ABSTRACT
Artificial light at night (ALAN) may pose threat to rotifer Brachionus plicatilis. Additionally, the food of rotifer, i.e. algal community composition, often fluctuates. Thus, we selected five wavelengths of ALAN (purple, blue, green, red, white) and a three-colored light flashing mode (3-Flash) to test their impacts on life history traits of B. plicatilis with different food experiences, including those feeding Chlorella vulgaris (RC) or Phaeocystis globosa (RP). Results indicated purple ALAN promoted RC development, white ALAN inhibited RC development, while 3-Flash and white ALAN promoted RP development. Under red and white ALAN, RP increased fecundity but decreased lifespan. High-quality food enhanced rotifer's resistance to the impact of ALAN on lifespan. ALAN and food experience interacted on B. plicatilis. The effect of blue ALAN has less negative effects on B. plicatilis, based on hierarchical cluster analysis. Such findings are helpful to evaluate the potential impact of ALAN on marine zooplankton.
ABSTRACT
Introduction: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity. Methods: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification. Results and discussion: Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.
ABSTRACT
Vaccines play a crucial role in improving global public health, with the ability to stem the spread of infectious diseases and the potential to eradicate them completely. Compared with pharmaceuticals that treat disease, however, preventative vaccines have received less attention from both biomedical researchers and innovation scholars. This neglect has substantial human and financial costs, as vividly illustrated by the COVID-19 pandemic. In this article, we argue that the large number of ``missing'' vaccines is likely due to more than lack of scientific opportunities. Two key aspects of vaccines help account for their anemic development pipeline: (1) they are preventatives rather than treatments; and (2) they are generally durable goods with long-term effects rather than products purchased repeatedly. We explain how both aspects make vaccines less profitable than repeat-purchase treatments, even given comparable IP protection. We conclude by arguing that innovation policy should address these market distortions by experimenting with larger government-set rewards for vaccine production and use. Most modestly, policymakers should increase direct funding-including no grants and public-private partnerships-and insurance-based market subsidies for vaccine development. We also make the case for a large cash prize for any new vaccine made available at low or zero cost.
