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Research Findings: The present study analyzes whether child factors are linked to variations in teacher cognitive activation (CA) among individual children in math-related activities within Chinese kindergarten classrooms. The findings show that teachers tend to provide direct CA with high concreteness and that child engagement with tasks is significantly positively associated with the ratio of teacher CA to individual children. Moreover, a child's math ability is significantly negatively associated with both the ratio and overall contingency degree of CA that a child experiences. Practice or Policy: These results suggest that teachers should improve their knowledge and competence to adapt their teaching behaviors to child characteristics to provide children with high-quality and equitable math learning opportunities and that children should be taught to highly engage to obtain more proactive reactions from their learning environments.

BackgroundThe objective of this study is to describe the use of targeted therapies for the treatment of advanced renal cell carcinoma (RCC) and overall survival (OS) among patients in clinical practice in the Veterans Health Administration (VHA).MethodsA retrospective cohort of 286 patients from 24 VHA Medical Centers diagnosed with advanced clear cell RCC between Fiscal Year (FY) 2010 and FY2014 was followed through September 30, 2016. Among patients who received targeted therapy, we described the medications taken, duration of therapy, and overall survival. We also assessed the effect of the first therapy received on overall survival using Cox Proportional Hazards models.ResultsThere were 66 patients who did not receive therapy for their advanced RCC. Of the 220 treated patients, the mean (sd) number of medications received was 1.9 (1.1). The medications most commonly used first were sunitinib (61.8%), pazopanib (17.3%), and temsirolimus (10.9%). The median duration of first-line therapy was 86 days (interquartile range [IQR] 42, 210). Median total duration of therapy was 159 days (IQR 58, 397). 62.3% of patients had ≥ 1 dose of therapy held or reduced, mainly due to an adverse drug event (ADE). Median survival from the start of treatment to death was 1.08 years (IQR 0.80, 1.31). Finally, receipt of temsirolimus vs sunitinib (HR 1.95 [95%CI 1.09,3.47]) as the first targeted therapy was independently associated with an increased hazard of death.ConclusionOur analysis of targeted therapies for advanced RCC in VHA suggests duration of treatment is shorter in a real-world setting than in clinical trials, and dose reductions and ADEs are more common.

The existing mine ventilator rolling bearing fault diagnosis method only extracts time-frequency component characteristics and adopts shallow network structure, thus causing low fault diagnosis accuracy. In order to solve this problem, a fault diagnosis method of rolling bearing of mine ventilator based on multi-domain characteristic fusion and deep belief network (DBN) is proposed. Firstly, the method performs wavelet packet noise reduction on the bearing vibration signal, and extracts time domain characteristics, frequency domain characteristics and IMF energy characteristics from the bearing vibration signal after noise reduction to obtain a relatively comprehensive set of high-dimensional characteristic set. Secondly, the characteristic selection method based on intra-class and inter-class standard deviation is used to eliminate the characteristics that are not effective for classification and characteristics with no obvious effect so as to filter out high-efficiency characteristics. Finally, kernel principal component analysis (KPCA) is used to reduce and fuse the high-dimensional screening characteristics, eliminate the redundancy between characteristics, and input the fused characteristics into DBN to complete the fault diagnosis. The experimental results show that compared with the diagnosis method based on single characteristic and shallow network, the average accuracy of mine ventilator rolling bearing fault diagnosis method based on multi-domain characteristic fusion and DBN has average accuracy and less average diagnosis time showing good stability and generalization ability for different damage fault data.

Hydrogen peroxide (H2O2) is closely associated with various diseases and is commonly used as a target for disease diagnosis and treatment, making research on H2O2 detection methods crucial. Compared to currently used single-mode detection methods, dual-mode detection offers self-validation and self-correction capabilities, which help to provide more accurate detection results. Herein, an electrochemical and colorimetric dual-mode sensing platform was developed to detect H2O2 based on copper/zirconium metal–organic framework (Cu/Zr-MOF) nanozymes with high peroxidase-like activity. Cu/Zr-MOF catalyzes the reduction of H2O2 to generate ·OH, which oxidizes colorless 3,3′,5,5′-tetramethylbenzidine (TMB) into a blue product (oxTMB), and the color change can be recognized and measured using a smartphone application. Furthermore, Cu/Zr-MOF exhibits excellent electrocatalytic reduction activity toward H2O2. Electrochemistry demonstrated higher sensitivity with a detection limit as low as 21.3 nM, compared to colorimetry (0.11 μM). The effective combination of the two detection modes allows the sensing platform to exhibit high accuracy, selectivity, and reliability. The dual-mode sensing platform opens a pathway for the accurate and sensitive detection of intracellular and extracellular H2O2 and has a broad spectrum of applications in pathological research and disease diagnosis. [ABSTRACT FROM AUTHOR]