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The adsorption of l-tryptophan (l-Trp) was studied based on the hydrophobic interaction/ion exchange/ion exclusion mixed-mode adsorption resin D301. Firstly, the interaction mode between l-Trp and resin was analyzed by studying the influence of pH variation on the adsorption capability and the dissociation state of l-Trp. Secondly, the adsorption mechanism was illuminated by studying the adsorption equilibrium and kinetic behaviors. The adsorption equilibrium and a kinetics model were constructed. The augmentation of pH gradually elicited an enhancement in the adsorption capacity of l-Trp. l-Trp existing in varied dissociation states could be adsorbed by the resin, and the interaction mode relied upon the pH of the solution. An integrated adsorption equilibrium model with the coadsorption of different dissociation states of l-Trp was developed and could predict the adsorption isotherms at various pH levels satisfactorily. Both external mass transfer and intra-particle diffusion collectively imposed constraints on the mass transfer process of l-Trp onto the resin. An improved liquid film linear driving force model (ILM) was constructed, and the model provided a satisfactory fit for the adsorption kinetics curves of l-Trp at various pH levels. l-Trp molecules had a high mass transfer rate at a relatively low solution pH. [ABSTRACT FROM AUTHOR]

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. • NcRNAs as biomarkers for diagnosing craniofacial abnormalities. • NcRNAs and their impact on craniofacial developmental biology. • NcRNAs regulatory networks as an attractive model for studying craniofacial abnormalities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Preserve the natural hierarchical and anisotropic structure of wood. • Dissolution and regeneration to obtain high specific surface area wood aerogel. • Silane modification to obtain highly flexible wood aerogel. • Wood derived conductive aerogel as pressure sensor has high fatigue resistance. Conductive nanocellulose aerogels possess durability and sensitivity by using as piezoresistive sensors. Those aerogels are mostly prepared via a "bottom-up" strategy by using nanocellulose or dissolved and regenerated cellulose as raw materials. However, there is a pressing need to further enhance their sustainability and economic viability. To alleviate these issues, cellulose aerogel can alternatively be made via a "top-down" technique that entails the direct removal of contaminants from naturally porous biomass. Herein, conductive aerogels with exceptional mechanical flexibility and ultrahigh specific surface area were created. Delignified wood was partially dissolved and regenerated to created nanocellulose fibrils with spider web structure inside the pores of wood. After modification with silane coupling agent and carbon nanotubes (CNTs), flexible conductive wood aerogel (C-WA) were obtained. The specific surface area of C-WA was as high as 333.82 m2/g, which was 45 times of that of wood. C-WA could maintain its good resilience even after 1000 cycles, showing high compression resilience and excellent fatigue resistance (stress retention rate was 87.22%, height retention rate was 99.5%). The utilization of C-WA as a pressure sensor demonstrated remarkable attributes, including ultra-fast response and stability within the designated range. Furthermore, it exhibited exceptional sensing performance even after enduring 2000 stable cycles. The system is capable of real-time, accurate, and efficient detection of human motion signals, showcasing immense potential for future applications in wearable electronic products. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

R1234yf is characterized by low global warming potential (GWP) and it is a very promising candidate to substitute traditional refrigerant in the near future. The condensation heat transfer of low GWP refrigerant R1234yf was experimentally studied in detailed in this study. The influence of tube diameter was also discussed. HTCs were measured in horizontal tubes with the inner diameter (ID) of 2 and 4 mm, and the flow patterns taken during experiment were used to analyze the results. Different effects including mass fluxes, vapor quality, thermophysical properties, flow pattern and tube diameter on the HTC were also analyzed. Under the same mass flux and vapor quality, the flow patterns within ID 4 mm and 2 mm are significantly different. The turbulence between the vapor and liquid interface become gentle within ID 2 mm compared that within ID 4 mm by observation. At high mass fluxes, the HTCs within ID 2 mm tube are close to or even higher than that within ID 4 mm tube. When reducing the tube diameter, the influence of thermophysical properties, especially the surface tension on heat transfer is more obvious. Based on the laminar liquid film analytical solution and single phase forced convective heat transfer correlation, also considering the influence of surface tension and gravity, a new heat transfer correlation is developed with the experimental data using R1234yf, R134a and R32 within tube inner diameter 2 mm. The comparison shows that the mean deviation is 13.5%. [ABSTRACT FROM AUTHOR]

