Contrastive representation learning on dynamic networks.

Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying excessive attention to network details, and disregarding distinctions between snapshots with larger time intervals, resulting in less robustness for sparse or noisy networks. To alleviate these challenges, this paper proposes a contrastive mechanism for temporal representation learning on dynamic networks, inspired by the success of contrastive learning in visual and static network representation learning. This paper proposes a novel Dynamic Network Contrastive representation Learning (DNCL) model. Specifically, contrast objective functions are constructed using intra-snapshot and inter-snapshot contrasts to capture the network topology, node feature information, and network evolution information, respectively. Rather than estimating or generating ground-truth network features, the proposed approach maximizes mutual information between nodes from different time steps and views generated. The experimental results of link prediction, node classification, and clustering on several real-world and synthetic networks demonstrate the superiority of DNCL over state-of-the-art methods, indicating the effectiveness of the proposed approach for dynamic network representation learning.

Urolithin A exerts a protective effect on lipopolysaccharide-induced acute lung injury by regulating HMGB1-mediated MAPK and NF-κB signaling pathways.

Acute lung injury (ALI) is a severe inflammatory disorder that has a high morbidity and mortality rate. Urolithin A (UA) is reported to have anti-inflammatory and anti-oxidative effects in ALI. However, its molecular mechanisms in ALI remain to be explored. Mice and BEAS-2B cells were administrated with lipopolysaccharide (LPS) to mimic the ALI model in vivo and in vitro. Hematoxylin-eosin (HE) staining was used to detect the pathological injury of lung tissues. The levels of proinflammatory cytokines in bronchoalveolar lavage fluid (BALF) and culture supernatant and the levels of oxidative stress markers in lung tissues were measured using ELISA. DCFH-DA probe was used to assess the reactive oxygen species (ROS) level. TUNEL staining and flow cytometry were performed to determine cell apoptosis. The key targets and pathways were confirmed by immunohistochemistry (IHC) and western blot. UA suppressed the pathologic damage, wet/dry weight ratio, and total protein and inflammatory cells in BALF. UA decreased neutrophil infiltration and proinflammatory cytokines production. UA reduced the level of malondialdehyde (MDA) and increased the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in pulmonary tissues. UA also inhibited cell apoptosis in lung tissues by decreasing Bax expression and increasing Bcl-2 expression. In addition, UA suppressed LPS-induced inflammatory factor production, ROS level, and cell apoptosis in BEAS-2B. Importantly, UA decreased the expression of HMGB1 in LPS-treated mice and BEAS-2B cells. HMGB1 overexpression greatly abrogated the inhibition of UA on inflammation, ROS, and cell apoptosis in LPS-administrated BEAS-2B. Furthermore, UA treatment suppressed the phosphorylated levels of p38, JNK, ERK, and p65 in LPS-administrated mice and BEAS-2B cells. UA alleviated lung inflammation, oxidative stress, and apoptosis in ALI by targeting HMGB1 to inactivate the MAPK/NF-κB signaling, suggesting the potential of UA to treat ALI.

Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis.

BACKGROUND: In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques. METHODS: In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer's convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy. RESULTS: Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively. CONCLUSIONS: Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

Exploring the roles of noncoding RNAs in craniofacial abnormalities: A systematic review.

Congenital craniofacial abnormalities are congenital anomalies of variable expressivity and severity with a recognizable set of abnormalities, which are derived from five identifiable primordial structures. They can occur unilaterally or bilaterally and include various malformations such as cleft lip with/without palate, craniosynostosis, and craniofacial microsomia. To date, the molecular etiology of craniofacial abnormalities is largely unknown. Noncoding RNAs (ncRNAs), including microRNAs, long ncRNAs, circular RNAs and PIWI-interacting RNAs, function as major regulators of cellular epigenetic hallmarks via regulation of various molecular and cellular processes. Recently, aberrant expression of ncRNAs has been implicated in many diseases, including craniofacial abnormalities. Consequently, this review focuses on the role and mechanism of ncRNAs in regulating craniofacial development in the hope of providing clues to identify potential therapeutic targets.

Ultrasonic-Assisted Extraction of Antioxidants from Perilla frutescens Leaves Based on Tailor-Made Deep Eutectic Solvents: Optimization and Antioxidant Activity.

