The effects of education and allocentrism on organizational commitment in Chinese companies: A multi-level analysis

As Chinese companies move to the world stage of business, they must leverage a more knowledgeable and collaborative workforce to meet new challenges. This study investigates how two prominent individual attributes, education and allocentrism, create work tension for human capital practices in Chinese companies. By surveying nearly 500 workers in four Chinese companies and using multi-level methodology, we demonstrate that higher levels of education work to the detriment of employees’ affective organizational commitment and positively influence seeking-to-leave behavior. In addition, this study suggests a positive relation between allocentrism and affective organizational commitment. Personalized leadership, a common leadership style in high-power distance cultures such as China, further exacerbates the problems with higher levels of education and diminishes the commitment benefits of allocentrism. Conversely, regardless of leadership style, if supervisors involve workers in decision-making activities, those workers who are more educated will become more committed to the organization and less likely to leave. Implications of these findings for practice and future research are discussed

Gunslinger Effect and Muller-Lyer Illusion: Examining Early Visual Information Processing for Late Limb-Target Control

The multiple process model contends that there are two forms of online control for manual aiming: impulse regulation and limb-target control. This study examined the impact of visual information processing for limb-target control. We amalgamated the Gunslinger protocol (i.e., faster movements following a reaction to an external trigger compared with the spontaneous initiation of movement) and Müller-Lyer target configurations into the same aiming protocol. The results showed the Gunslinger effect was isolated at the early portions of the movement (peak acceleration and peak velocity). Reacted aims reached a longer displacement at peak deceleration, but no differences for movement termination. The target configurations manifested terminal biases consistent with the illusion. We suggest the visual information processing demands imposed by reacted aims can be adapted by integrating early feedforward information for limb-target control

Piloting a scale-up platform for high-quality human T-cells production

Copyright \ua9 2024 Selvarajan, Teo, Chang, Ng, Cheong, Sivalingam, Khoo, Wong and Loo. Cell and gene therapies are an innovative solution to various severe diseases and unfulfilled needs. Adoptive cell therapy (ACT), a form of cellular immunotherapies, has been favored in recent years due to the approval of chimeric antigen receptor CAR-T products. Market research indicates that the industry’s value is predicted to reach USD 24.4 billion by 2030, with a compound annual growth rate (CAGR) of 21.5%. More importantly, ACT is recognized as the hope and future of effective, personalized cancer treatment for healthcare practitioners and patients worldwide. The significant global momentum of this therapeutic approach underscores the urgent need to establish it as a practical and standardized method. It is essential to understand how cell culture conditions affect the expansion and differentiation of T-cells. However, there are ongoing challenges in ensuring the robustness and reproducibility of the manufacturing process. The current study evaluated various adoptive T-cell culture platforms to achieve large-scale production of several billion cells and high-quality cellular output with minimal cell death. It examined factors such as bioreactor parameters, media, supplements and stimulation. This research addresses the fundamental challenges of scalability and reproducibility in manufacturing, which are essential for making adoptive T-cell therapy an accessible and powerful new class of cancer therapeutics

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing.

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated, and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimized experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 colony-forming units (CFU)/mL using metagenomic classification. Machine learning can be coupled with long-read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP organisms to 10 CFU/mL. IMPORTANCE This research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7-14 days, while our approach can significantly reduce this time. By combining advanced long-read nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process

Natural pH indicators: Using paper or solution?

NATURAL pH INDICATORS: USING PAPER OR SOLUTION? In this work. the fruit extracts of Morus nigra - mulberry, Szygium cuminii - jambolao, Vitis vinifera - grape, Myrciaria cauliflora - jabuticaba are suggested as pH indicators in the form of either solutions or paper. The pH indicator solutions were prepared by soaking the fruits or their peels in ethanol 1:3 (m/V) for 24 h, followed by simple filtration. The pH indicator papers were prepared by imersion of the qualitative filter paper strips in the pH indicator solutions. The different pH leads to color changes in the indicator solutions or papers and it can be used for teaching elementary chemistry concepts.25468468