In-Situ Construction of Anti-Aggregation Tellurium Nanorods/Reduced Graphene Oxide Composite to Enable Fast Sodium Storage.

Sodium-ion batteries (SIBs) as a replaceable energy storage technology have attracted extensive attention in recent years. The design and preparation of advanced anode materials with high capacity and excellent cycling performance for SIBs still face enormous challenges. Herein, a solution method is developed for in situ synthesis of anti-aggregation tellurium nanorods/reduced graphene oxide (Te NR/rGO) composite. The material working as the sodium-ion battery (SIB) anode achieves a high reversible capacity of 338 mAh g-1 at 5 A g-1 and exhibits up to 93.4% capacity retention after 500 cycles. This work demonstrates an effective preparation method of nano-Te-based composites for SIBs.

Novel database for accA gene revealed a vertical variability pattern of autotrophic carbon fixation potential of ammonia oxidizing archaea in a permeable subterranean estuary.

The autotrophic carbon fixation pathway of ammonia-oxidizing archaea (AOA) was the 3-hydroxypropionate/4-hydroxybutyrate (3-HP/4-HB) cycle, of which the acetyl-CoA carboxylase α-submit (accA) gene is widely recognized as the indicator. To date, there is no reference database or suitable cut-off value for operational taxonomic unit (OTU) clustering to analyze the diversity of AOA based on the accA gene. In this study, a reference database with 489 sequences was constructed, all the accA gene sequences was obtained from the AOA enrichment culture, pure culture and environmental samples. Additionally, the 79% was determined as the cut-off value for OTU clustering by comparing the similarity between the accA gene and the 16S rRNA gene. The developed method was verified by analyzing samples from the subterranean estuary and a vertical variation pattern of autotrophic carbon fixation potential of AOA was revealed. This study provided an effective method to analyze the diversity and autotrophic carbon fixation potential of AOA based on accA gene.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Face expression recognition based on NGO-BILSTM model.

Introduction: Facial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's performance depends largely on its hyperparameters, which is a challenge for optimization. Methods: In this paper, a Northern Goshawk optimization (NGO) algorithm is proposed to optimize the hyperparameters of BILSTM network for facial expression recognition. The proposed methods were evaluated and compared with other methods on the FER2013, FERplus and RAF-DB datasets, taking into account factors such as cultural background, race and gender. Results: The results show that the recognition accuracy of the model on FER2013 and FERPlus data sets is much higher than that of the traditional VGG16 network. The recognition accuracy is 89.72% on the RAF-DB dataset, which is 5.45, 9.63, 7.36, and 3.18% higher than that of the proposed facial expression recognition algorithms DLP-CNN, gACNN, pACNN, and LDL-ALSG in recent 2 years, respectively. Discussion: In conclusion, NGO algorithm effectively optimized the hyperparameters of BILSTM network, improved the performance of facial expression recognition, and provided a new method for the hyperparameter optimization of BILSTM network for facial expression recognition.

Rhoifolin Alleviates Alcoholic Liver Disease In Vivo and In Vitro via Inhibition of the TLR4/NF-κB Signaling Pathway.

Background: Alcoholic liver disease (ALD) is a common chronic liver disorder worldwide, which is detrimental to human health. A preliminary study showed that the total flavonoids within Citrus grandis "Tomentosa" exerted a remarkable effect on the treatment of experimental ALD. However, the active substances of Citrus grandis "Tomentosa" were not elucidated. Rhoifolin (ROF) is a flavonoid component present in high levels. Therefore, this research aimed to evaluate the hepatoprotective effects of ROF and its possible mechanisms. Methods: Molecular docking was performed to analyze the binding energy of ROF to the main target proteins related to ALD. Subsequently, mice were fed ethanol (ETH) for 49 days to establish the chronic alcoholic liver injury models. The liver pathological injury, serum aminotransferase levels, and oxidative stress levels in the liver tissue were measured. Human normal hepatocytes (LO2 cells) were incubated with ETH to construct the alcoholic liver cell model. The inflammatory markers and apoptosis factors were evaluated using real-time PCR and flow cytometry. Finally, the effects of ROF on the CYP2E1 and NF-κB signaling pathways were tested in vitro and in vivo. Results: Molecular docking results demonstrated that ROF was able to successfully dock with the target proteins associated with ALD. In animal studies, ROF attenuated ETH-induced liver damage in mice by decreasing the serum concentrations of AST and ALT, reducing the expression of inflammatory cytokines, and maintaining antioxidant balance in the liver tissue. The in vitro experiments demonstrated that ROF suppressed ETH-induced apoptosis in LO2 cells by promoting Bcl-2 mRNA and inhibiting Bax mRNA and caspase 3 protein expression. ROF decreased the level of LDH, ALT, AST, ROS, and MDA in the supernatant; induced the activity of GSH and SOD; and inhibited TNF-α, IL-6, and IL-1ß expression levels. Mechanistically, ROF could significantly downregulate the expression levels of CYP2E1, TLR4, and NF-κB phosphorylation. Conclusion: This study indicates that ROF is the active component within the total flavonoids, which may alleviate ETH-induced liver injury by inhibiting NF-κB phosphorylation. Therefore, ROF may serve as a promising compound for treating ALD.

The Loss of Raw Material Criticality: Implications of the Collapse of Saudi Arabian Oil Exports.

Raw material criticality has played an important role in geostrategic thinking, especially since the crisis surrounding the price and supply of rare earths at the beginning of the 2010s. However, once dependency and strategic importance grow too strong, substitution efforts will take place that could reduce or even eradicate the previous criticality. Critical resources rarely become obsolete very quickly. However, this could happen in the case of crude oil because climate policy is forcing defossilisation, but also because artificial scarcity is falling as a result of geostrategic rivalries that are causing oversupply. This article analyses this process and the possible consequences using Saudi Arabia as an example. The development of a green hydrogen industry has potential, but it should not be overestimated in view of the absorption capacity of the economy.