The development of natural antioxidants to replace synthetic compounds is attractive. Perilla frutescens leaves were proven to be rich in antioxidants. The extraction of antioxidants from Perilla leaves via ultrasonic-assisted extraction (UAE) based on choline chloride-based deep eutectic solvents (DESs) was studied. Firstly, several DESs were prepared, and their extraction effects were compared. Secondly, the extraction process was optimized by single-factor experiments and response surface methodology (RSM). Finally, the optimization results were verified and compared with the results of traditional solvent-based UAE. The effects of solvents on the surface cell morphology of Perilla frutescens leaves were characterized by scanning electron microscopy (SEM). Choline chloride-acetic acid-based DES (ChCl-AcA) extract showed a relatively high ferric-reducing antioxidant activity (FRAP) and 2,2-diphenyl-1-picrylhyldrazyl radical scavenging rate (DPPH). Under the optimal operating conditions (temperature 41 °C, liquid-solid ratio 33:1, ultrasonic time 30 min, water content 25%, ultrasonic power 219 W), the experimental results are as follows: DPPH64.40% and FRAP0.40 mM Fe(II)SE/g DW. The experimental and predicted results were highly consistent with a low error (<3.38%). The values of the DPPH and FRAP were significantly higher than that for the water, ethanol, and butanol-based UAE. SEM analysis confirmed that ChCl-AcA enhanced the destruction of the cell wall, so that more antioxidants were released. This study provides an eco-friendly technology for the efficient extraction of antioxidants from Perilla frutescens leaves. The cytotoxicity and biodegradability of the extract will be further verified in a future work.

Metal-Organic Framework for Hypoxia/ROS/pH Triple-Responsive Cargo Release.

Nanoparticulate antitumor photodynamic therapy (PDT) is suffering from a very short lifetime, limited diffusion distance of reactive oxygen species (ROS). Herein, a hypoxia/ROS/pH triple-responsive metal-organic framework (MOF) is designed to facilitate the on-demand release of photosensitizers and hence enhanced PDT efficacy. Tailored azo-containing imidazole ligand is coordinated with zinc to form MOF where photosensitizer (Chlorin e6/Ce6) is encapsulated. Azo can be reduced by overexpressed azoreductase in hypoxic tumor cells, resulting in depletion of glutathione (GSH) and thioredoxin (Trx) which are major antioxidants against ROS oxidative damage in PDT, resulting in rapid cargo release and additional efficacy amplification. The imidazole ionization causes a proton sponge effect to ensure the disintegration of the nanocarriers in acidic organelles, allowing the rapid release of Ce6 through lysosome escape. Under light irradiation, ROS produced by Ce6 may oxidize imidazole to urea, resulting in rapid cargo release. All of the triggers are expected to show interactive synergism. The pH- and hypoxia-responsiveness can improve the release rate of Ce6 for enhanced PDT therapy, whereas the consumption of oxygen by PDT may induce elevated hypoxia and hence in turn enhanced cargo release. This work highlights the role of triple-responsive nanocarriers for triggered photosensitizer release and improved antitumor PDT efficacy.

Baicalin Exerts a Protective Effect in Diabetic Nephropathy by Repressing Inflammation and Oxidative Stress Through the SphK1/S1P/NF-κB Signaling Pathway.

Background: Inflammation and oxidative stress contribute to the development of diabetic nephropathy (DN). Baicalin (BA) shows renal protection against DN through its anti-inflammatory and anti-oxidant properties. However, the molecular mechanism by which BA exerts the therapeutic effects on DN remains to be investigated. Methods: The db/db mice and high glucose (HG)-induced HK-2 cells were used as the in vivo and in vitro model of DN, respectively. The effects of BA were assessed by detecting the related blood and urine biochemical parameters, kidney histopathology, inflammatory cytokine production, oxidative stress indicators, and apoptosis. Cell viability and apoptosis were detected by CCK-8 assay and TUNEL assay, respectively. Related protein levels were measured by an immunoblotting method. Results: In db/db model mice, BA reduced serum glucose concentration, decreased blood lipid levels, ameliorated kidney functions, and decreased histopathological changes in kidney tissues. BA also alleviated oxidative stress and inflammation in db/db mice. In addition, BA blocked the activation of sphingosine kinases type 1/sphingosine 1-phosphate (SphK1/S1P)/NF-κB pathway in db/db mice. In HK-2 cells, BA hindered HG-induced apoptosis, oxidative stress and inflammation, while overexpression of SphK1 or S1P could reverse these effects. BA alleviated HG-induced apoptosis, oxidative stress and inflammation in HK-2 cells through the S1P/NF-κB pathway. Furthermore, BA blocked the NF-κB signaling by diminishing p65 nuclear translocation via the SphK1/S1P pathway. Conclusion: Our study strongly suggests that BA protects against DN via ameliorating inflammation, oxidative stress and apoptosis through the SphK1/S1P/NF-κB pathway. This study provides a novel insight into the therapeutic effects of BA in DN.