Background: Endoplasmic reticulum (ER) stress has been found to foster the escape of cancer cells from immune surveillance and upregulate PD-L1 expression. However, the underlying mechanisms are unknown. Methods: While analyzing the protein levels using immunofluorescence and Western blotting, the RNA levels were measured using qRT-PCR. Ten injection of exosomes into six-week-old nude mice was made through the tail vein once every other day in total. Results: The expression of certain ER stress markers such as PERK (PKR-like endoplasmic reticulum kinase), ATF6 (activating transcription factor 6), and GRP78 (glucose-regulated protein 78), was found to be upregulated in the oral squamous cell carcinoma (OSCC) tissues and related to poor overall survival. There is a positive relationship between the extent of ER stress-related proteins and a cluster of PD-L1 expression and macrophage infiltration among the OSCC tissues. Further, incubation with exosomes derived from ER-stressed HN4 cells (Exo-ER) was found to upregulate PD-L1 extents in macrophages in vitro and in vivo, and macrophage polarization toward the M2 subtype was promoted by upregulating PD-L1. Conclusions: ER stress causes OSCC cells to secrete exosomal PD-L1 and upregulates PD-L1 expression in macrophages to drive M2 macrophage polarization. The delineation of a new exosome-modulated mechanism was made for OSCC–macrophage crosstalk driving tumor development and to be examined for its therapeutic use. Exosomal PD-L1 secreted by ER-stressed OSCC cells promoted M2 macrophage polarization. BvoEQJGxSejnxUc-5MZRg5 Video Abstract. [ABSTRACT FROM AUTHOR]

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. • We propose a novel approach to dynamic network representation learning by applying contrastive learning. • DNCL design dynamic contrastive objective to capture the temporal and topological features of dynamic networks. • DNCL's objective consists of inter-snapshot contrast and intra-snapshot contrast. • DNCL achieves state-of-the-art performance compared with the base-lines on tasks. [ABSTRACT FROM AUTHOR]

To compare the micropore structure of marine-continental transitional shale with marine shale, organic geochemical, field emission scanning electron microscopy, and low-temperature nitrogen adsorption experiments were conducted on shale samples from the Shanxi Formation in the eastern Ordos Basin and the Longmaxi Formation in the southern Sichuan Basin. The results show that Shanxi Formation shale has a smaller specific surface area and pore volume than Longmaxi Formation shale; therefore, the transitional shales fail to provide sufficient pore spaces for the effective storage and preservation of natural gas. Both the transitional and marine shales are in an overmature stage with high total organic carbon content, but they differ considerably in pore types and development degrees. Inorganic pores and fractures are dominantly developed in transitional shales, such as intragranular pores and clay mineral interlayer fractures, while organic nanopores are rarely developed. In contrast, organic pores are the dominant pore type in the marine shales and inorganic pores are rarely observed. The fractal analysis also shows that pore structure complexity and heterogeneity are quite different. These differences were related to different organic types, i.e., type I of marine shale and type III of transitional shale. Marine Longmaxi shale has experienced liquid hydrocarbon cracking, gas generation, and pore-forming processes, providing good conditions for natural gas to be preserved. However, during the evolution of transitional Shanxi shale, gas cannot be effectively preserved due to the lack of the above evolution processes, leading to the poor gas-bearing property. The detailed comparison of the micropore structure between the transitional and marine shales is of great importance for the future exploitation of marine-continental transitional shale gas in China. [ABSTRACT FROM AUTHOR]

Abstract Temporal community detection could help us analyze and understand the meaningful substructure hidden within dynamic networks in the real world. Evolutionary clustering, as a popular framework for clustering stream data, has been denoted for mining the communities in dynamic networks. However, most of these methods ignore the varying characteristics of micro structure of the networks and lack of statistical interpretation. In this paper, we propose a powerful, interpretable and extensible evolutionary clustering framework based on nonnegative matrix factorization (NMF) for temporal community detection via combining the first-order varying information of micro structure in dynamic networks from the perspective of statistical model. Firstly, we consider the first-order varying information of nodes by constructing a temporal similarity matrix over time. Secondly, we present the framework, FVI-NMF, for detecting temporal community based on NMF combining the First-order Varying Information. Thirdly, we develop a effective algorithm to optimize the objective function of FVI-NMF and analyze its complexity. In addition, our model can discover the evolutionary pattern of temporal communities synchronously, which has a variety applications in the analysis of dynamic network. Experiments on both artificial and real dynamic networks demonstrate that our proposed framework has superior performance in comparison with state-of-art methods. Highlights • A powerful framework FVI-NMF for temporal community detection is proposed. • FVI-NMF combines historical structures with the varying characteristics of micro structure. • FVI-NMF could uncover the temporal evolutionary pattern of communities. [ABSTRACT FROM AUTHOR]

