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Collaborative reflection refers to the process by which members of a community reflect through social interaction, as well as to the outcomes of this process. In this study, we adopted a sociocultural perspective to examine the obstacles a group of Chinese kindergarten teachers faced with collaborative reflection in collectivist cultural contexts. We tried to overcome these constraints by introducing three supportive methods—(a) the Minute Paper Method; (b) the Six Thinking Hats Method; and (c) the 360° Reflection Method. Sixteen teachers working at a kindergarten in Shanghai participated in this 1-year action research study. Research shows that collectivism brings many benefits to collaborative teacher reflection, however, it also leads to some problems, such as: the neglect of individual interests, centralization, undemocratic reflective atmosphere, and inefficiency. The three methods cited above successfully helped teachers overcome these constraints and promoted effective collaborative reflection, especially in terms of broadening perspectives, facilitating critical thinking, and creating a more democratic, equitable reflective atmosphere. The study frames collaborative reflection as both a metacognitive mechanism and a social practice. It suggests more attention needs to be paid to the importance of teacher emotions, human relationships, and culture in understanding and promoting collaborative teacher reflection. It also suggests that more innovative approaches and methods should be implemented to help the teaching community generate the dynamics of change from the inside out.

This paper addresses the flexible job-shop scheduling problem with multiple time factors—namely, transportation time and setup time—as well as job priorities (referred to as FJSP-JPC-TST). An optimization model is established with the objective of minimizing the completion time. Considering the characteristics of the FJSP-JPC-TST, we propose an improved whale optimization algorithm that incorporates multiple strategies. First, a two-layer encoding mechanism based on operations and machines is introduced. To prevent illegal solutions, a priority-based encoding repair mechanism is designed, along with an active scheduling decoding method that fully considers multiple time factors and job priorities. Subsequently, a multi-level sub-population optimization strategy, an adaptive inertia weight, and a cross-population differential evolution strategy are implemented to enhance the optimization efficiency of the algorithm. Finally, extensive simulation experiments demonstrate that the proposed algorithm offers significant advantages and exhibits high reliability in effectively solving such scheduling problems.

Quality inspection is a crucial step in ensuring product conformity and avoiding rework waste, while job priority constraints are prevalent in the production of complex products with assembly structures. This paper presents a modeling and solution framework for the multi-objective flexible job shop scheduling problem that incorporates both quality inspection activities and job priority constraints. An optimization model is constructed with the objectives of minimizing the makespan, minimizing the total energy consumption, and maximizing the processing quality. To solve this model, an improved multi-objective evolutionary algorithm based on decomposition is developed, which integrates several well-established mechanisms into a unified framework. The algorithm integrates multi-product assembly structures via virtual nodes, employs a two-vector encoding scheme, and incorporates a product—group repair mechanism based on binary sorting tree to handle job priority constraints. To maintain diversity among non-dominated solutions, a niching-based elite archive strategy is adopted. Furthermore, a quality enhancement strategy and a memory vector-based local search mechanism are embedded to strengthen the algorithm’s search capability. Simulation results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of both convergence and diversity.

Diabetes Mellitus can cause dental pulp cells apoptosis by oxidative stress, and affect the integrity and function of dental pulp tissue. Mitochondria are the main attack targets of oxidative stress and have a critical role in apoptosis. However, whether mitochondria are involved in dental pulp damage caused by diabetes mellitus remains unclear. This study aimed to investigate the role of mitochondria in the apoptosis of odontoblast-like cell line (mDPC6T) induced by glucose oxidative stress, and to explore its possible mechanism. We established an oxidative stress model in vitro using glucose oxidase/glucose to simulate the pathological state under diabetic conditions. We found that the opening of mitochondrial permeability transition pore (mPTP) contributed to the apoptosis of mDPC6T treated with glucose oxidase, as evidenced by enhanced mitochondrial reactive oxygen species (mtROS) and intracellular Ca 2+ disorder, significantly reduced mitochondrial membrane potential (MMP) and ATP production. Antioxidant N-acetylcysteine (NAC) or Cyclosporine A (mPTP inhibitor) blocked the mPTP opening, which significantly attenuated mitochondrial dysfunction and apoptosis induced by glucose oxidative stress. In addition, we found that glucose oxidative stress stimulated mPTP opening may through inhibition of Akt-GSK3β pathway. This study provides a new insight into the mitochondrial mechanism underlying diabetes-associated odontoblast-like cell apoptosis, laying a foundation for the prevention and treatment of diabetes-associated pulp injury.

