), the homeostatic model assessment for insulin resistance (HOMA-IR), low-density lipoprotein (LDL), triglycerides (TG), total cholesterol (TC), apolipoprotein B (ApoB), ApoB/apolipoprotein A1 (ApoA1) ratio, total testosterone (TT), and free androgen index (FAI), as well as the prevalence of IR, metabolic syndrome (MetS), NAFLD, and hyperlipidemia. Conversely, MAP was negatively correlated with the quantitative insulin sensitivity check index (QUICKI), high-density lipoprotein (HDL), sex hormone-binding globulin (SHBG), luteinizing hormone (LH), the LH/follicle stimulating hormone (FSH) ratio, and anti-Müllerian hormone (AMH). After adjusting for age and BMI, a significant linear relationship was observed between MAP and WC, WHR, hirsutism score, FBG, LDL, TG, TC, ApoB, and ApoB/ApoA1 ratio. Logistic regression analysis demonstrated that participants in the highest quartile (Q4) of MAP had no significantly higher odds ratios (OR) for IR, NAFLD and hyperlipidemia after adjusting for confounding factors. The ROC curve analysis indicated that the AUCIR was 0.593 (95%CI: 0.557 ~ 0.629), with 85.9% sensitivity and 28.8% specificity at a cut-off value of 82.83, and the AUCNAFLD was 0.621 (95%CI: 0.554 ~ 0.687), with 69.4% sensitivity and 53.5% specificity at a cut-off value of 87.17, and the AUChyperlipidemia was 0.555 (95% CI: 0.518 ~ 0.592), with 39.5% sensitivity and 70.00% specificity at a cut-off value of 90.83.ConclusionElevated MAP is associated with dysregulation of glucose and lipid metabolism and alterations in endocrine hormone levels. It may thus serve as a promising screening approach for IR-related conditions in patients with PCOS

Expression Patterns of Genes Involved in Sugar Metabolism and Accumulation during Apple Fruit Development

Both sorbitol and sucrose are imported into apple fruit from leaves. The metabolism of sorbitol and sucrose fuels fruit growth and development, and accumulation of sugars in fruit is central to the edible quality of apple. However, our understanding of the mechanisms controlling sugar metabolism and accumulation in apple remains quite limited. We identified members of various gene families encoding key enzymes or transporters involved in sugar metabolism and accumulation in apple fruit using homology searches and comparison of their expression patterns in different tissues, and analyzed the relationship of their transcripts with enzyme activities and sugar accumulation during fruit development. At the early stage of fruit development, the transcript levels of sorbitol dehydrogenase, cell wall invertase, neutral invertase, sucrose synthase, fructokinase and hexokinase are high, and the resulting high enzyme activities are responsible for the rapid utilization of the imported sorbitol and sucrose for fruit growth, with low levels of sugar accumulation. As the fruit continues to grow due to cell expansion, the transcript levels and activities of these enzymes are down-regulated, with concomitant accumulation of fructose and elevated transcript levels of tonoplast monosaccharide transporters (TMTs), MdTMT1 and MdTMT2; the excess carbon is converted into starch. At the late stage of fruit development, sucrose accumulation is enhanced, consistent with the elevated expression of sucrose-phosphate synthase (SPS), MdSPS5 and MdSPS6, and an increase in its total activity. Our data indicate that sugar metabolism and accumulation in apple fruit is developmentally regulated. This represents a comprehensive analysis of the genes involved in sugar metabolism and accumulation in apple, which will serve as a platform for further studies on the functions of these genes and subsequent manipulation of sugar metabolism and fruit quality traits related to carbohydrates