Manipulation and Study of Gene Expression in Neurotoxin- Treated Neuronal PC12 and SH-SY5Y Cells for In Vitro Studies of Parkinson’s Disease

Neuronal PC12 and SH-SY5Y cells are highly suitable in vitro models for study of the neurodegenerative mechanisms occurring in Parkinson’s disease (PD). Differentiated PC12 and SH-SY5Y cells bear many similarities to the neuronal populations affected in PD, and they provide a convenient source of large amounts of homogeneous material for biochemical and molecular downstream applications. In the present review, we describe how to differentiate PC12 and SH-SY5Y cells into neuron-like cells and provide protocols for their transfection with plasmids and infection with viral particles to manipulate gene expression. We also describe how to treat neuronal PC12 and SH-SY5Y cells with the classical PD neurotoxins 6-hydroxydopamine (6-OHDA) and 1-methyl-4-phenyl-pyridinium ion (MPP+). Finally, we give detailed methods for several downstream applications useful for the analysis of cell death pathways in PD

SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking

Due to the ambiguity of homophones, Chinese Spell Checking (CSC) has widespread applications. Existing systems typically utilize BERT for text encoding. However, CSC requires the model to account for both phonetic and graphemic information. To adapt BERT to the CSC task, we propose a token-level self-distillation contrastive learning method. We employ BERT to encode both the corrupted and corresponding correct sentence. Then, we use contrastive learning loss to regularize corrupted tokens' hidden states to be closer to counterparts in the correct sentence. On three CSC datasets, we confirmed our method provides a significant improvement above baselines

Innovative solutions for language growth: the impact of problem-based learning via DingTalk on Chinese undergraduates’ business vocabulary amid COVID-19

Amidst the COVID-19, which has necessitated the widespread use of distant learning, there has been a notable increase in the recognition and utilization of inventive pedagogical methods and technological tools in the field of language teaching. The primary objective of this research is to assess the effects of DingTalk-based PBL on the business vocabulary growth of Chinese undergraduates during the pandemic, with a particular focus on remote learning environments. This mixed-methods research employed a sample of 58 participants. The study involved both quantitative vocabulary assessments and qualitative interviews. The quantitative assessments aimed to measure the impact on vocabulary scores, while qualitative interviews were conducted to gather insights into participants’ experiences and perceptions regarding DingTalk-based PBL. The quantitative assessment revealed a significant improvement in business vocabulary scores among the participants who underwent DingTalk-based PBL. This result indicates the platform’s potential to enhance language acquisition. The qualitative interviews provided further insights, with participants expressing positive attitudes toward DingTalk-based PBL. They emphasized its capacity to sustain engagement, foster collaboration, and bridge the gap between remote learning and effective language acquisition. These findings underscore the transformative potential of DingTalk-based PBL in language education, especially in the context of challenges posed by the pandemic. While recognizing the constraints of this study, such as its limited duration and restricted contextual applicability, the research encourages further investigation into sustainable vocabulary expansion, the development of multifaceted language abilities, and the integration of these platforms into emerging hybrid educational frameworks. This study makes a valuable contribution to the ongoing discourse regarding novel technology-based methods in language instruction, providing relevant insights applicable to both present and future educational contexts

Differentiable Genetic Programming for High-dimensional Symbolic Regression

Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in solving SR problems. However, as the scale of SR problems increases, GP often poorly demonstrates and cannot effectively address the real-world high-dimensional problems. This limitation is mainly caused by the stochastic evolutionary nature of traditional GP in constructing the trees. In this paper, we propose a differentiable approach named DGP to construct GP trees towards high-dimensional SR for the first time. Specifically, a new data structure called differentiable symbolic tree is proposed to relax the discrete structure to be continuous, thus a gradient-based optimizer can be presented for the efficient optimization. In addition, a sampling method is proposed to eliminate the discrepancy caused by the above relaxation for valid symbolic expressions. Furthermore, a diversification mechanism is introduced to promote the optimizer escaping from local optima for globally better solutions. With these designs, the proposed DGP method can efficiently search for the GP trees with higher performance, thus being capable of dealing with high-dimensional SR. To demonstrate the effectiveness of DGP, we conducted various experiments against the state of the arts based on both GP and deep neural networks. The experiment results reveal that DGP can outperform these chosen peer competitors on high-dimensional regression benchmarks with dimensions varying from tens to thousands. In addition, on the synthetic SR problems, the proposed DGP method can also achieve the best recovery rate even with different noisy levels. It is believed this work can facilitate SR being a powerful alternative to interpretable ML for a broader range of real-world problems

Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach

In a multitude of industrial fields, a key objective entails optimising resource management whilst satisfying user requirements. Resource management by industrial practitioners can result in a passive transfer of user loads across resource providers, a phenomenon whose accurate characterisation is both challenging and crucial. This research reveals the existence of user clusters, which capture macro-level user transfer patterns amid resource variation. We then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric model capable of automating cluster identification, and thereby predicting user transfer in response to resource variation. Furthermore, CLUSTER facilitates uncertainty quantification for further reliable decision-making. Our method enables privacy protection by functioning independently of personally identifiable information. Experiments with simulated and real-world data from the communications industry reveal a pronounced alignment between prediction results and empirical observations across a spectrum of resource management scenarios. This research establishes a solid groundwork for advancing resource management strategy development

Particle-size fractal dimensions and pore structure characteristics of soils of typical vegetation communities in the Kubuqi Desert

The goal of this study was to investigate the soil particle-size distribution (PSD) and pore structure characteristics in the Kubuqi Desert in order to provide basic data for gaining insights into the soil-modifying properties of the local vegetation. Based on laser diffraction analysis, we measured the soil PSD and calculated the single and multi-fractal dimensions of the soils under typical artificial forest and natural grassland vegetation. The diameters, volumes and surface areas of the soil pores were determined via nitrogen adsorption experiments. The relationships between the fractal characteristics and pore structure parameters of the soil were investigated via redundancy analysis and stepwise regression. The clay content was less than 2.0%, and the sand content was greater than 75.0%. There was variability in the PSD and fractal dimension, which was mainly observed for the 0–20 cm, 20–80 cm, and 80–100 cm soil layers. In the 0–80 cm, the fine particle content, single fractal dimension (D), entropy dimension (D1), ratio of D1 to the capacity dimension (D1/D0), and correlation dimension (D2) were smaller for the forest soils than for the grassland soils. The fine particle (clay and silt) content of the 80–100 cm soil layer was approximately 37.8% higher for the Salix psammophila-8a than for the Salix psammophila-3a, and it was approximately 161.4% higher for the Populus popular-35a than for the Pinus sylvestris var. mongolica-8a. The silt content, D1, D1/D0, and D2 were significantly positively correlated with the specific surface area (SSA), total pore volume (TPV), and average pore diameter (APD) of the nanopores (p < 0.05, 0.01, or 0.001), and they were significantly negatively correlated with the percentage of the micropore volume (PMV) (p < 0.05 or 0.01). In the Kubuqi Desert, the fine particle content and fractal dimensions of the soil layer below the root zone of shrub and arbor vegetation increased with increasing stand age, but the trend was reversed in the shallower soil layers. The variability of the soil PSD characteristics was strongly correlated with the variability of the nanopore parameters on the microscopic scale, suggesting that the total pore volume, average pore diameter, and percentage of the micropore volume may be potential indicators of the soil structure and quality