Activated carbon from Camellia oleifera shells for adsorption of Y(iii): experimental and DFT studies.

Yttrium is an important rare earth element and is widely used in fields such as special glass preparation, metallurgy, and materials science. However, it is difficult to recover yttrium ion waste from dilute solutions with traditional processes, resulting in a significant waste of rare earth resources. The simple, effective, and easy-to-operate adsorption method is the most promising method for recovering yttrium, which is of great significance for sustainable development of the rare earth industry. In this study, activated carbon was prepared from Camellia oleifera fruit shells (COS) using phosphoric acid activation, and efficient recovery of Y(iii) from the Camellia oleifera fruit shell activated carbon was studied. Adsorption equilibrium data showed that this activated carbon had a Y(iii) adsorption capacity of 35.41 mg g-1, indicating significant potential for recovery of yttrium ions. The adsorption of Y(iii) by the activated carbon prepared from COS was consistent with the Langmuir model, and the adsorption data were consistent with the pseudo second-order kinetic model, indicating that the adsorption process was primarily chemical adsorption. After adsorption, the surface of the activated carbon contained large amounts of N, O, and Y, indicating that Y(iii) was stably adsorbed. The mechanisms for adsorption of Y(iii) on three types of activated carbon were studied through DFT calculations. The results showed that Y(iii) interacted with the carbon atoms on the surfaces to form new chemical bonds. The yttrium ion adsorption capacities for the three different activated carbons decreased in the order C I > C II > C.

Polysulfonated covalent organic framework as active electrode host for mobile cation guests in electrochemical soft actuator.

Tailoring transfer dynamics of mobile cations across solid-state electrolyte-electrode interfaces is crucial for high-performance electrochemical soft actuators. In general, actuation performance is directly proportional to the affinity of cations and anions in the electrolyte for the opposite electrode surfaces under an applied field. Herein, to maximize electrochemical actuation, we report an electronically conjugated polysulfonated covalent organic framework (pS-COF) used as a common electrolyte-electrode host for 1-ethyl-3-methylimidazolium cation embedded into a Nafion membrane. The pS-COF-based electrochemical actuator exhibits remarkable bending deflection at near-zero voltage (~0.01 V) and previously unattainable blocking force, which is 34 times higher than its own weight. The ultrafast step response shows a very short rising time of 1.59 seconds without back-relaxation, and substantial ultralow-voltage actuation at higher frequencies up to 5.0 hertz demonstrates good application prospects of common electrolyte-electrode hosts. A soft fluidic switch is constructed using the proposed soft actuator as a potential engineering application.

Joint Entity and Relation Extraction With Set Prediction Networks.

Joint entity and relation extraction is an important task in natural language processing, which aims to extract all relational triples mentioned in a given sentence. In essence, the relational triples mentioned in a sentence are in the form of a set, which has no intrinsic order between elements and exhibits the permutation invariant feature. However, previous seq2seq-based models require sorting the set of relational triples into a sequence beforehand with some heuristic global rules, which destroys the natural set structure. In order to break this bottleneck, we treat joint entity and relation extraction as a direct set prediction problem, so that the extraction model is not burdened with predicting the order of multiple triples. To solve this set prediction problem, we propose networks featured by transformers with non-autoregressive parallel decoding. In contrast to autoregressive approaches that generate triples one by one in a specific order, the proposed networks are able to directly output the final set of relational triples in one shot. Furthermore, we also design a set-based loss that forces unique predictions through bipartite matching. Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities. Various experiments on two benchmark datasets demonstrate that our proposed model significantly outperforms the current state-of-the-art (SoTA) models. Training code and trained models are now publicly available at http://github.com/DianboWork/SPN4RE.

TLR4 involved in immune response against Vibrio Parahaemolyticus by MyD88-dependent pathway in Crassostrea hongkongensis.

Vibrio parahaemolyticus (V. parahaemolyticus) is a salt-loving gram-negative bacterium, and is the leading cause of mortality in cultured shellfish in recent years. Toll-like Receptor 4 (TLR4) is a classical pattern recognition receptor (PRRs) that recognizes pathogen-associated molecular patterns (PAMPs) of pathogenic microorganism and activates the immune response. However, the function and signal pathway of TLR4 in oyster are still unknown. In this study, a new TLR4 gene was identified from the Crassostrea hongkongensis (C. hongkongensis). The ChTLR4 contained an open reading frame of 2643 bp, encoding 880 amino acids with seven leucine-rich repeat (LRR) domains and a Toll/IL-1R (TIR) domain. The ChTLR4 shared the highest sequence identity (83.0%) with TLR4 of Crassostrea gigas. Tissue expression analysis revealed that ChTLR4 showed the highest constitutive expression in the gill and hepatopancreas, and was significantly upregulated in immune tissues post V. parahaemolyticus infection, especially in gill and hemocytes. Moreover, TLR4 silencing significantly inhibited the immune-enzyme activities, including SOD, CAT, ACP, AKP in gill and LZM in hemolymph supernatant, and increased MDA content in hemolymph supernatant. Meanwhile, the antimicrobial activities of the hemolymph supernatant were also significantly inhibited by TLR4 silencing. These data demonstrated that the ChTLR4 involved in innate immune response of C. hongkongensis against V. parahaemolyticus challenge. Finally, qRT-PCR analysis showed that ChTLR4 silencing clearly inhibited the expression of genes in TLR4-MyD88 pathway, indicating that MyD88-dependent pathway played a crucial role in ChTLR4-mediated immune response against V. parahaemolyticus.