Abstract Quantitative analysis of human reposting patterns on social networking sites will provide helpful insights into the tracking and management of information spreading and social contagion. A large amount of efforts have been made on mining and explaining the patterns recently. However, existing researches mainly focused on specific events, topics or individuals. A more macroscopic angle is required for understanding the reposting characteristics on SNSs. In this paper, we explore the human reposting patterns from a global perspective by analyzing the large-scale reposting networks, which are constructed by about 1.5 billion reposting records collected from Sina Weibo over 15 months. Experiments indicate that the times of being reposted obey power law decays, and the frequency of reposting between two users follows a distribution of exponentially truncated power law. Meanwhile, taking user categories and micro-blog contents into account, we classify users into three categories and find that different users have distinct habits when reposting micro-blogs, e.g. common individuals would like to repost micro-blogs about entertainments, good qualities and sharing life styles. Highlights • Reposting behavior makes the networks share a property of high connectivity. • The reposting frequency follows an exponentially truncated power law decay. • Reposting behavior exhibits the aggregation characteristic. • Users with different attributes have particular reposting habits on SNSs. [ABSTRACT FROM AUTHOR]

Role-oriented representation learning extracts structural features by capturing the structural similarities of nodes to generate representations that preserve the structural equivalence, which is crucial for addressing the role discovery task of real networks. However, most studies directly combine different types of structural features and ignore the correlations among features, which is not enough to accurately portray node roles. Therefore, we propose a novel role-oriented network representation learning method RFLH that mines the correlations among features by normalizing flows to improve the performance on the role discovery tasks. We utilize the Variational Auto-Encoder (VAE) to generate the original representation distribution and learn the structural features of nodes by constraint of local and higher-order features, where local features are learned by the traditional variational module, while higher-order features are learned by the flow-based variational module that introduces normalizing flows to map the original representation distribution to a complex distribution. Moreover, we deduce the new loss by introducing the Jacobian determinant to Evidence Lower Bound (ELBO). Finally, we fuse local and higher-order features into the node representations, which are concatenated by the representations sampled from the original distribution and the new distribution, to generate the final node representations. Extensive experimental results on several real network datasets demonstrate that our model achieves superior performance compared to typical baseline methods. [ABSTRACT FROM AUTHOR]

Abstract Detecting the temporal communities and analyzing their evolution in dynamic networks is an important question for understanding the structure and function of complex networks. Most existing methods deal the temporal community detection and evolution as a two-step processes and only apply to unweighted and undirected dynamic networks. In this paper, we proposed a new clustering method based on non-negative matrix factorization from a fully probabilistic perspective, to explore temporal and constant community structure as well as the importance of nodes in any type dynamic networks synchronously. In detailed, we first denote the important matrix of node in dynamic networks, the community membership matrix, the similarity matrix at each snapshot and the probability transition matrix of community between the two consecutive snapshots. Second, we constitute the temporal community detection method from the view of generating networks. Third, we introduce a gradient descent algorithm to optimize the objection function of the proposed model. The experimental results on both artificial and real dynamic networks demonstrate that the superior performance of our proposed method is over some widely-used methods. [ABSTRACT FROM AUTHOR]

On one hand, the detection of tightly connected groups, also known as community detection in complex networks, is a prominent problem for network analysis and mining. On the other hand, almost all of social, biological, bibliographic, communication and computer systems are modeled as temporal networks, the topological structures of which evolve with time, or multiplex networks, each pair of nodes of which has multiple linked relations. Current methods of community detection for temporal networks are based on incremental, independent or evolutionary clustering, and for multiplex networks are based on fusion of the multiple links. However, all these methods ignore the common structure hidden in the networks, which is denoted as the common cluster here. So in this paper, we propose a constrained common cluster based model ( C 3 model) to analyze the temporal and multiplex networks, which can not only detect the community structure, but also identify the importance of each node based on the common cluster structure of both two classes of networks. The intrinsic assumption of the proposed model is that there are common or coincident clusters hidden in these networks. In detail, we first construct the Markov steady-state matrices of each snapshot of temporal network or each slice of multiplex network. Next, we propose the object function of C 3 model by combining the Markov steady-state matrices, similarity matrices with community membership matrices of each snapshot or slice of the network. Last, a gradient descent algorithm based on non-negative matrix factorization is proposed for the object function. Experiments on both synthetic datasets and real-world networks demonstrate that the proposed C 3 model has competitive performance based on the evaluation indexes NMI and error of community detection, otherwise, the proposed model could identify the importance of nodes of the temporal or multiplex networks. [ABSTRACT FROM AUTHOR]

