Unlocking Li superionic conductivity in face-centred cubic oxides via face-sharing configurations.

Oxides with a face-centred cubic (fcc) anion sublattice are generally not considered as solid-state electrolytes as the structural framework is thought to be unfavourable for lithium (Li) superionic conduction. Here we demonstrate Li superionic conductivity in fcc-type oxides in which face-sharing Li configurations have been created through cation over-stoichiometry in rocksalt-type lattices via excess Li. We find that the face-sharing Li configurations create a novel spinel with unconventional stoichiometry and raise the energy of Li, thereby promoting fast Li-ion conduction. The over-stoichiometric Li-In-Sn-O compound exhibits a total Li superionic conductivity of 3.38 × 10-4 S cm-1 at room temperature with a low migration barrier of 255 meV. Our work unlocks the potential of designing Li superionic conductors in a prototypical structural framework with vast chemical flexibility, providing fertile ground for discovering new solid-state electrolytes.

Effects of Light Color on the Growth, Feeding, Digestion, and Antioxidant Enzymes of Tripneustes gratilla (Linnaeus, 1758).

To study the effects of light color on sea urchin (Tripneustes gratilla), blue light (B, λ450nm), yellow light (Y, λ585-590nm), red light (R, λ640nm), green light (G, λ510nm), white light (W, λ400-780nm), and darkness (H) groups were established in a recirculating seawater aquaculture system. Six different LED light color treatment groups with a photoperiod of 12 L:12 D were tested for 30 days to investigate the effects of different light colors on the feeding, growth, and enzyme activities of T. gratilla (142.45 ± 4.36 g). We found that using different LED light colors caused significantly different impacts on the feeding, growth, and enzyme activity of T. gratilla. Notably, the sea urchins in group B exhibited better growth, with a weight gain rate of 39.26%, while those in group R demonstrated poorer growth, with a weight gain rate of only 26%. The feeding status differed significantly (p < 0.05) between groups B and R, with group B consuming the highest daily intake (6.03 ± 1.69 g) and group R consuming the lowest (4.54 ± 1.26 g). Throughout the three phases, there was no significant change in the viability of the α-amylase (p > 0.05). Conversely, the pepsin viability significantly increased (p < 0.05) in group B. The lipase viability consistently remained at the lowest level, with no notable differences between group W and group B. In group R, both the α-amylase and pepsin viabilities remained lower, whereas the lipase viability was noticeably greater in each phase than in group B (p < 0.05). Among the antioxidant enzymes, group R exhibited a trend of initial increase followed by decreases in catalase, superoxide dismutase, and glutathione peroxidase activities, particularly during the third stage (15-30 days), during which a significant decrease in antioxidant enzyme activity was observed (p < 0.05). Taken together, these findings suggest that blue light positively affects the growth, feeding, digestion, and antioxidant capacity of T. gratilla in comparison with those in other light environments, whereas red light had an inhibitory effect. Furthermore, T. gratilla is a benthic organism that lives on shallow sandy sea beds. Thus, as short wavelengths of blue and green light are more widely distributed on the seafloor, and long wavelengths of red light are more severely attenuated on the seafloor, shorter wavelengths of light promote the growth of bait organisms of sea urchins, which provide better habitats for T. gratilla.

scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) has been widely used for disease studies, where sample batches are collected from donors under different conditions including demographic groups, disease stages, and drug treatments. It is worth noting that the differences among sample batches in such a study are a mixture of technical confounders caused by batch effect and biological variations caused by condition effect. However, current batch effect removal methods often eliminate both technical batch effect and meaningful condition effect, while perturbation prediction methods solely focus on condition effect, resulting in inaccurate gene expression predictions due to unaccounted batch effect. Here we introduce scDisInFact, a deep learning framework that models both batch effect and condition effect in scRNA-seq data. scDisInFact learns latent factors that disentangle condition effect from batch effect, enabling it to simultaneously perform three tasks: batch effect removal, condition-associated key gene detection, and perturbation prediction. We evaluate scDisInFact on both simulated and real datasets, and compare its performance with baseline methods for each task. Our results demonstrate that scDisInFact outperforms existing methods that focus on individual tasks, providing a more comprehensive and accurate approach for integrating and predicting multi-batch multi-condition single-cell RNA-sequencing data.

Construction of chub mackerel (Scomber japonicus) fishing ground prediction model in the northwestern Pacific Ocean based on deep learning and marine environmental variables.

