Photocatalytic Upgrading of Polylactic Acid Waste into Alanine under Mild Conditions.

Polylactic acid (PLA) has attracted increasing interest as a sustainable plastic because it can be degraded into CO2 and H2O in nature. However, this process is sluggish, and even worse, it is a CO2-emitting and carbon resource waste process. Therefore, it is highly urgent to develop a novel strategy for recycling post-consumer PLA to achieve a circular plastic economy. Herein, we report a one-pot photoreforming route for the efficient and selective amination of PLA waste into value-added alanine using CoP/CdS catalysts under mild conditions. Results show the alanine production rate can reach up to 2.4 mmol gcat -1 h-1, with a high selectivity (>75 %) and excellent stability. Time-resolved transient absorption spectra (TAS) reveal that CoP can rapidly extract photogenerated electrons from CdS to accelerate proton reduction, favoring hole-dominated PLA oxidation to coproduce alanine. This study offers an appealing way for upcycling PLA waste and creates new opportunities for green synthesis of amino acids.

Cross-modality interaction for few-shot multispectral object detection with semantic knowledge.

Multispectral object detection (MOD), which incorporates additional information from thermal images into object detection (OD) to robustly cope with complex illumination conditions, has garnered significant attention. However, existing MOD methods always demand a considerable amount of annotated data for training. Inspired by the concept of few-shot learning, we propose a novel task called few-shot multispectral object detection (FSMOD) that aims to accomplish MOD using only a few annotated data from each category. Specifically, we first design a cross-modality interaction (CMI) module, which leverages different attention mechanisms to interact with the information from visible and thermal modalities during backbone feature extraction. With the guidance of interaction process, the detector is able to extract modality-specific backbone features with better discrimination. To improve the few-shot learning ability of the detector, we also design a semantic prototype metric (SPM) loss that integrates semantic knowledge, i.e., word embeddings, into the optimization process of embedding space. Semantic knowledge provides stable category representation when visual information is insufficient. Extensive experiments on the customized FSMOD dataset demonstrate that the proposed method achieves state-of-the-art performance.

Electrocatalytic reforming of waste plastics into high value-added chemicals and hydrogen fuel.

The upcycling of waste plastic offers an attractive way to protect the environment and turn waste into value-added chemicals and H2 fuel. Herein, we report a novel electroreforming strategy to upcycle waste polyethylene terephthalate into high value-added chemicals, such as terephthalate and carbonate, over a Pd modified Ni foam catalyst. This system exhibits excellent electrocatalytic activity (400 mA cm-2 at 0.7 V vs. RHE) and high selectivity (95%)/faradaic efficiency (93%) for the product carbonate. Our work demonstrates a technology that can not only transform waste polyethylene terephthalate into value-added chemicals but also generate H2 fuel via an all-in-one electro-driven process.

Information flow analysis of interactome networks.

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.