Herein, the purification of the water-soluble yellow Monascus pigment from fermentation broth using the LX3020 resin is reported. The resin adsorbed the pigment via chemisorption involving hydrophobic and electrostatic interactions. The optimal operating conditions for the dynamic separation of pigment are 25 °C, a feed solution pH of 4.0, 70% (v/v) ethanol as eluent, and an adsorption and elution flow rate of 1 B V/h. The purity and recovery rate of this pigment were 100% and 22.4%, respectively. Antioxidant activity assays showed that the antioxidant capacity of the pigment (2.5 mg/mL) reached 64.2% of that of vitamin C units. Its free radical scavenging capacity is good, and it can protect against H 2 O 2 -induced cell oxidative injury with no cytotoxicity. Therefore, utilization of this pigment (120 mg/100 g) to manufacture functional rice noodles with improved texture qualities and sensory indexes. The degree of rice noodle contamination decreased by ∼90% after cultivation for 48 h. These results reveal that yellow Monascus pigment could play an important role in the preparations of functional foods. • Water-soluble yellow Monascus pigment (WSYMP) was separated through resin LX3020. • WSYMP can protect H 2 O 2 -induced cell oxidative injury with no cytotoxicity. • Functional rice noodles was manufactured via addition of WSYMP powder. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An innovative benign process for recovery lactic acid from its fermentation broth is proposed using a novel hyper-cross-linked meso-micropore resin and water as eluent. This work focuses on modeling the competitive adsorption behaviors of glucose, lactic acid and acetic acid ternary mixture and explosion of the adsorption mechanism. The characterization results showed the resin had a large BET surface area and specific pore structure with hydrophobic properties. By analysis of the physicochemical properties of the solutes and the resin, the mechanism of the separation is proposed as hydrophobic effect and size-exclusion. Subsequently three chromatographic models were applied to predict the competitive breakthrough curves of the ternary mixture under different operating conditions. The pore diffusion was the major limiting factor for the adsorption process, which was consistent with the BET results. The novel HD-06 resin can be a good potential adsorbent for the future SMB continuous separation process. [ABSTRACT FROM AUTHOR]

To understand time-evolving networks, researchers should not only concentrate on the community structures, an essential property of complex networks, in each snapshot, but also study the internal evolution of the entire networks. Temporal communities provide insights into such mechanism, i.e., how the communities emerge, expand, shrink, merge, split and decay over time. Based on the symmetric nonnegative matrix factorization (SNMF), we present a dynamic model to detect temporal communities, which not only could find a well community structure in a given snapshot but also demands the results bear some similarity to the partition obtained from the previous snapshot. Moreover, our method can handle the situation that of the number of community changes in the networks. Also, a gradient descent algorithm is proposed to optimize the objective function of the model. Experimental results on both the synthetic and real-world networks indicate that our method outperforms the state-of-art methods for temporal community detection. [ABSTRACT FROM AUTHOR]

Identifying functional urban areas is a significant research of considerable interest in many important fields such as city planning and facility location problem. Traditionally, we identify the function of urban areas from the macro-level perspective. With the availability of human digital footprints, investigation of functional urban areas from a micro-level perspective becomes possible. In this paper, we identified the functional urban areas of a metropolitan city in China by some metrics of community detection based on the social network of mobile phone users. The result shows that there are close relations between urban area and individual communication network, which can help us identify the function of areas more conveniently. [ABSTRACT FROM AUTHOR]

A wide variety of natural or artificial systems can be modeled as time-varying or temporal networks. To understand the structural and functional properties of these time-varying networked systems, it is desirable to detect and analyze the evolving community structure. In temporal networks, the identified communities should reflect the current snapshot network, and at the same time be similar to the communities identified in history or say the previous snapshot networks. Most of the existing approaches assume that the number of communities is known or can be obtained by some heuristic methods. This is unsuitable and complicated for most real world networks, especially temporal networks. In this paper, we propose a Bayesian probabilistic model, named Dynamic Bayesian Nonnegative Matrix Factorization (DBNMF), for automatic detection of overlapping communities in temporal networks. Our model can not only give the overlapping community structure based on the probabilistic memberships of nodes in each snapshot network but also automatically determines the number of communities in each snapshot network based on automatic relevance determination. Thereafter, a gradient descent algorithm is proposed to optimize the objective function of our DBNMF model. The experimental results using both synthetic datasets and real-world temporal networks demonstrate that the DBNMF model has superior performance compared with two widely used methods, especially when the number of communities is unknown and when the network is highly sparse. [ABSTRACT FROM AUTHOR]

