A nomogram based on contrast-enhanced ultrasound for evaluating the glomerulosclerosis rate in transplanted kidneys.

Background: A high rate of glomerulosclerosis serves as an important signal of poor response to treatment and a high risk of disease progression or adverse prognosis in transplanted kidneys. We hypothesized that contrast-enhanced ultrasound (CEUS) could serve as a novel imaging biomarker in the early prediction of glomerulosclerosis rate by evaluating renal allograft microcirculation. Methods: A retrospective analysis was performed on 143 transplanted kidney recipients with confirmed pathology, including 100 in the training group and 43 in the validation group. All patients underwent conventional ultrasound (CUS) and CEUS examinations. The patients were divided into two groups: those with >50% glomerulosclerosis and those with ≤50% glomerulosclerosis. The nomograms derived from independent predictors identified by multivariate logistic analysis were assessed using receiver operating characteristic (ROC) curve analysis, 1,000 bootstrap resamples, calibration curves, and decision curve analysis (DCA). Results: The patients with >50% glomerulosclerosis and those with ≤50% glomerulosclerosis showed statistically significant differences in CEUS parameters, including in peak intensity (PI) (25 vs. 30; P<0.001), absolute time to peak (ATTP) (10 vs. 9; P=0.004), and time to peak (TTP) (22 vs. 19.5; P=0.026). Multivariate analysis revealed that PI [odds ratio (OR) =0.852; 95% confidence interval (CI): 0.737-0.986], peak systolic velocity (PSV) of the interlobar artery (OR =0.850; 95% CI: 0.758-0.954), cortical echogenicity (OR =38.429; 95% CI: 3.695-399.641), and time since transplantation (OR =1.017; 95% CI: 1.006-1.028) were independent predictors of whether the glomerulosclerosis rate was >50% and were incorporated into the construction of a nomogram. The area under the curve (AUC) of the nomogram in the training and validation groups was 0.914 (95% CI: 0.840-0.960) and 0.909 (95% CI: 0.781-0.975), respectively, with a bootstrap resampling AUC of 0.877. The calibration curve and DCA confirmed the diagnostic performance of the nomogram model. Conclusions: The nomogram, which combined CUS, CEUS, and clinical indicators, exhibited notable predictive efficacy for the glomerulosclerosis rate in transplanted kidneys, thereby demonstrating the potential to improve clinical decision-making.

Environmentally Friendly Synthesis of Highly Substituted Phenols Using Enallenoates and Grignard Reagents.

We developed an efficient and environmentally friendly methodology for selectively synthesizing highly substituted phenols using readily available enallenoates and Grignard reagents. This method consistently yields good to excellent results across over 60 examples, demonstrating the substrate scope and the exploration of phenol product derivatization, further extending the method's utility.

Enhancing biomass and lipid accumulation by a novel microalga for unsterilized piggery biogas slurry remediation.

The cost and efficiency of an algal-BS treatment system are determined by the specific microalgal species and BS pretreatment method. This study examines the growth of a novel algae Chlorella sp. YSD-2 and the removal of nutrients from the BS using different pretreatment methods, including dilution ratio and sterilization. The highest biomass production (1.84 g L-1) was achieved in the 1:2 unsterilized biogas slurry, which was 2.03 times higher than that in the sterilized group, as well as higher lipid productivity (17.29 mg L-1 d-1). Nevertheless, the sterilized biogas slurry at a 1:1 dilution ratio exhibited the most notable nutrient-removal efficiency, with COD at 71.97%, TP at 91.32%, and TN at 88.80%. Additionally, the analysis of 16S rRNA sequencing revealed a significant alteration in the indigenous bacterial composition of the biogas slurry by microalgal treatment, with Proteobacteria and Cyanobacteria emerging as the predominant phyla, and unidentified_Cyanobacteria as the primary genus. These findings suggest that Chlorella sp. YSD-2 exhibits favorable tolerance and nutrient-removal capabilities in unsterilized, high-strength biogas slurry, along with high productivity of biomass and lipids. Consequently, these results offer a theoretical foundation for the development of an efficient and economically viable treatment method for algal-BS.

