County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China.

The role of carbon emissions resulting from land use change in the compilation of national greenhouse gas emission inventories is of paramount significance. This study is centered on the Mianyang Science and Technology City New Area located in Sichuan Province, China. We used the CLUE-S model and Sentinel-2A remote sensing data from 2017 to simulate and validate land use changes in 2022. Based on this validation, we established three simulation scenarios: a baseline scenario, an agricultural development scenario, and a construction development scenario. Using remote sensing data from 2022, we projected the land use for 2030. We also used CO2 concentration data collected in 2022 and 2023, processed the data using ArcGIS and Python, and conducted a quantitative analysis of carbon emissions under each scenario. Ultimately, the accuracy of both measured and predicted CO2 values for 2023 was juxtaposed and authenticated, thus concluding the investigative cycle of this study. Key findings include: (1) The accuracy of the CLUE-S model in the study area was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. In 2022, the overall accuracy is 98.19%, the quantity disagreement is 1.7%, and the allocation disagreement is 2.2%. (2) Distinct land resource utilization characteristics in scenarios, highlighting potential impacts on economic development and pollution. (3) Increased carbon emissions across scenarios, with construction development showing the highest rise (4.170%) and agricultural development the lowest (0.766%). (4) The predictive accuracy of the validation group's CO2 concentration values can reach 99.5%. This study proposes precise CO2 prediction at the county level, thus laying the groundwork for future research endeavors. Such findings are indispensable for informing carbon policy formulation and promoting low-carbon development strategies.

Task-adaptive physical reservoir computing.

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in 'physical' reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a 'task-adaptive' approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe).

Deep Image Matting With Sparse User Interactions.

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous. Recently, some hint-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, some hints for removing semantic ambiguity and improving matting quality are essential. Apparently, there is a trade-off between interaction cost and matting quality. To balance performance and user-friendliness, we propose an improved deep image matting framework which is trimap-free and only needs sparse user click or scribble interaction to minimize the needed auxiliary constraints while still allowing interactivity. Moreover, we introduce uncertainty estimation that predicts which parts need polishing and conduct uncertainty-guided refinement. To trade off runtime against refinement quality, users can also choose different refinement modes. Experimental results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort. Finally, we demonstrate the extensibility of our framework to video human matting without any structure modification, by adding optical flow-based sparse hint propagation and temporal consistency regularization imposed on the single frame.

Associations between Adipokines and Metabolic Dysfunction-Associated Fatty Liver Disease Using Three Different Diagnostic Criteria.

BACKGROUND: A panel of experts proposed a new definition of metabolic dysfunction-associated fatty liver disease (MAFLD) in 2020. To date, the associations between adipokines, such as adiponectin, adipsin, and visfatin and MAFLD remain unclear. Therefore, we aimed to evaluate the associations between each of these three adipokines and MAFLD using different diagnostic criteria. METHODS: In total, 221 participants were included in our study based on medical examination. Detailed questionnaire information, physical examination, abdominal ultrasound, and blood-biochemical-test indexes were collected. The levels of adipokines were tested by using an enzyme immunoassay. Logistic regression models were used to assess the associations of the adipokines with MAFLD. RESULTS: In total, 122 of the participants were diagnosed with MAFLD. Higher levels of adipsin and lower levels of adiponectin were found in the MAFLD group than in the non-MAFLD group (all p < 0.05). According to the logistic regression analysis, the ORs were 0.11 (95% CI: 0.05-0.23) for adiponectin, 4.46 (95% CI: 2.19-9.12) for adipsin, and 0.51 (95% CI: 0.27-0.99) for visfatin when comparing the highest tertile with the lowest tertile (all p-trend < 0.05). The inverse association between adiponectin and MAFLD was strongest when T2DM was used as the diagnostic criterion alone, and the positive association between adipsin and MAFLD was strongest when BMI was used as the diagnostic criterion alone. There was no significant association between visfatin and MAFLD, regardless of whether each of BMI, T2DM, or metabolic dysregulation (MD) was used as the diagnostic criterion for MAFLD alone. CONCLUSION: Adipsin levels were positively associated with MAFLD and adiponectin levels were inversely associated with MAFLD. The strength of these associations varied according to the different diagnostic criteria for MAFLD.

Biosynthesis of coelulatin for the methylation of anthraquinone featuring HemN-like radical S-adenosyl-L-methionine enzyme.

Bacterial aromatic polyketides are usually biosynthesized by the type II polyketide synthase (PKS-II) system. Advances in deoxyribonucleic acid (DNA) sequencing, informatics, and biotechnologies have broadened opportunities for the discovery of aromatic polyketides. Meanwhile, metagenomics is a biotechnology that has been considered as a promising approach for the discovery of novel natural products from uncultured bacteria. Here, we cloned a type II polyketide biosynthetic gene cluster (BGC) from the soil metagenome, and the heterologous expression of this gene cluster in Streptomyces coelicolor M1146 resulted in the production of three anthraquinones, two of which (coelulatins 2 and 3) had special hydroxymethyl and methyloxymethyl modifications at C2 of the polyketide scaffold. Gene deletion and in vitro biochemical characterization indicated that the HemN-like radical S-adenosyl-L-methionine (SAM) enzyme CoeI exhibits methylation and is involved in C2 modification.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis.

Background: Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial. Methods: This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa. Results: Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients. Conclusions: In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion.

This paper studies the problem of StyleGAN inversion, which plays an essential role in enabling the pretrained StyleGAN to be used for real image editing tasks. The goal of StyleGAN inversion is to find the exact latent code of the given image in the latent space of StyleGAN. This problem has a high demand for quality and efficiency. Existing optimization-based methods can produce high-quality results, but the optimization often takes a long time. On the contrary, forward-based methods are usually faster but the quality of their results is inferior. In this paper, we present a new feed-forward network "E2Style" for StyleGAN inversion, with significant improvement in terms of efficiency and effectiveness. In our inversion network, we introduce: 1) a shallower backbone with multiple efficient heads across scales; 2) multi-layer identity loss and multi-layer face parsing loss to the loss function; and 3) multi-stage refinement. Combining these designs together forms an effective and efficient method that exploits all benefits of optimization-based and forward-based methods. Quantitative and qualitative results show that our E2Style performs better than existing forward-based methods and comparably to state-of-the-art optimization-based methods while maintaining the high efficiency as well as forward-based methods. Moreover, a number of real image editing applications demonstrate the efficacy of our E2Style. Our code is available at https://github.com/wty-ustc/e2style.

Oxidized Vitamin C (DHA) Overcomes Resistance to EGFR-targeted Therapy of Lung Cancer through Disturbing Energy Homeostasis.

Switching aerobic respiration to anaerobic glycolysis of cancer cells plays an important role in development and progression of acquired resistance. Since vitamin C enabled the inhibition of glycolysis of cancer cells, and erlotinib-resistant sub-line of HCC827 (ER6 cells) switched its metabolic features to higher glycolysis for survival, we hypothesize that vitamin C is able to inhibit glycolysis of ER6 cells. In this study, we found that both reduced vitamin C and oxidized vitamin C (DHA) could selectively suppress the viability of ER6 cells. DHA was efficient in inhibiting glycolysis and leading to energy crisis, which could be one mechanism for overcoming drug resistance to erlotinib of ER6 cells. Our data suggest that applying DHA could be a novel treatment strategy for NSCLC with acquired resistance to targeted therapy.