Accurate prediction of the central fishing grounds of chub mackerel is substantial for assessing and managing marine fishery resources. Based on the high-seas chub mackerel fishery statistics and multi-factor ocean remote-sensing environmental data in the Northwest Pacific Ocean from 2014 to 2021, this article applied the gravity center of the fishing grounds, 2DCNN, and 3DCNN models to analyze the spatial and temporal variability of the chub mackerel catches and fishing grounds. Results:1) the primary fishing season of chub mackerel fishery was April-November which catches were mainly concentrated in 39°âˆ¼43°N, 149°âˆ¼154°E. 2) Since 2019, the annual gravity center of the fishing grounds has continued to move northeastward; the monthly gravity center has prominent seasonal migratory characteristics. 3) 3DCNN model was better than the 2DCNN model. 4) For 3DCNN, the model prioritized learning information on the most easily distinguishable ocean remote-sensing environmental variables in different classifications.

scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) has been widely used for disease studies, where sample batches are collected from donors under different conditions including demographical groups, disease stages, and drug treatments. It is worth noting that the differences among sample batches in such a study are a mixture of technical confounders caused by batch effect and the biological variations caused by condition effect. However, current batch effect removal methods often eliminate both technical batch effects and meaningful condition effects, while perturbation prediction methods solely focus on condition effects, resulting in inaccurate gene expression predictions due to unaccounted batch effects. Here we introduce scDisInFact, a deep learning framework that models both batch effect and condition effect in scRNA-seq data. scDisInFact learns latent factors that disentangle condition effects from batch effects, enabling it to simultaneously perform three tasks: batch effect removal, condition-associated key gene detection, and perturbation prediction. We evaluated scDisInFact on both simulated and real datasets, and compared its performance to baseline methods for each task. Our results demonstrate that scDisInFact outperforms existing methods that focus on individual tasks, providing a more comprehensive and accurate approach for integrating and predicting multi-batch multi-condition single-cell RNA-sequencing data.

Extraordinarily Large Electrocaloric Strength of Metal-Free Perovskites.

The availability of materials with high electrocaloric (EC) strengths is critical to enabling EC refrigeration in practical applications. Although large EC entropy changes, ΔSEC , and temperature changes, ΔTEC , have been achieved in traditional thin-film ceramics and polymer ferroelectrics, they require the application of very high electric fields and thus their EC strengths ΔSEC /ΔE and ΔTEC /ΔE are too low for practical applications. Here, a fundamental thermodynamic description is developed, and extraordinarily large EC strengths of a metal-free perovskite ferroelectric [MDABCO](NH4 )I3 (MDABCO) are predicted. The predicted EC strengths: isothermal ΔSEC /ΔE and adiabatic ΔTEC /ΔE for MDABCO are 18 J m kg-1 K-1 MV-1 and 8.06 K m MV-1 , respectively, more than three times the largest reported values in BaTiO3 single crystals. These predictions strongly suggest the metal-free ferroelectric family of materials as the best candidates among existing materials for EC applications. The present work not only presents a general approach to developing thermodynamic potential energy functions for ferroelectric materials but also suggests a family of candidate materials with potentially extremely high EC performance.

Bicyclol, a synthetic dibenzocyclooctadiene derivative, decreases hepatic lipids but increases serum triglyceride level in normal and hypercholesterolaemic mice.

Bicyclol is used for the treatment of chronic hepatitis B in China. In this study, the effects of bicyclol (100 or 300 mg kg(-1), p.o.) on serum and liver lipid contents were investigated in both normal and experimentally induced hypercholesterolaemic mice. Hypercholesterolaemia was induced by either oral administration of cholesterol/bile salt or feeding a diet containing lard/cholesterol. Daily administration of bicyclol for 7 days dose-dependently increased the serum triglyceride level (29-80%) but slightly decreased the hepatic total cholesterol level (12-17%) in normal mice. Co-administration of bicyclol with cholesterol/bile salt decreased the hepatic triglyceride and total cholesterol levels (7-15% and 25-31%, respectively), when compared with the drug-untreated and cholesterol/bile salt-treated group. Bicyclol treatment for 7 days decreased hepatic triglyceride (5-76%) and total cholesterol (5-48%) levels in mice fed with high-fat/cholesterol diet. In contrast, bicyclol treatment increased the serum triglyceride level (18-77%) in mice treated with cholesterol/bile salt or fed with high-fat/cholesterol diet. Bicyclol treatment also caused an increase in hepatic index of normal and hypercholesterolaemic mice (3-32%). The results indicate that bicyclol treatment can invariably decrease hepatic lipid levels and increase serum triglyceride levels in normal and hypercholesterolaemic mice.