Hip Preservation or Total Hip Arthroplasty? A Retrospective Case-Control Study of Factors Influencing Arthroplasty Decision-Making for Patients with Osteonecrosis of the Femoral Head in China.

OBJECTIVE: At present, there is no consensus or guidance on indications for osteonecrosis of the femoral head (ONFH) patients to receive hip arthroplasty (THA) treatment. This study aims to explore the factors that influence the decision-making for THA in patients with ONFH, and to provide references for clinical decision for ONFH patients to be indicated for THA or hip preservation. METHODS: This retrospective case-control study involved data for ONFH patients from July 2016 to October 2021 from the China Osteonecrosis of the Femoral Head Database (CONFHD). The patients with ONFH, and unilateral hip affected at the first visit were divided into THA group and non-THA group according to if they had undergone THA treatment. The differences between the two groups of patients in terms of gender, age at the time of consultation, body mass index (BMI), etiology, onset side, association research circulation osseous (ARCO) stage, hip joint function, visual analog scale (VAS), etc. were analyzed. Multivariate binomial logistic regression analysis was then applied to evaluate the risk factors of ONFH patients who underwent THA during the first visit. RESULTS: A total of 640 patients were recruited for analysis, including 209 cases from the THA group and 431 cases from the non-THA group. The results of univariate analysis showed that the two groups of patients were significantly different in the following six indicators: age (59 vs. 46, Z = -9.58, p < 0.001), duration of disease (78 vs. 17, Z = -16.14, p < 0.001), gender composition (χ2  = 8.09, p = 0.004), disease etiology (χ2  = 33.04, p < 0.001), ARCO stage (χ2  = 334.86, p < 0.001), flexion of hip joint (χ2  = 172.33, p < 0.001). However, the comparison between the two groups on VAS (Z = -0.82, p = 0.41), BMI (Z = -1.35, p = 0.18), and onset side (χ2  = 1.53, p = 0.22) did not obviously differ. The results regression analysis showed that the age at the time of consultation, duration of disease, ARCO stage, and the hip joint function affected the decision making if the patients should undergo THA. The results of receiver operating characteristic curve (ROC) analysis showed that aforementioned indicators were satisfactory in predicting whether patients with ONFH would be treated with THA. The regression model using the above four indicators as comprehensive indicators has satisfactory performance in predicting whether to perform THA, and the area under the curve (AUC) is 93.94%. CONCLUSION: These factors such as age, duration of disease, ARCO stage, and hip flexion function should be considered comprehensively before making decisions to perform THA or not in our clinical practice.

A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.

