Cellulose is the most abundant natural biopolymer, with unique properties such as biodegradability, biocompability, nontoxicity, and so on. However, its extensive application has actually been hindered, because of its insolubility in water and most solvents. Herein, highly efficient cellulose solvents were developed by coupling diallylimidazolium methoxyacetate ([A2im][CH3OCH2COO]) with polar aprotic solvents dimethyl sulfoxide (DMSO), N,N-dimethylformamide (DMF), and N,N-dimethylacetamide (DMA). Attractively, these solvents showed extraordinarily powerful dissolution performance for cellulose (e.g., 26.1 g·100g−1) in [A2im][CH3OCH2COO]/DMSO(RDMSO = 1.01 solvent even at 25 °C), which is much more advantageous over previously reported solvents. To our knowledge, such powerful cellulose solvents have not been reported before. The cellulose dissolution mechanism is proposed to be of three combined factors: (1) The hydrogen bond interactions of the H2, H4 and H6 in [A2im]+ and the carboxyl O atom in [CH3OCH2COO]−, along with the hydroxyl H atom and O atom in cellulose, are main driving force for cellulose dissolution; (2) the dissociation of [A2im][CH3OCH2COO] by DMF increases the anion and cation concentrations and thus promotes cellulose dissolution; (3) at the same time, DMF also stabilizes the dissolved cellulose chains. Meanwhile, the porous cellulose material with a varying morphologic structure could be facially fabricated by modulating the cellulose solution concentration. Additionally, the dissolution of cellulose in the solvents is only a physical process, and the regenerated cellulose from the solvents retains sufficient thermostability and a chemical structure similar to the original cellulose. Thus, this work will provide great possibility for developing cellulose-based products at ambient temperatures or under no extra heating/freezing conditions.

As the main by-product during the oil production of peony seeds, the episperm is traditionally used as a lead component in folk herbal formulas for the cancer treatment in China. However, the investigation of its phytochemical foundation underlying anticancer effects remains an ongoing challenge. The work therefore determined growth inhibition activities of 8 solvent extracts of peony episperms in the human liver cancer cell line. This activity was then mapped onto the secondary metabolite profile of extracts by principal components analysis (PCA). The top 3 principal components of High Performance Liquid Chromatograph (HPLC)-PCA map discriminated extract activities mainly based on the differential content of 5 stilbene compounds, which were then tested individually. The trans-ε-viniferin, gnetin-H, suffruticosol A, suffruticosol B, and suffruticosol C were thus determined as growth inhibitors and apoptosis inducers of human liver cancer cells with activities comparable to that of the antineoplastic cisplatin. A partial least squares regression-HPLC model was also constructed for the prediction of inhibitory effects of peony episperm extracts. These results expand the fundamental understanding of the peony episperms and support its current medicinal uses in China. Moreover, PCA-mediated secondary metabolite mapping was proved to be an efficient approach to qualify biomedical products required for pharmaceutical and medicinal uses.

Classroom behavior is an important criterion for evaluating instructional efficacy. In comparison to other behaviors, the challenge of classroom behavior detection is primarily influenced by ambient light variables and the presence of too many targets to recognize, resulting in missed detection. Recent research has demonstrated that information about the human skeleton can be used to identify classroom conduct. As a result, we present an enhanced yolov5-based skeletal recognition system for detecting classroom behavior in this paper. First, the YOLOv5 detection algorithm is improved to extract target prospects for the problem of missed detection; then, the human skeleton information is obtained using the Alphapose framework; finally, the skeletal data is sent into a two-stream adaptive graph convolution network to allow for the accurate recognition of various classroom behaviors. According to extensive tests, the detection algorithm based on bone recognition improves detection accuracy and lowers the false detection rate.

Conflicts of interest/Competing interests (include appropriate disclosures)There are no conflicts to declare.Availability of data and material (data transparency) Supporting information available. Code availability (software application or custom code) Not applicableAuthors' contributions (optional: please review the submission guidelines from the journal whether statements are mandatory) Graham Dawson, Xiaorong Cheng and Anthony Centeno contributed to the conception and design of the study. FDTD calculations were performed by Anthony Centeno. Experiments were performed by Wentian Niu, Yulia Pilyugina and Ruochen Liu.

2D-layered transition metal chalcogenides are useful semiconductors for a wide range of opto-electronic applications. Their similarity as layered structures offers exciting possibility to modify their electronic properties by creating new...