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration-a bibliometrics and visualization analysis.

Background: Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions. Methods: We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration. Results: A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas. Conclusion: We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.

Cuproptosis in glioblastoma: unveiling a novel prognostic model and therapeutic potential.

Glioblastoma, a notably aggressive brain tumor, is characterized by a brief survival period and resistance to conventional therapeutic approaches. With the recent identification of "Cuproptosis," a copper-dependent apoptosis mechanism, this study aimed to explore its role in glioblastoma prognosis and potential therapeutic implications. A comprehensive methodology was employed, starting with the identification and analysis of 65 cuproptosis-related genes. These genes were subjected to differential expression analyses between glioblastoma tissues and normal counterparts. A novel metric, the "CP-score," was devised to quantify the cuproptosis response in glioblastoma patients. Building on this, a prognostic model, the CP-model, was developed using Cox regression techniques, designed to operate on both bulk and single-cell data. The differential expression analysis revealed 31 genes with distinct expression patterns in glioblastoma. The CP-score was markedly elevated in glioblastoma patients, suggesting an intensified cuproptosis response. The CP-model adeptly stratified patients into distinct risk categories, unveiling intricate associations between glioblastoma prognosis, immune response pathways, and the tumor's immunological environment. Further analyses indicated that high-risk patients, as per the CP-model, exhibited heightened expression of certain immune checkpoints, suggesting potential therapeutic targets. Additionally, the model hinted at the possibility of personalized therapeutic strategies, with certain drugs showing increased efficacy in high-risk patients. The CP-model offers a promising tool for glioblastoma prognosis and therapeutic strategy development, emphasizing the potential of Cuproptosis in cancer treatment.

Eye blinks as a visual processing stage.

Humans blink their eyes frequently during normal viewing, more often than it seems necessary for keeping the cornea well lubricated. Since the closure of the eyelid disrupts the image on the retina, eye blinks are commonly assumed to be detrimental to visual processing. However, blinks also provide luminance transients rich in spatial information to neural pathways highly sensitive to temporal changes. Here, we report that the luminance modulations from blinks enhance visual sensitivity. By coupling high-resolution eye tracking in human observers with modeling of blink transients and spectral analysis of visual input signals, we show that blinking increases the power of retinal stimulation and that this effect significantly enhances visibility despite the time lost in exposure to the external scene. We further show that, as predicted from the spectral content of input signals, this enhancement is selective for stimuli at low spatial frequencies and occurs irrespective of whether the luminance transients are actively generated or passively experienced. These findings indicate that, like eye movements, blinking acts as a computational component of a visual processing strategy that uses motor behavior to reformat spatial information into the temporal domain.

Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy.

PURPOSE: Some individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data. METHODS: The study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms. RESULTS: Among the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features. CONCLUSIONS: Considering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.

A neural network potential energy surface assisted molecular dynamics study on the pyrolysis behavior of two spiro-hydrocarbons.

Spiro-hydrocarbons are potentially a type of novel alternative jet fuel due to their high density and net heat of combustion. In this work, the pyrolysis study of two spiro-hydrocarbons (spiro[cyclopropane-1,6'-tricyclo[3.2.1.02,4]octane] (C10H14) as Fuel 1 and spiro[bicyclo[2.2.1]heptane-2,1'-cyclopropane] (C9H14) as Fuel 2) is performed via molecular dynamics (MD) simulations, with a neural network potential energy surface (NNPES), deep potential (DP) model, adopted. The data set for the DP model of each fuel is constructed after 31 and 27 iterations, respectively. The high precision of the DP model is demonstrated, and the temperature transferability of each model is observed. The overall pyrolysis performance is evaluated with the fuel decomposition rate, showing that both fuels have comparable gas-reactivity to commercial aviation fuels, such as JP-10. The reaction networks of initial pyrolysis for Fuels 1 and 2 are constructed, and the contribution of each pathway is discussed. Fuel 1 tends to form an unsaturated six-membered ring structure, while Fuel 2 generates unsaturated open-chain hydrocarbons. Further analyses of the MD results provide time-evolution information on each component in the pyrolysis species pool. Compared to Fuel 1, the initial pyrolysis of Fuel 2 leads to more hydrogen, alkenes, and alkanes, as well as fewer monocyclic aromatic hydrocarbons (MAHs), demonstrating a reduced tendency for afterward coking. This work might contribute to the development of the mechanism of the two spiro-hydrocarbons and guide the research of other similar structural fuels.