Driving Pressure-Guided Ventilation in Obese Patients Undergoing Laparoscopic Sleeve Gastrectomy: A Randomized Controlled Trial.

Purpose: This study aims to compare the conventional lung protective ventilation strategy (LPVS) with driving pressure-guided ventilation in obese patients undergoing laparoscopic sleeve gastrectomy (LSG). Methods: Forty-five patients undergoing elective LSG under general anesthesia were randomly assigned to the conventional LPVS group (group L) or the driving pressure-guided ventilation group (group D) using random numbers generated by Excel. The primary outcome was the driving pressure of both groups 90 min after pneumoperitoneum. Results: After 30 min of pneumoperitoneum, 90 min of pneumoperitoneum, 10 min of closing the pneumoperitoneum, and restoring the supine position, the driving pressure of group L and group D were 20.0 ± 2.9 cm H2O vs 16.6 ± 3.0 cm H2O (P < 0.001), 20.7 ± 3.2 cm H2O vs 17.3 ± 2.8 cm H2O (P < 0.001), and 16.3 ± 3.1 cm H2O vs 13.3 ± 2.5 cm H2O (P = 0.001), respectively; the respiratory compliance of groups L and D were 23.4 ± 3.7 mL/cm H2O vs 27.6 ± 5.1 mL/cm H2O (P = 0.003), 22.7 ± 3.8 mL/cm H2O vs 26.4 ± 3.5 mL/cm H2O (P = 0.005), and 29.6 ± 6.8 mL/cm H2O vs 34.7 ± 5.3 mL/cm H2O (P = 0.007), respectively. The intraoperative PEEP in groups L and group D was 5 (5-5) cm H2O vs 10 (9-11) cm H2O (P < 0.001). Conclusion: An individualized peep-based driving pressure-guided ventilation strategy can reduce intraoperative driving pressure and increase respiratory compliance in obese patients undergoing LSG.

Representation Learning on Heterostructures via Heterogeneous Anonymous Walks.

Capturing structural similarity has been a hot topic in the field of network embedding (NE) recently due to its great help in understanding node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks, while the related study on heterogeneous networks is still void. In this article, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we first propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and give two more applicable variants. Then, we devise the HAW embedding (HAWE) and its variants in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods and can be applied on large-scale networks.

Identifying fetal status with fetal heart rate: Deep learning approach based on long convolution.

CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fetal heart rate). The rapid development of Artificial Intelligence has led realization of computer-aided CTG technology, Intelligent CTG classification based on FHR is a fundamental component of these technologies. Its implementation can provide doctors with auxiliary decisions. Most of existing FHR classification methods are based on combing different deep learning models, such as CNN (Convolutional Neural Network), LSTM (Long short-term memory) and Transformer. However, these studies ignore the balance of positive and negative samples in dataset and the matching degree between model and FHR classification task, which reduces the classification accuracy. In this paper, we mainly discuss two major problems in previous FHR classification studies: reduce class imbalance and select appropriate convolution kernel. To address above two problems, we propose a data augmentation method based on ECMN (Edge Clipping and Multiscale Noise) to resolve class imbalance. Subsequently, we introduce a one-dimensional long convolutional layer, which use trend area to calculate the appropriate convolution kernel. Based on appropriate convolution kernel, an improved residual structure with attention mechanism named TGLCN (Trend-Guided Long Convolution Network) is proposed to improve FHR classification accuracy. Finally, horizontal and longitudinal experiments show that the TGLCN obtains high classification accuracy and speed of parameter adjustment.

Integrating Network Pharmacology and Experimental Validation to Elucidate the Mechanism of Yiqi Yangyin Decoction in Suppressing Non-Small-Cell Lung Cancer.

Yiqi Yangyin Decoction (YYD) is a classic traditional Chinese medicine (TCM) formulation to treat lung cancer in clinic. Nevertheless, the active ingredients, key targets, and molecular mechanisms for YYD are still poorly understood. This study is focused on elucidating the pharmacological mechanism of YYD in non-small-cell lung cancer (NSCLC) by using a combined network pharmacology approach and biological experiment validation. Online bioinformatics tools showed that 40 bioactive compounds and 229 putative targets of YYD were associated with anti-NSCLC activity. Protein-Protein Interaction (PPI) network demonstrated AKT1, SRC, JUN, TP53, and EGFR as the top five key targets for YYD against NSCLC. Through enrichment analysis, YYD was found to affect cell proliferation and apoptosis in NSCLC possibly by PI3K-AKT signaling. Molecular docking confirmed a strong binding between the main compounds (quercetin or luteolin) and EGFR. As demonstrated by CCK-8, EdU, and colony formation assays, we found a significant inhibition of YYD on cell proliferation. Moreover, YYD treatment induced cell cycle arrest by affecting p53, p21, and cyclin D1 expression. YYD administration enhanced apoptosis by changing the expression of cleaved caspase-3, Bax, and Bcl-2. Mechanistically, YYD resulted in a significant inactivation of EGFR-PI3K-AKT signaling. Furthermore, EGFR activator significantly reversed YYD-mediated proliferation inhibition and apoptosis. YYD also showed an inhibitory effect on tumor growth in mice. Together, YYD might target the EGFR-PI3K-AKT pathway to repress NSCLC progression.

