What Content PreK-8 Prospective Teachers Should Know and Why: Topic Recommendations for Content Courses

There is a long history of mathematics educators trying to answer the questions of what mathematics is essential to learn and why, including the mathematical education of prospective teachers. Determining the answer to these questions and conveying that answer convincingly are not always easy tasks for new mathematics teacher educators (MTEs). As an MTE’s career progresses (or in some cases as novice MTEs), they must also decide how much content is possible to cover in the context of one, two, or three semesters. That is, how deep is deep enough, and how much breadth is feasible? And how does an MTE make these decisions? To address these questions, we provide potential answers from three perspectives: policy, research, and textbook analysis. We begin with a brief summary of three policy documents: The Mathematical Education of Teachers II (MET-II) (CBMS, 2012), Standards for Preparing Teachers of Mathematics (SPTM) (AMTE, 2017), and the Statistical Education of Teachers (SET) (ASA, 2016). Drawing on these policy documents, a textbook analysis, the Common Core State Standards for Mathematics (CCSSM) (National Governors Association, 2010), and relevant research, we provide insights into how MTEs might choose topics for content courses, with the aim of aligning their teaching objectives with answers to why more math matters

Inversion of Different Cultivated Soil Types’ Salinity Using Hyperspectral Data and Machine Learning

Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson’s correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK400 nm was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R2 = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing

Forecasting smog in Beijing using a novel time-lag GM (1, N) model based on interval grey number sequences

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction. Design/methodology/approach This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness. Findings In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies. Practical implications The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective. Originality/value Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model

Myofibril-Inducing RNA (MIR) is essential for tropomyosin expression and myofibrillogenesis in axolotl hearts

The Mexican axolotl, Ambystoma mexicanum, carries the naturally-occurring recessive mutant gene 'c' that results in a failure of homozygous (c/c) embryos to form hearts that beat because of an absence of organized myofibrils. Our previous studies have shown that a noncoding RNA, Myofibril-Inducing RNA (MIR), is capable of promoting myofibrillogenesis and heart beating in the mutant (c/c) axolotls. The present study demonstrates that the MIR gene is essential for tropomyosin (TM) expression in axolotl hearts during development. Gene expression studies show that mRNA expression of various tropomyosin isoforms in untreated mutant hearts and in normal hearts knocked down with double-stranded MIR (dsMIR) are similar to untreated normal. However, at the protein level, selected tropomyosin isoforms are significantly reduced in mutant and dsMIR treated normal hearts. These results suggest that MIR is involved in controlling the translation or post-translation of various TM isoforms and subsequently of regulating cardiac contractility

Diagnostic significance of noncoding RNAs in kawasaki disease: A systematic review and meta-analysis

ObjectiveKawasaki disease (KD) is a systemic vasculitis disease, and early effective intervention would reduce the occurrence of coronary artery lesions (CALs). Recently, many scholars have been committed to studying the relationship between noncoding RNAs and KD. This systematic review aimed to analyze the diagnostic value of noncoding RNAs(ncRNAs) in distinguishing different KD status.MethodsWe searched for the literature about diagnostic values of ncRNAs in KD in CNKI, VIP, Wanfang, China Biomedical Literature Database as well as PubMed, Web of Science, Embase, and Cochrane Library up to April 15, 2022. All included studies were further analyzed using STATA 12.0, Meta-disc 1.4 and RevMan 5.4 software.ResultsA total of six studies investigating the diagnostic performance of ncRNAs in differentiating KD-CAL (n = 101) from KD-NCAL patients (n = 123) were included in this this meta-analysis. The calculated area under the curve(AUC) was 0.83 (0.80–0.86). Four studies on the diagnostic performance of ncRNAs in differentiating acute KD patients (n = 139) from convalescent KD patients (n = 109) were included. The calculated AUC was 0.87 (0.84–0.90). Four studies focused on the diagnostic performance of ncRNAs combined with other laboratory indexes in KD by assessing 137 KD patients and 152 febrile controls. The calculated AUC was 0.90 (0.87–0.92). Four studies assessed the diagnostic performance of ncRNAs in differentiating intravenous immunoglobulin (IVIG)-resistant KD patients from IVIG-responsive KD patients. The calculated AUC was 0.9135 ± 0.0307. These results indicated that ncRNAs have a good diagnostic efficacy in KD.ConclusionsThis meta-analysis showed that ncRNAs have potential as a biomarker for distinguishing different KD status. However, since limited studies were included in this meta-analysis, larger and well-designed diagnostic studies should be conducted to validate these results.Systematic Review RegistrationINPLASY.COM, identifier: doi: 10.37766/inplasy2022.10.0035

The cellular source for APOBEC3G's incorporation into HIV-1

<p>Abstract</p> <p>Background</p> <p>Human APOBEC3G (hA3G) has been identified as a cellular inhibitor of HIV-1 infectivity. Viral incorporation of hA3G is an essential step for its antiviral activity. Although the mechanism underlying hA3G virion encapsidation has been investigated extensively, the cellular source of viral hA3G remains unclear.</p> <p>Results</p> <p>Previous studies have shown that hA3G forms low-molecular-mass (LMM) and high-molecular-mass (HMM) complexes. Our work herein provides evidence that the majority of newly-synthesized hA3G interacts with membrane lipid raft domains to form Lipid raft-associated hA3G (RA hA3G), which serve as the precursor of the mature HMM hA3G complex, while a minority of newly-synthesized hA3G remains in the cytoplasm as a soluble LMM form. The distribution of hA3G among the soluble LMM form, the RA LMM form and the mature forms of HMM is regulated by a mechanism involving the N-terminal part of the linker region and the C-terminus of hA3G. Mutagenesis studies reveal a direct correlation between the ability of hA3G to form the RA LMM complex and its viral incorporation.</p> <p>Conclusions</p> <p>Together these data suggest that the Lipid raft-associated LMM A3G complex functions as the cellular source of viral hA3G.</p

Macrophage deletion of Noc4l triggers endosomal TLR4/TRIF signal and leads to insulin resistance

In obesity, macrophages drive a low-grade systemic inflammation (LSI) and insulin resistance (IR). The ribosome biosynthesis protein NOC4 (NOC4) mediates 40 S ribosomal subunits synthesis in yeast. Hereby, we reported an unexpected location and function of NOC4L, which was preferentially expressed in human and mouse macrophages. NOC4L was decreased in both obese human and mice. The macrophage-specific deletion of Noc4l in mice displayed IR and LSI. Conversely, Noc4l overexpression by lentivirus treatment and transgenic mouse model improved glucose metabolism in mice. Importantly, we found that Noc4l can interact with TLR4 to inhibit its endocytosis and block the TRIF pathway, thereafter ameliorated LSI and IR in mice.Macrophage inflammation promotes insulin resistance during diet-induced obesity. Here the authors show that macrophage NOC4L is decreased in humans and mice with obesity, that macrophage NOC4L deficiency aggravated high-fat diet induced inflammation and insulin resistance, and that NOC4L interacts with toll-like receptor 4, to inhibit endocytosis, and thus blocks TLF4/TRIF inflammatory signaling