Single-photon avalanche diodes dynamic range and linear response enhancement by conditional probability correction.

Detectors based on single-photon avalanche diodes (SPADs) operating in free-running mode surfer from distorted detection signals due to the impact of afterpulse, dead time, and the non-linear detection efficiency response. This study presents a correction method based on conditional probability. In the experiments with high temporal resolution and huge dynamic range conditions, this method's residual sum of squares is near 68 times smaller than the uncorrected received data of SPAD and near 50 times smaller than deconvolution method. This method is applied to polarization lidar and CO2 lidar, and the performance shows significant improvement. This method effectively mitigates the impact of SPAD afterpulse, dead time, and detection efficiency non-linear response, making it suitable for all SPADs. Especially, our method is primarily employed for atmospheric detection.

Remarkable emission enhancement of CsPbBr3 quantum dots based on an Ag nanoparticle-Ag film plasmonic coupling structure.

All-inorganic halide perovskite quantum dots (QDs) have recently received much attention due to their excellent optoelectronic properties. And their emission properties still need to be improved for further applications. Here, we demonstrated a remarkable emission enhancement of the CsPbBr3 QDs based on an Ag nanoparticle-Ag film plasmonic coupling structure. Through precise control of the gap distance between Ag nanoparticle and Ag film, the localized surface plasmon resonance (LSPR) peak was tuned to match the emission wavelength of the CsPbBr3 QDs. We achieved a 30-fold fluorescence intensity enhancement and a lower lasing threshold, which is 25% of that of the CsPbBr3 QDs without plasmonic coupling structure. It is attributed to that the plasmonic coupling structure exhibits an extremely strong local electric field owing to the coupling between LSPR of Ag nanoparticle and surface plasmon polariton of Ag film. This work provides an effective way to enhance the optical emission of perovskite QDs and promotes the further exploration of on-chip light source.

Characterization of BCP/PreC/C region quasispecies in treatment-naive patients with different phases of HBV infection using next-generation sequencing.

BACKGROUND: To analysis of quasispecies (QS) changes and high-frequency mutations in the BCP/PreC/C region of patients at different phases of hepatitis B virus (HBV) infection and provides novel biomarkers for the diagnosis of chronic hepatitis B (CHB) patients. METHODS: With the application of next-generation sequencing technology, we were able to sequence the HBV BCP/PreC/C regions in 40 patients, each at different phases of the HBV infection. The heterogeneity of QS and the frequency of mutations were calculated using MEGA 7 software. RESULTS: Our results show that the complexity and diversity of the BCP/PreC/C QS in HBeAg-positive CHB patients are significantly higher than those in HBeAg-positive chronic infection patients, while HBeAg-negative chronic infection patients had significantly higher QS complexity and diversity than HBeAg-negative CHB patients. In addition, HBeAg-negative patients showed reduced complexity but increased diversity compared with HBeAg-positive patients. Receiver operating characteristic curves showed that G1764A, C2102T, dN and complexity of QS could be used as potential biomarkers for diagnosing HBeAg-positive CHB, while the A2189C, dS and complexity of QS could be used as potential biomarkers for diagnosing HBeAg-negative chronic hepatitis. Finally, our study also found that G1896A and A2159G may be hotspot mutations affecting HBeAg seroconversion. CONCLUSION: Our research elucidates the evolution of HBV by analyzing QS heterogeneity and mutation patterns, offering novel serum biomarkers for enhancing clinical diagnosis and disease prognosis. This comprehensive approach sheds light on the intricate dynamics of HBV progression and paves the way for more precise medical interventions.

Copper Ion-Inspired Dual Controllable Drug Release Hydrogels for Wound Management: Driven by Hydrogen Bonds.

