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This research examines the unique Chinese approaches to implementing the Early Childhood Curriculum (ECC) in Shenzhen and Hong Kong, drawing on School-based Curriculum Development (SBCD) studies. A total of 200 administrators and teachers were interviewed in total, and transcripts from those interviews were examined, cross-checked, and assessed using document analysis and classroom observation. Through interviews that have been conducted by administrators and teachers analyzed by document analysis and classroom observation, the influence of Chinese culture on ECC implementation is explored using the Cultural-Historical Activity Theory (CHAT). An exploratory, inferential, and descriptive statistical approach evaluates the sociocultural mechanism of ECC in Chinese society. The proposed framework utilizes K-Nearest Neighbor (KNN) regression analysis to illustrate how social development leads to cultural fusion and conflicts. The overall sociocultural framework promotes cultural growth and inheritance in China's early childhood education settings., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Fine-grained soils present engineering challenges. Stabilization with marble powder has shown promise for improving engineering properties. Understanding the temporal evolution of Unconfined Compressive Strength (UCS) and geotechnical properties in stabilized soils could aid strength assessment. This study investigates the stabilization of fine-grained clayey soils using waste marble powder as an alternative binder. Laboratory experiments were conducted to evaluate the geotechnical properties of soil-marble powder mixtures, including Atterberg's limits, compaction characteristics, California Bearing Ratio (CBR), Indirect Tensile Strength (ITS), and Unconfined Compressive Strength (UCS). The effects of various factors, such as curing time, molding water content, and composition ratios, on UCS, were analyzed using Exploratory Data Analysis (EDA) techniques, including histograms, box plots, and statistical modeling. The results show that the CBR increased from 10.43 to 22.94% for unsoaked and 4.68 to 12.46% for soaked conditions with 60% marble powder, ITS rose from 100 to 208 kN/m 2 with 60-75% marble powder, and UCS rose from 170 to 661 kN/m 2 after 28 days of curing, molding water content (optimum at 22.5%), and composition ratios (optimum at 60% marble powder). Complex modeling yielded R 2 (0.954) and RMSE (29.82 kN/m 2 ) between predicted and experimental values. This study demonstrates the potential of utilizing waste marble powder as a sustainable and cost-effective binder for soil stabilization, transforming weak soils into viable construction materials.

Background: Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation., Results: We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies an adaptive threshold based on the median absolute deviation on four QC metrics (gene and UMI complexity, fraction of reads mapping to mitochondrial and ribosomal genes). ddqc retains over a third more cells when compared to conventional data-agnostic QC filters. Finally, we show that ddqc recovers biologically meaningful trends in gradation of gene complexity among cell types that can help answer questions of biological interest such as which cell types express the least and most number of transcripts overall, and ribosomal transcripts specifically., Conclusions: ddqc retains cell types such as metabolically active parenchymal cells and specialized cells such as neutrophils which are often lost by conventional QC. Taken together, our work proposes a revised paradigm to quality filtering best practices-iterative QC, providing a data-driven QC framework compatible with observed biological diversity., (© 2022. The Author(s).)