A healthy fund flow system is crucial for the successful construction of any project. Project fund flow management has made significant progress, increasingly aligning with real-world applications. However, due to the uncertainties associated with Engineering Changes (ECs) in projects, the actual fund flow may still deviate from expectations. These systems still require improvements and corrections of flaws to enhance the efficiency of construction projects and reduce exposure to risks associated with ECs. Construction projects are complex and involve many processes. Each process represents a specific part of the project; therefore, an EC in one area can impact resource scheduling and fund balance. In our analysis, we found that ECs are directly related to fund demands and may result in the need for more materials, labor, and duration. Furthermore, ECs can alter construction progress and payment schedules, exacerbating project risks. As a result, effective management of fund flexibility becomes highly necessary. To explore the impact of ECs on the value dynamics of fund flow, it is important to understand and describe the stochastic paths of fund flow and discern the dynamic changes at each stage. Given this, we introduced a system dynamics model based on the Monte Carlo simulation. This model adeptly characterizes project risks and quantifies uncertainty variables, thereby making the simulation more aligned with reality. Moreover, the model illuminates the intricate relationship between project risk and project productivity, highlighting the origins of fund flow fluctuations. It is imperative to identify project risks early and address ECs promptly and effectively. Through sensitivity analysis and strategies, we ensure the stability of fund flow. This study offers a pivotal framework for understanding and managing fund flow in projects, emphasizing the central role of system dynamics in this process.

Background Under the influences of chemotherapy regimens, clinical staging, immunologic expressions and other factors, the survival rates of patients with diffuse large B-cell lymphoma (DLBCL) are different. The accurate prediction of mortality hazards is key to precision medicine, which can help clinicians make optimal therapeutic decisions to extend the survival times of individual patients with DLBCL. Thus, we have developed a predictive model to predict the mortality hazard of DLBCL patients within 2 years of treatment. Methods We evaluated 406 patients with DLBCL and collected 17 variables from each patient. The predictive variables were selected by the Cox model, the logistic model and the random forest algorithm. Five classifiers were chosen as the base models for ensemble learning: the naïve Bayes, logistic regression, random forest, support vector machine and feedforward neural network models. We first calibrated the biased outputs from the five base models by using probability calibration methods (including shape-restricted polynomial regression, Platt scaling and isotonic regression). Then, we aggregated the outputs from the various base models to predict the 2-year mortality of DLBCL patients by using three strategies (stacking, simple averaging and weighted averaging). Finally, we assessed model performance over 300 hold-out tests. Results Gender, stage, IPI, KPS and rituximab were significant factors for predicting the deaths of DLBCL patients within 2 years of treatment. The stacking model that first calibrated the base model by shape-restricted polynomial regression performed best (AUC = 0.820, ECE = 8.983, MCE = 21.265) in all methods. In contrast, the performance of the stacking model without undergoing probability calibration is inferior (AUC = 0.806, ECE = 9.866, MCE = 24.850). In the simple averaging model and weighted averaging model, the prediction error of the ensemble model also decreased with probability calibration. Conclusions Among all the methods compared, the proposed model has the lowest prediction error when predicting the 2-year mortality of DLBCL patients. These promising results may indicate that our modeling strategy of applying probability calibration to ensemble learning is successful.

Background Although many patients receive good prognoses with standard therapy, 30–50% of diffuse large B-cell lymphoma (DLBCL) cases may relapse after treatment. Statistical or computational intelligent models are powerful tools for assessing prognoses; however, many cannot generate accurate risk (probability) estimates. Thus, probability calibration-based versions of traditional machine learning algorithms are developed in this paper to predict the risk of relapse in patients with DLBCL. Methods Five machine learning algorithms were assessed, namely, naïve Bayes (NB), logistic regression (LR), random forest (RF), support vector machine (SVM) and feedforward neural network (FFNN), and three methods were used to develop probability calibration-based versions of each of the above algorithms, namely, Platt scaling (Platt), isotonic regression (IsoReg) and shape-restricted polynomial regression (RPR). Performance comparisons were based on the average results of the stratified hold-out test, which was repeated 500 times. We used the AUC to evaluate the discrimination ability (i.e., classification ability) of the model and assessed the model calibration (i.e., risk prediction accuracy) using the H-L goodness-of-fit test, ECE, MCE and BS. Results Sex, stage, IPI, KPS, GCB, CD10 and rituximab were significant factors predicting the 3-year recurrence rate of patients with DLBCL. For the 5 uncalibrated algorithms, the LR (ECE = 8.517, MCE = 20.100, BS = 0.188) and FFNN (ECE = 8.238, MCE = 20.150, BS = 0.184) models were well-calibrated. The errors of the initial risk estimate of the NB (ECE = 15.711, MCE = 34.350, BS = 0.212), RF (ECE = 12.740, MCE = 27.200, BS = 0.201) and SVM (ECE = 9.872, MCE = 23.800, BS = 0.194) models were large. With probability calibration, the biased NB, RF and SVM models were well-corrected. The calibration errors of the LR and FFNN models were not further improved regardless of the probability calibration method. Among the 3 calibration methods, RPR achieved the best calibration for both the RF and SVM models. The power of IsoReg was not obvious for the NB, RF or SVM models. Conclusions Although these algorithms all have good classification ability, several cannot generate accurate risk estimates. Probability calibration is an effective method of improving the accuracy of these poorly calibrated algorithms. Our risk model of DLBCL demonstrates good discrimination and calibration ability and has the potential to help clinicians make optimal therapeutic decisions to achieve precision medicine.