A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism.

Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model's ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.

Out of the Qinghai-Tibet plateau: Genomic biogeography of the alpine monospecific genus Megadenia (Biscutelleae, Brassicaceae).

Numerous high-elevation alpine plants of the Qinghai-Tibet Plateau (QTP) also have disjunct distribution in adjacent low-altitude mountains. The out-of-QTP versus into-the-QTP hypothesis of alpine plants provide strong evidence for the highly disputed assumption of the massive ice sheet developed in the central plateau during the Last Glacial Maximum (LGM). In this study, we sequenced the genomes of most known populations of Megadenia, a monospecific alpine genus of Brassicaceae distributed primarily in the QTP, though rarely found in adjacent low-elevation mountains of north China and Russia (NC-R). All sequenced samples clustered into four geographic genetic groups: one pair was in the QTP and another was in NC-R. The latter pair is nested within the former, and these findings support the out-of-QTP hypothesis. Dating the four genetic groups and niche distribution suggested that Megadenia migrated out of the QTP to adjacent regions during the LGM. The NC-R group showed a decrease in the effective population sizes. In addition, the genes with high genetic divergences in the QTP group were mainly involved in habitat adaptations during low-altitude colonization. These findings reject the hypothesis of development massive ice sheets, and support glacial survival of alpine plants within, as well as further migration out of, the QTP.

HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node Level.

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior. To solve the above-mentioned problem, this article proposes a hierarchical Bayesian dynamic SBM (HB-DSBM) for modeling the node-level and community-level dynamic behavior in a dynamic network synchronously. Based on the SBM, we introduce a hierarchical Dirichlet generative mechanism to associate the global community evolution with the microscopic transition behavior of nodes near-perfectly and generate the observed links across the dynamic networks. Meanwhile, an effective variational inference algorithm is developed and we can easy to infer the communities and dynamic behaviors of the nodes. Furthermore, with the two-level evolution behaviors, it can identify nodes or communities with abnormal behavior. Experiments on simulated and real-world networks demonstrate that HB-DSBM has achieved state-of-the-art performance on community detection and evolution. In addition, abnormal evolutionary behavior and events on dynamic networks can be effectively identified by our model.

Role Discovery-Guided Network Embedding Based on Autoencoder and Attention Mechanism.

Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the methods and models of NE are based on the local, high-order, or global similarity of the networks, and few studies have focused on the role discovery or structural similarity, which is of great significance in spreading dynamics and network theory. Meanwhile, existing NE models for role discovery suffer from two limitations, that is: 1) they fail to model the varying dependencies between each node and its neighbor nodes and 2) they cannot capture the effective node features which are helpful to role discovery, which makes these methods ineffective when applied to the role discovery task. To solve the above problems of NE for role discovery or structural similarity, we propose a unified deep learning framework, called RDAA, which can effectively represent features of nodes and benefit the Role Discovery-guided NE with a deep autoencoder, while modeling the local links with an Attention mechanism. In addition, we design an elaborately binding technique to combine both parts and optimize the framework in a unified way. We conduct different experiments, including visualization, role classification, role discovery, and running time compared to popular NE methods for both proximity and structural similarity. The RDAA has better performance on all the datasets and achieves good tradeoffs.

Exploring Temporal Community Structure via Network Embedding.

Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks. Although some methods for static networks have shown promising results in boosting community detection by integrating community embedding, they are not suitable for temporal networks and unable to capture their dynamics. Furthermore, the dynamic embedding methods only model network varying without considering community structures. Hence, in this article, we propose a novel unsupervised dynamic community detection model, which is based on network embedding and can effectively discover temporal communities and model dynamic networks. More specifically, we propose the community prior by introducing the Gaussian mixture model (GMM) in the variational autoencoder, which can obtain community information and better model the evolutionary characteristics of community structure and node embedding by utilizing the variant of gated recurrent unit (GRU). Extensive experiments conducted in real-world and artificial networks demonstrate that our proposed model has a better effect on improving the accuracy of dynamic community detection.

Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework.

Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models' superior performance and effectiveness.

Simulation of Adsorption Process of l-Tryptophan on Mixed-Mode Resin HD-1 with Combined Physical Adsorption and Ion Exchange.

The mass-transfer process of l-tryptophan (l-Trp) in the hydrophobic interaction/ion-exchange mixed-mode resin HD-1 particles and fixed bed was studied experimentally and theoretically. The adsorption kinetics of l-Trp in single-component and multicomponent adsorption systems was investigated under different pH conditions. The co-adsorption of sodium ions (Na+) and l-Trp anions was found to be negligible. A modified liquid-film linear driving force model considering the physical adsorption of l-Trp zwitterions and anions as well as ion exchange of l-Trp cations was proposed. The dissociation equilibria of l-Trp molecules and functional groups on the resin were introduced in the model. The model could well fit the kinetic adsorption curves of l-Trp at different pH values. The presence of Na+ and the impurity amino acid l-glutamic acid (l-Glu) did not significantly affect the mass-transfer rate of l-Trp. The dynamic adsorption processes of l-Trp under different pH and concentration conditions were studied. A modified transport-dispersive model considering axial diffusion, liquid-film mass transfer, and a combined physical adsorption and ion-exchange equilibrium was established, which could predict the adsorption breakthrough curves of l-Trp well. During the dynamic adsorption process, the pH of mobile phase in the fixed bed changed with changing the l-Trp concentration in the mobile phase. l-Trp was well separated from Na+ and l-Glu with the purity of l-Trp higher than 99%, the recovery rate higher than 95%, and a concentration of 4.69 × 10-3 mol/L. The elution chromatographic peaks of l-Trp, l-Glu, and Na+ and the pH of the outlet solution were predicted satisfactorily.

Mass-Transfer Simulation of Salicylic Acid on Weakly Polar Hyper-cross-linked Resin XDA-200 with Coadsorption of Sodium Ion.

The mass-transfer process of salicylic acid on hyper-cross-linked resin XDA-200 was experimentally and theoretically studied. Undissociated salicylic acid was found to be the favorable form for salicylic acid adsorption on the resin. A pH-dependent adsorption isotherm model established in this paper could well fit the adsorption isotherm data at different pH values. Surface diffusion is the main mass-transfer mode for salicylic acid in resin particles. The salicylate anions and Na+ coadsorbed on the resin. The modified surface diffusion model considering the coadsorption was proposed. The model could satisfactorily fit the concentration decay curves of salicylic acid at different pH values and feed concentrations. NaOH aqueous solution at pH 12 could elute salicylic acid in the fixed bed efficiently. A pH-dependent dynamic adsorption and elution process model considering axial diffusion, external mass transfer, surface diffusion, pH-dependent adsorption equilibrium, as well as coadsorption of salicylate anions and Na+ was established. The model could well predict the breakthrough and elution curves at different feed concentrations. The research carried out in this paper has reference significance for optimizing the separation process of salicylic acid and its analogues.

Molecular Imprinted ZnS Quantum Dots-Based Sensor for Selective Sulfanilamide Detection.

Combining molecular imprinted polymers and water-soluble manganese-doped zinc sulfide quantum dots (Mn2+: ZnS QDs), a new molecule imprinted polymers-based fluorescence sensor was designed. The molecule imprinted quantum dots (MIP@QDs) were constructed by coating molecular imprinted polymers layer on the surface of ZnS: Mn2+ QDs using the surface molecular imprinting technology. The developed MIP@QDs-based sensor was used for rapid and selective fluorescence sensing of sulfanilamide in water samples. The binding experiments showed that the MIP@QDs has rapid fluorescent responses, which are highly selective of and sensitive to the detection of sulfanilamide. The respond time of the MIP@QDs was 5 min, and the imprinting factor was 14.8. Under optimal conditions, the developed MIP@QDs-based sensor shows a good linearity (R2 = 0.9916) over a sulfanilamide concentration range from 2.90 × 10-8 to 2.90 × 10-6 mol L-1, with a detection limit of 3.23 × 10-9 mol L-1. Furthermore, the proposed MIP@QDs-based sensor was applied to the determination of sulfanilamide in real samples, with recoveries of 96.80%-104.33%, exhibiting good recyclability and stability. Experimental results showed that the prepared MIP@QDs has the potential to serve as a selective and sensitive sensor for the fluorescence sensing of sulfonamides in water samples.