Whole genome sequencing has rapidly progressed in recent years, with sequencing the SARS-CoV-2 genomes, making it a more reliable clinical tool for public health surveillance. This development has resulted in the production of a large amount of genomic data used for various types of genomic exploration. However, without a proper standard protocol, the usage of genomic data for analyzing various biological phenomena, such as mutation and evolution, may result in a propagating risk of using an unvalidated data set. This process could lead to irregular data being generated along with a high risk of altered analysis. Thus, the current study lays out the foundation for a preprocess pipeline using data analysis to analyze the genomic data set for its accuracy. We have used the recent example of SARS-CoV-2 to demonstrate the process overflow that can be utilized for various kinds of biological exploration such as understanding mutational events, evolutionary divergence, and speciation. Our analysis reveals a significant amount of sequence divergence in the gamma variant as compared with the reference genome thereby making the variant less infective and deadly. Moreover, we found regions in the genomic sequence that is more prone to mutational localization thereby altering the structural integrity of the virus resulting in a more reliable molecular viral mechanism. We believe that the current work will help for an initial check of the genomic data followed by the biological assessment of the process overflow which will be beneficial for the variant analysis and mutational uprising., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Objective: To evaluate the healthcare resources in a tertiary center related to exclusive use of non-enzyme inducing anti-seizure medications relative to concomitant use of enzyme-inducing anti-seizure medications in patients with refractory epilepsy., Methods: In this retrospective case-time-control study, we compared the effects of two anti-seizure medication strategies: exclusively non-inducing anti-seizure medications (NIND) or a combination of NIND and inducing anti-seizure medications (IND+). The primary outcome parameter was the number of consultations with relevant healthcare professionals in our tertiary center, assessed with a negative binomial regression model, adjusting for several covariates like blood drug level and time interval (TI). Results from statistical models were visualized to explore the contribution of all covariates on the outcome in the total population and in subgroups., Results: From the 21538 patients with refractory epilepsy referred to our center 1648 patients met the inclusion criteria. The regression model showed that the IND + strategy was significantly associated with fewer consultations compared to the NIND strategy (p < 0.001), reflected in an incidence risk ratio (IRR) of 0.844 (0.799-0.890). Visualization of subgroups, defined by anti-seizure medications strategy, revealed patterns in contribution of blood drug level measurements on the outcome. Although sex was not included as a covariate in the regression model, as it was eliminated by the backward-elimination approach, visualization of this subgroup showed differences in effects of blood drug level and TI., Conclusion: For patients with refractory epilepsy in our tertiary center, treatment following the IND + strategy is associated with fewer consultations with healthcare professionals compared to the NIND strategy. Comprehensive visualization of the results facilitated the exploration of effects of covariates across subgroups., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

The current researchers all over the world are striving to facilitate the answer to COVID-19 in a unique commitment of scientific collaborations, and with Cognitive technologies, highly flexible learning processes are needed to maintain the transmission of knowledge, prototype and code, by integrating application areas to specific culture and cross-border cooperation. Experts in artificial intelligence and machine learning technology were tracked and predicted with real-time data that was created worldwide by this pandemic situation and distributed COVID-19 patient information timely. Considered physiological features followed by clinical tests of patients with COVID-19 with very simple access to subsequent data transformation was relevant, but complicated. This paper works on in-depth Exploratory Data Analysis (EDA) prediction analysis over global database of COVID-19 medical data from around the world will be available benefiting future artificial predictive, analytical and biomedical research additional COVID-19 approaches including associated pandemics., (© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.)

Background: With the enactment of the Brazilian Law Arouca 11,794/2008 and Decree 6.899/2009, there has been an urgent need for changes in the processes related to animal experimentation in Brazil; in particular, there is a need for improvements in enhancements of the lab animal management. To improve the management capacity of the Lab animal facility of the Carlos Chagas Institute's Laboratory Animals Science (LAS), BioterC software was developed and implemented in 2014 for tracking mouse laboratory colonies. Five years after the implementation of this software, we sought to analyze the information in the database originated from BioterC using the Exploratory Analysis Data methodology (EDA). This article aims to identify animal breeding patterns using a data mining tool (Data Science) with Python programming language., Results: The results show that from September 2014 to June 2019, under the license IACUC number LW- 6/17, 15.106 animals were produced. The C57BL/6, BALB/c and Swiss strains were the most frequently produced strains. The distribution of births due to crosses between these strains showed a median of 6 to 10 animals, depending on the genetic homozygosis and heterozygosis of the animal. The median number of days of mating was 35 days. In the sexing period, the records reported a median of 19 days. A total of 393 requests for animals from internal and external laboratories were registered. It was noted that approximately half of the animals produced to meet the demand for orders were discarded. Of the 15,106 animals, 38% were requested for animal experimentation, 58% were discarded and 4% did not have an outcome recorded in the data., Conclusions: This volume of data provides an initial view of the information retrieval capabilities contained in BioterC, allowing for unique breeding knowledge by installing laboratory animals.