Bacterial infections and inflammation progression yield huge trouble for the management of serious skin wounds and burns. However, some hydrogel dressing exhibit poor wound-healing capabilities. Additionally, little information is given on the molecular theory of hydrogel gelation mechanisms and drug release performance from drug-polymer network in the water environment. Herein, cationic guar gum (CG) is first mixed with dipotassium glycyrrhizinate (DG), and then crosslinked Cu2+ to strengthen the mechanical strength followed by encapsulating mussel adhesive protein (MAP) as composite dressings. Intriguingly, CG-Cu2+ 0.5-DG10 possessed proper rheological properties and mechanical strength predominantly driven by strong CG-H2O-Cu2+ and Cu2+-CG hydrogen bonding interaction. Weak DG-CG hydrogen bonding only controlled DG release in the initial 4 h, while strong hydrogen bonding is the main force regulating the sustained release of Cu2+ within 48 h. The incorporation of MAP further loosened the tight crosslinking of CG-Cu2+ 0.5-DG10. The screened CG-Cu2+ 0.5-DG10/MAP possessed excellent self-healing, injectability, antibacterial, anti-inflammatory, cell proliferation-promotion activities with high biocompatibility. Therefore, CG-Cu2+ 0.5-DG10/MAP hydrogel expedited wound closure on S. aureus-infected full-thickness skin wound model and lowered necrosis progression to the unburned interspaces on a rat burn model. The results highlight the promising translational potential of Cu2+-inspired hydrogels for the management of burns and infected wounds.

Comprehensive prognostic and immune analysis of sterol O-acyltransferase 1 in patients with hepatocellular carcinoma.

BACKGROUND: Sterol O-acyltransferase 1 (SOAT1) is an important target in the diagnosis and treatment of liver cancer. However, the prognostic value of SOAT1 in patients with hepatocellular carcinoma (HCC) is still not clear. AIM: To investigate the correlation of SOAT1 expression with HCC, using RNA-seq and gene expression data of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) and pan-cancer. METHODS: The correlation between SOAT1 expression and HCC was analyzed. Cox hazard regression models were conducted to investigate the prognostic value of SOAT1 in HCC. Overall survival and disease-specific survival were explored based on TCGA-LIHC data. Biological processes and functional pathways mediated by SOAT1 were characterized by gene ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes. In addition, the protein-protein interaction network and co-expression analyses of SOAT1 in HCC were performed to better understand the regulatory mechanisms of SOAT1 in this malignancy. RESULTS: SOAT1 and SOAT2 were highly expressed in unpaired samples, while only SOAT1 was highly expressed in paired samples. The area under the receiver operating characteristic curve of SOAT1 expression in tumor samples from LIHC patients compared with para-carcinoma tissues was 0.748, while the area under the curve of SOAT1 expression in tumor samples from LIHC patients compared with GTEx was 0.676. Patients with higher SOAT1 expression had lower survival rates. Results from GO/KEGG and gene set enrichment analyses suggested that the PI3K/AKT signaling pathway, the IL-18 signaling pathway, the calcium signaling pathway, secreted factors, the Wnt signaling pathway, the Jak/STAT signaling pathway, the MAPK family signaling pathway, and cell-cell communication were involved in such association. SOAT1 expression was positively associated with the abundance of macrophages, Th2 cells, T helper cells, CD56bright natural killer cells, and Th1 cells, and negatively linked to the abundance of Th17 cells, dendritic cells, and cytotoxic cells. CONCLUSION: Our findings demonstrate that SOAT1 may serve as a novel target for HCC treatment, which is helpful for the development of new strategies for immunotherapy and metabolic therapy.

Dauricine attenuates ovariectomized-induced bone loss and RANKL-induced osteoclastogenesis via inhibiting ROS-mediated NF-κB and NFATc1 activity.