The recovery and purification of biobutanol based on the adsorption method were performed in dynamic conditions. Computational and theoretical modeling is an important tool in the characterization, development, and validation of fixed-bed columns. Relevant breakthrough curves provide valuable information for designing fixed-bed adsorption processes for field applications. In the present study, a general rate model (GRM), implementing convection/diffusion approach theory and a competitive isotherm model, was used to predict the competitive sorption dynamics of acetone-butanol-ethanol (ABE) on a KA-I resin in a fixed-bed column under different operating conditions, i.e., inlet feed flow rate, initial adsorbate concentration, and bed height. The model simulation was quantified by the absolute average deviation (AAD). The calculated AAD values, ranging from 0.05 to 0.1, indicated that the GRM gives a general prediction for experimental data. The axial dispersion, external mass transfer, and pore diffusion coefficients were calculated by a series of empirical correlations. Biot number was used to identify the rate controlling step for the adsorption process of ABE on the resin. And the pore diffusion coefficient was found to be major governing factor for adsorption of ABE. The data and modeling presented are valuable for designing the continuous chromatographic separation process and simulation of ABE. [ABSTRACT FROM AUTHOR]

• Separation process simulation of nucleotides by a mixed-mode resin was studied. • An isotherm model with adsorption of different nucleotide species was developed. • A kinetic model combining physical adsorption with ion exchange was established. • Profiles of nucleotides concentration and pH in the column were well simulated. The preparative separation of guanosine 5′-monophosphate (GMP) and cytidine 5′-monophosphate (CMP) on mixed-mode resin HD-1 was experimentally and theoretically investigated. The adsorption mechanisms of the two nucleotides were elucidated by analyzing adsorption equilibria and kinetics at different pH values. The adsorption dynamics of GMP and CMP in a fixed bed packed with resin HD-1 were studied. All nucleotide monovalent cations, zwitterions, and monovalent anions were adsorbed by the resin. Further, a general adsorption isotherm model was developed by considering the adsorption of different nucleotide species and the dissociation equilibrium behaviors of resin ligands. The model fit the adsorption isotherm data of GMP and CMP well. A modified liquid-film linear driving force model with the combined physical adsorption of nucleotides in different dissociation states and ion exchange of Na+ was established. The dissociation equilibria of resin ligands and nucleotides were considered. The model satisfactorily predicted the adsorption kinetic data at different pH values. The values of the efficient diffusion coefficients for GMP and CMP were not significantly influenced by the solution pH. A modified transport-diffusion model with pH gradient elution was proposed. The model accurately predicted the elution chromatographic peaks of GMP and CMP, as well as the pH at the outlet of the fixed bed packed with resin HD-1. [ABSTRACT FROM AUTHOR]

