Controlling Redox Status for Stem Cell Survival, Expansion, and Differentiation

International audienceReactive oxygen species (ROS) have long been considered as pathological agents inducing apoptosis under adverse culture conditions. However, recent findings have challenged this dogma and physiological levels of ROS are now considered as secondary messengers, mediating numerous cellular functions in stem cells. Stem cells represent important tools for tissue engineering, drug screening, and disease modeling. However, the safe use of stem cells for clinical applications still requires culture improvements to obtain functional cells. With the examples of mesenchymal stem cells (MSCs) and pluripotent stem cells (PSCs), this review investigates the roles of ROS in the maintenance of self-renewal, proliferation, and differentiation of stem cells. In addition, this work highlights that the tight control of stem cell microenvironment, including cell organization, and metabolic and mechanical environments, may be an effective approach to regulate endogenous ROS generation. Taken together, this paper indicates the need for better quantification of ROS towards the accurate control of stem cell fate

An Empirical Study on the Influencing Factors of University Students’ Sense of Gain in Ideological and Political Theory Course -- Take the Course of “Ideological and Moral Cultivation and Legal Basis”as An Example

The self-made questionnaire was administered to a random sample of 1000 undergraduates, the result of data analysis shows that the “Mechanism model of influencing factors on university students’ ‘Basic Course’  gain” proposed in this paper can partly explain the influence of personal, family, school and social factors on college students’ “Basic Course” acquisition; The factors of family, school and society are the external factors which affect the students’ sense of gain of“Basic Course”, and the personal factors are the internal factors which affect the students’ sense of gain of “Basic Course”; External factors act through internal factors. Based on that, this paper puts forward some suggestions and countermeasures to enhance the sense of gain of university students’“Basic courses”

Remembering Joe Slovo’s third visit to China

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Feature learning from incomplete EEG with denoising autoencoder

An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb–Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect

Painterly Image Harmonization using Diffusion Model

Painterly image harmonization aims to insert photographic objects into paintings and obtain artistically coherent composite images. Previous methods for this task mainly rely on inference optimization or generative adversarial network, but they are either very time-consuming or struggling at fine control of the foreground objects (e.g., texture and content details). To address these issues, we propose a novel Painterly Harmonization stable Diffusion model (PHDiffusion), which includes a lightweight adaptive encoder and a Dual Encoder Fusion (DEF) module. Specifically, the adaptive encoder and the DEF module first stylize foreground features within each encoder. Then, the stylized foreground features from both encoders are combined to guide the harmonization process. During training, besides the noise loss in diffusion model, we additionally employ content loss and two style losses, i.e., AdaIN style loss and contrastive style loss, aiming to balance the trade-off between style migration and content preservation. Compared with the state-of-the-art models from related fields, our PHDiffusion can stylize the foreground more sufficiently and simultaneously retain finer content. Our code and model are available at https://github.com/bcmi/PHDiffusion-Painterly-Image-Harmonization.Comment: Accepted by ACMMM 202