BACKGROUND: Osteoclast plays an important role in maintaining the balance between bone anabolism and bone catabolism. The abnormality of osteoclast is closely related to osteolytic bone diseases such as osteoporosis, rheumatoid arthritis and tumor bone metastasis. PURPOSE: We aim to search for natural compound that may suppress osteoclast formation and function. STUDY DESIGN: In this study, we assessed the impact of Dauricine (Dau) on the formation and function of osteoclasts in vitro, as well as its potential in preventing bone loss in an ovariectomy mouse model in vivo. METHODS: Multiple in vitro experiments were carried out, including osteoclastogenesis, podosomal belt formation, bone resorption assay, RNA-sequencing, real-time quantitative PCR, ROS level detection, surface plasmon resonance assay, luciferase assay and western blot. To verify the effect in vivo, an ovariectomized mouse model (OVX model) was constructed, and bone parameters were measured using micro-CT and histology. Furthermore, metabolomics analysis was performed on blood serum samples from the OVX model. RESULTS: In vitro experiments demonstrated that Dau inhibits RANKL-induced osteoclastogenesis, podosomal belt formation, and bone resorption function. RNA-sequencing results revealed that Dau significantly suppresses genes related to osteoclast. Functional enrichment analysis indicated that Dau's inhibition of osteoclasts may be associated with NF-κB signaling pathway and reactive oxygen metabolism pathway. Molecular docking, surface plasmon resonance assay and western blot analysis further confirmed that Dau inhibits RANKL-induced osteoclastogenesis by modulating the ROS/NF-κB/NFATc1 pathway. Moreover, administration of Dau to OVX-induced mice validated its efficacy in treating bone loss disease. CONCLUSION: Dau prevents OVX-induced bone loss by inhibiting osteoclast activity and bone resorption, potentially offering a new approach for preventing and treating metabolic bone diseases such as osteoporosis. This study provides innovative insights into the inhibitory effects of Dau in an in vivo OVX model and elucidates the underlying mechanism.

Engineering Nanoplatforms for Theranostics of Atherosclerotic Plaques.

Atherosclerotic plaque formation is considered the primary pathological mechanism underlying atherosclerotic cardiovascular diseases, leading to severe cardiovascular events such as stroke, acute coronary syndromes, and even sudden cardiac death. Early detection and timely intervention of plaques are challenging due to the lack of typical symptoms in the initial stages. Therefore, precise early detection and intervention play a crucial role in risk stratification of atherosclerotic plaques and achieving favorable post-interventional outcomes. The continuously advancing nanoplatforms have demonstrated numerous advantages including high signal-to-noise ratio, enhanced bioavailability, and specific targeting capabilities for imaging agents and therapeutic drugs, enabling effective visualization and management of atherosclerotic plaques. Motivated by these superior properties, various noninvasive imaging modalities for early recognition of plaques in the preliminary stage of atherosclerosis are comprehensively summarized. Additionally, several therapeutic strategies are proposed to enhance the efficacy of treating atherosclerotic plaques. Finally, existing challenges and promising prospects for accelerating clinical translation of nanoplatform-based molecular imaging and therapy for atherosclerotic plaques are discussed. In conclusion, this review provides an insightful perspective on the diagnosis and therapy of atherosclerotic plaques.

MACTFusion: Lightweight Cross Transformer for Adaptive Multimodal Medical Image Fusion.

Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there are limitations for Transformers in image fusion, such as lacks of local feature extraction and cross-modal feature interaction, resulting in insufficient multimodal feature extraction and integration. In addition, the computational cost of Transformers is higher. To address these challenges, in this work, we develop an adaptive cross-modal fusion strategy for unsupervised multimodal medical image fusion. Specifically, we propose a novel lightweight cross Transformer based on cross multi-axis attention mechanism. It includes cross-window attention and cross-grid attention to mine and integrate both local and global interactions of multimodal features. The cross Transformer is further guided by a spatial adaptation fusion module, which allows the model to focus on the most relevant information. Moreover, we design a special feature extraction module that combines multiple gradient residual dense convolutional and Transformer layers to obtain local features from coarse to fine and capture global features. The proposed strategy significantly boosts the fusion performance while minimizing computational costs. Extensive experiments, including clinical brain tumor image fusion, have shown that our model can achieve clearer texture details and better visual quality than other state-of-the-art fusion methods.

Dissecting the shared genetic landscape of anxiety, depression, and schizophrenia.