BACKGROUND: Most of existing deep learning research in medical image analysis is focused on networks with stronger performance. These networks have achieved success, while their architectures are complex and even contain massive parameters ranging from thousands to millions in numbers. The nature of high dimension and nonconvex makes it easy to train a suboptimal model through the popular stochastic first-order optimizers, which only use gradient information. PURPOSE: Our purpose is to design an adaptive cubic quasi-Newton optimizer, which could help to escape from suboptimal solution and improve the performance of deep neural networks on four medical image analysis tasks including: detection of COVID-19, COVID-19 lung infection segmentation, liver tumor segmentation, optic disc/cup segmentation. METHODS: In this work, we introduce a novel adaptive cubic quasi-Newton optimizer with high-order moment (termed ACQN-H) for medical image analysis. The optimizer dynamically captures the curvature of the loss function by diagonally approximated Hessian and the norm of difference between previous two estimates, which helps to escape from saddle points more efficiently. In addition, to reduce the variance introduced by the stochastic nature of the problem, ACQN-H hires high-order moment through exponential moving average on iteratively calculated approximated Hessian matrix. Extensive experiments are performed to access the performance of ACQN-H. These include detection of COVID-19 using COVID-Net on dataset COVID-chestxray, which contains 16 565 training samples and 1841 test samples; COVID-19 lung infection segmentation using Inf-Net on COVID-CT, which contains 45, 5, and 5 computer tomography (CT) images for training, validation, and testing, respectively; liver tumor segmentation using ResUNet on LiTS2017, which consists of 50 622 abdominal scan images for training and 26 608 images for testing; optic disc/cup segmentation using MRNet on RIGA, which has 655 color fundus images for training and 95 for testing. The results are compared with commonly used stochastic first-order optimizers such as Adam, SGD, and AdaBound, and recently proposed stochastic quasi-Newton optimizer Apollo. In task detection of COVID-19, we use classification accuracy as the evaluation metric. For the other three medical image segmentation tasks, seven commonly used evaluation metrics are utilized, that is, Dice, structure measure, enhanced-alignment measure (EM), mean absolute error (MAE), intersection over union (IoU), true positive rate (TPR), and true negative rate. RESULTS: Experiments on four tasks show that ACQN-H achieves improvements over other stochastic optimizers: (1) comparing with AdaBound, ACQN-H achieves 0.49%, 0.11%, and 0.70% higher accuracy on the COVID-chestxray dataset using network COVID-Net with VGG16, ResNet50 and DenseNet121 as backbones, respectively; (2) ACQN-H has the best scores in terms of evaluation metrics Dice, TPR, EM, and MAE on COVID-CT dataset using network Inf-Net. Particularly, ACQN-H achieves 1.0% better Dice as compared to Apollo; (3) ACQN-H achieves the best results on LiTS2017 dataset using network ResUNet, and outperforms Adam in terms of Dice by 2.3%; (4) ACQN-H improves the performance of network MRNet on RIGA dataset, and achieves 0.5% and 1.0% better scores on cup segmentation for Dice and IoU, respectively, compared with SGD. We also present fivefold validation results of four tasks. It can be found that the results on detection of COVID-19, liver tumor segmentation and optic disc/cup segmentation can achieve high performance with low variance. For COVID-19 lung infection segmentation, the variance on test set is much larger than on validation set, which may due to small size of dataset. CONCLUSIONS: The proposed optimizer ACQN-H has been validated on four medical image analysis tasks including: detection of COVID-19 using COVID-Net on COVID-chestxray, COVID-19 lung infection segmentation using Inf-Net on COVID-CT, liver tumor segmentation using ResUNet on LiTS2017, optic disc/cup segmentation using MRNet on RIGA. Experiments show that ACQN-H can achieve some performance improvement. Moreover, the work is expected to boost the performance of existing deep learning networks in medical image analysis.

Synthesis of Micro-Electrolysis Composite Materials from Blast Furnace Dust and Application into Organic Pollutant Degradation.

A micro-electrolysis material (MEM) was successfully prepared from carbothermal reduction of blast furnace dust (BFD) and coke as raw materials in a nitrogen atmosphere. The MEM prepared from BFD had strong ability in removing methyl orange, methylene blue, and rose bengal (the removal rates of methyl orange and methylene blue were close to 100%). X-ray diffraction showed that the iron mineral in BFD was ferric oxide, which was converted to zero-valent iron after being reduced by calcination. Scanning electron microscopy showed that nano-scale zero-valent iron particles were formed in the MEM. In short, the MEM prepared from BFD can effectively degrade organic pollutants.

AdaCN: An Adaptive Cubic Newton Method for Nonconvex Stochastic Optimization.

In this work, we introduce AdaCN, a novel adaptive cubic Newton method for nonconvex stochastic optimization. AdaCN dynamically captures the curvature of the loss landscape by diagonally approximated Hessian plus the norm of difference between previous two estimates. It only requires at most first order gradients and updates with linear complexity for both time and memory. In order to reduce the variance introduced by the stochastic nature of the problem, AdaCN hires the first and second moment to implement and exponential moving average on iteratively updated stochastic gradients and approximated stochastic Hessians, respectively. We validate AdaCN in extensive experiments, showing that it outperforms other stochastic first order methods (including SGD, Adam, and AdaBound) and stochastic quasi-Newton method (i.e., Apollo), in terms of both convergence speed and generalization performance.

Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion.

BACKGROUND: Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. METHODS: To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. RESULTS: Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. CONCLUSIONS: In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.

A Novel Low-Bit Quantization Strategy for Compressing Deep Neural Networks.

The increase in sophistication of neural network models in recent years has exponentially expanded memory consumption and computational cost, thereby hindering their applications on ASIC, FPGA, and other mobile devices. Therefore, compressing and accelerating the neural networks are necessary. In this study, we introduce a novel strategy to train low-bit networks with weights and activations quantized by several bits and address two corresponding fundamental issues. One is to approximate activations through low-bit discretization for decreasing network computational cost and dot-product memory. The other is to specify weight quantization and update mechanism for discrete weights to avoid gradient mismatch. With quantized low-bit weights and activations, the costly full-precision operation will be replaced by shift operation. We evaluate the proposed method on common datasets, and results show that this method can dramatically compress the neural network with slight accuracy loss.