Octamer-binding transcription factor 4 (OCT4) and cancerous inhibitor of protein phosphatase 2A (CIP2A) are upregulated in testicular cancer and cell lines. However, its contribution to orchitis (testicular inflammation) is unclear and was thus, investigated herein. Cell-based experiments on a lipopolysaccharide (LPS)-induced orchitis mouse model revealed robust inflammation, apoptotic cell death, and redox disorder in the Leydig (interstitial), Sertoli (supporting), and, germ cells. Meanwhile, real-time quantitative PCR revealed low OCT4 and CIP2A levels in testicular tissue and LPS-stimulated cells. A gain-of-function study showed that OCT4 overexpression not only increased CIP2A expression but also repressed LPS-induced inflammation, apoptosis, and redox disorder in the aforementioned cells. Furthermore, the re-inhibition of CIP2A expression by TD-19 in OCT4-overexpressing cells counteracted the effects of OCT4 overexpression on inflammation, apoptosis, and redox equilibrium. In addition, our results indicated that the Keap1-Nrf2-HO-1 signaling pathway was mediated by OCT4 and CIP2A. These findings provide insights into the potential mechanism underlying OCT4- and CIP2A-mediated testicular inflammation.

Previous research has shown abnormal functional network gradients in Alzheimer's disease (AD). Structural network gradient is capable of capturing continuous changes in brain morphology and has the ability to elucidate the underlying processes of neurodevelopment. However, it remains unclear whether structural network gradients are altered in AD and what associations exist between these changes and cognitive function, and gene expression profiles. By constructing an individualized structural network gradient decomposition framework, we calculated the morphological similarity network (MSN) gradients for 404 subjects (186 AD patients and 218 normal controls). We investigated AD-related alterations in MSN gradients, along with the associations between MSN gradients and cognitive function, MSN topological properties, and gene expression profiles. Our findings indicated that the principal MSN gradient alterations in AD were primarily characterized by an increase in the primary and secondary sensory cortices and a decrease in the association cortex 1. The primary and higher-order cortices exhibited opposite associations with cognition, including executive function, language skills, and memory processes. Moreover, the principal MSN gradients were found to significantly predict cognitive function in AD. The altered gradient pattern was 14.8% attributable to gene expression profiles, and the genes demonstrating the highest correlation are involved in metabolic activity and synaptic signaling. Our results offered novel insights into the underlying mechanisms of structural brain network impairment in AD patients, enhancing our understanding of the neurobiological processes responsible for impaired cognition in patients with AD, and offering a new dimensional structural biomarker for AD.

Falls are a common occurrence among older adults and can pose serious health and financial burdens. Several studies have shown that a decline of ADL, cognitive decline, and depression are all associated with an increased risk of falls in older adults. However, there is currently a lack of research examining these three factors in conjunction and their combined effect on the risk of falls. Our study aims to examine the longitudinal associations between distinct trajectories of activities of daily living, cognition, and psychological status among adults and their risk of falls. We conducted a longitudinal analysis using data from five waves (2011-2020) of the China Health and Retirement Longitudinal Study (CHARLS). A group-based multiple trajectory model (GBMTM) was employed to identify latent ADL-cognitive-Depression trajectories among 2,938 individuals aged 60 years. Multivariable logistic regression models were then used to assess the odds of experiencing falls between 2018 and 2020 across the identified trajectory groups, with sequential adjustment for demographics, health behaviors, comorbidities, and functional limitations. GBMTM identified five distinct trajectory subtypes: \"Pervasive Decline (Severe-ADL-Decline)\" (n = 378, 12.87%), \"Pervasive Decline (Mild-ADL-Decline)\" (n = 449, 15.28%), \"Cognitive - Decline & Depressed\" (n = 338, 11.50%), \"Mild ADL Decline\" (n = 351, 11.95%) and \"Normal\" (n = 1422, 48.40%). Compared to the \"Normal\" group, all other trajectory groups showed significantly elevated risks of falls in the fully adjusted model. The highest risk was observed in the \"Pervasive Decline (Severe-ADL-Decline)\" group (OR = 2.83, 95%CI: 2.12-3.77), followed by the \"Pervasive Decline (Mild-ADL-Decline)\" (OR = 2.13, 95%CI: 1.62-2.80), \"Mild ADL Decline\" (OR = 1.97, 95%CI: 1.47-2.63), and \"Cognitive Decline & Depressed\" (OR = 1.47, 95%CI: 1.07-2.02) groups. Five distinct trajectory groups of ADL, cognitive function, and depressive symptoms were identified. These groups exhibited significant differences in fall risk, with the \"Pervasive Decline (Severe-ADL-Decline)\" group presenting the highest risk, necessitating multidimensional comprehensive interventions for fall prevention.