BACKGROUND: Numerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies. METHODS: We utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein-protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets. RESULTS: We found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data. CONCLUSIONS: Our study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.

Accelerating Oxygen Electrocatalysis Kinetics on Metal-Organic Frameworks via Bond Length Optimization.

Metal-organic frameworks (MOFs) have been developed as an ideal platform for exploration of the relationship between intrinsic structure and catalytic activity, but the limited catalytic activity and stability has hampered their practical use in water splitting. Herein, we develop a bond length adjustment strategy for optimizing naphthalene-based MOFs that synthesized by acid etching Co-naphthalenedicarboxylic acid-based MOFs (donated as AE-CoNDA) to serve as efficient catalyst for water splitting. AE-CoNDA exhibits a low overpotential of 260 mV to reach 10 mA cm-2 and a small Tafel slope of 62 mV dec-1 with excellent stability over 100 h. After integrated AE-CoNDA onto BiVO4, photocurrent density of 4.3 mA cm-2 is achieved at 1.23 V. Experimental investigations demonstrate that the stretched Co-O bond length was found to optimize the orbitals hybridization of Co 3d and O 2p, which accounts for the fast kinetics and high activity. Theoretical calculations reveal that the stretched Co-O bond length strengthens the adsorption of oxygen-contained intermediates at the Co active sites for highly efficient water splitting.

Short-term forecasting approach of single well production based on multi-intelligent agent hybrid model.

The short-term prediction of single well production can provide direct data support for timely guiding the optimization and adjustment of oil well production parameters and studying and judging oil well production conditions. In view of the coupling effect of complex factors on the daily output of a single well, a short-term prediction method based on a multi-agent hybrid model is proposed, and a short-term prediction process of single well output is constructed. First, CEEMDAN method is used to decompose and reconstruct the original data set, and the sliding window method is used to compose the data set with the obtained components. Features of components by decomposition are described as feature vectors based on values of fuzzy entropy and autocorrelation coefficient, through which those components are divided into two groups using cluster algorithm for prediction with two sub models. Optimized online sequential extreme learning machine and the deep learning model based on encoder-decoder structure using self-attention are developed as sub models to predict the grouped data, and the final predicted production comes from the sum of prediction values by sub models. The validity of this method for short-term production prediction of single well daily oil production is verified. The statistical value of data deviation and statistical test methods are introduced as the basis for comparative evaluation, and comparative models are used as the reference model to evaluate the prediction effect of the above multi-agent hybrid model. Results indicated that the proposed hybrid model has performed better with MAE value of 0.0935, 0.0694 and 0.0593 in three cases, respectively. By comparison, the short-term prediction method of single well production based on multi-agent hybrid model has considerably improved the statistical value of prediction deviation of selected oil well data in different periods. Through statistical test, the multi-agent hybrid model is superior to the comparative models. Therefore, the short-term prediction method of single well production based on a multi-agent hybrid model can effectively optimize oilfield production parameters and study and judge oil well production conditions.

One Stone Several Birds: Self-Localizing Submicrocages with Dual Loading Points for Multifunctional Drug Delivery.

As the core index, how to improve bioavailability of loaded cargoes has been a hot topic of drug carriers. In our study, aminated ß-cyclodextrin (ß-CD) as cross-linking points was first integrated into three-dimensional poly(acrylamide-co-acrylonitrile) network to build up a unique submicrocage (466.2± 47.6 nm), featuring upper critical solution temperature (UCST; ∼40 °C), high volume expansion coefficient and excellent biocompatibility. Hereinto hydrophobic ß-elemene was locally loaded in ß-CD with high loading efficiency (8.72%) and encapsulation efficiency (78.60%) through hydrophobic desolvation and host-guest interaction. Above UCST, the release of the loaded ß-elemene was accelerated to 72.87% in 24 hours, together with the enhanced sensitization effect of synergized radiotherapy. Given spontaneous long-lasting delivery, targeted embolization, and post-treatment removal of such UCST-type submicrocage, it is anticipated to provide a novel, facile, efficient and versatile strategy of comprehensive anticancer treatments for high drug bioavailability. This article is protected by copyright. All rights reserved.