Condensation heat and mass transfer characteristics of low GWP zeotropic refrigerant mixtures R1234yf/R32 (mass fractions of 0.52:0.48 and 0.77:0.23, respectively) inside a horizontal smooth tube (inner diameter 4 mm) were studied experimentally. Moreover, a non-equilibrium film heat and mass transfer model was also developed by considering the mass transfer resistance on the vapor and the liquid side. Effects of mass flux, vapor quality, thermophysical properties and mass fraction on the heat transfer coefficients (HTCs) were analyzed. The heat transfer characteristics, especially the heat transfer deterioration caused by the mass transfer difference of the zeotropic refrigerant mixture, were evaluated. The experiment data show the HTCs of R1234yf/R32 (0.48:0.52) are lower than that of R1234yf at the inlet nearby of the condenser, then that becomes higher gradually than R1234yf due to mass transfer resistance decreasing. The non-equilibrium film model shows good agreement with the experimental results with the mean deviation in HTC of 22.9% at a mass fraction of 0.77:0.23 and 17.8% at a mass fraction of 0.48:0.52. Furthermore, the heat transfer degradation coefficient, interface temperature, diffusion flux of R32 and mass transfer Nusselt number were also reasonably determined with the non-equilibrium film model. • Condensation heat and mass transfer of R1234yf/R32 was experimentally studied. • A non-equilibrium film model was developed considering the mass transfer resistance. • Heat transfer deterioration caused by the mass transfer difference was evaluated. • Heat transfer degradation coefficient and diffusion flux were reasonably determined. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• pH-dependent adsorption isotherm model for L-Trp-was developed. • L-Trp± is the most favorable form for L-Trp-adsorption on resin XDA-200. • Surface diffusion model with coadsorption of Na+ and L-Trp- was established. • The dynamic separation process of L-Trp-with coadsorption of Na+ was simulated. The adsorption separation of L-tryptophan (L-Trp) by the weakly polar hyper-cross-linked resin XDA-200 was studied. First, the adsorption equilibria of different species of L-Trp on the resin were compared. Then, the adsorption isotherms and adsorption kinetics of L-Trp were studied at different pH values. Finally, the dynamic adsorption and separation processes of L-Trp in a packed bed of the resin were studied. The distribution coefficient of L-Trp± between the resin and an aqueous solution of L-Trp (55.69) was found to be markedly larger than that of L-Trp+ (27.53) and L-Trp- (10.42). An adsorption isotherm model depending on pH was established to simulate the adsorption equilibrium data of L-Trp. The cooperative adsorption of sodium ion (Na+) with L-Trp- cannot be ignored when the solution pH is higher than 8.0. Thus, a modified surface diffusion model considering cooperative adsorption of Na+ with L-Trp- was established. The model fitted the kinetic curves for L-Trp adsorption under different pH values satisfactorily. The surface diffusion coefficient of L-Trp first decreased and then increased as pH increased from 3 to 12. In this study, a modified film-surface diffusion model considering cooperative adsorption of Na+ with L-Trp- is proposed. Further, we show that our proposed model can predict the chromatographic peaks of L-Trp, L-glutamic acid (L-Glu), and Na+ satisfactorily. [ABSTRACT FROM AUTHOR]

Heterogeneous Information Networks (HINs) are commonly employed to model complex real-world scenarios with diverse node and edge types. However, due to constraints in data collection and processing, constructed networks often lack certain relations. Consequently, various methods have emerged, particularly recently, leveraging heterogeneous graph neural networks (HGNNs) to predict missing relations. Nevertheless, these methods primarily focus on pairwise relations between two nodes. Real-world interactions, however, often involve multiple nodes and diverse types, extending beyond simple pairwise relations. For instance, academic collaboration networks may entail interactions among authors, papers, and conferences simultaneously. Despite their prevalence, higher-order relations are often overlooked. While HGNNs are effective at learning network structures, they may suffer from over-smoothing, resulting in similar representations for nodes and their neighbors. The learned inaccurate proximity among nodes impedes the discernment of higher-order relations. Furthermore, observed edges among a target group of nodes can provide valuable evidence for predicting higher-order relations. To address these challenges, we propose a novel model called Accurate Neighborhood- and Self-information Enhanced Heterogeneous Graph Neural Network (ANSE-HGN). Building upon HGNNs to encode network structure and attributes, we introduce a relation-based neighborhood encoder to capture information within multi-hop neighborhoods in heterogeneous higher-order relations. This enables the calculation of accurate proximity among target groups of nodes, thereby enhancing prediction accuracy. Additionally, we leverage self-information from observed higher-order relations as an auxiliary loss to reinforce the learning process. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed method in higher-order relation prediction tasks. • We propose ANSE-HGN to solve the problem of higher-order relation prediction in HINs. • We accurately capture the proximity of higher-order relations by multi-hop neighbors. • We design a self-information loss to assist in model optimization. • We conduct experiments on four datasets, demonstrating the effectiveness of ANSE-HGN. [ABSTRACT FROM AUTHOR]

The mechanism schematic model by SNHG14/miR-34a/HMGB1 axis. SNHG14 expression was decreased in NSCLC cells by transfection of si-SNHG14. Then, miR-34a expression was upregulated. Next, upregulated miR-34a inhibited HMGB1 expression. Finally, low HMGB1 level repressed NSCLC cell migration and invasion, and enhanced cell apoptosis and cell sensitivity to CDDP, thereby suppressing NSCLC progression in vitro. • Upregulated SNHG14 was associated with CDDP resistance of NSCLC cells. • SNHG14 silencing repressed NSCLC progression through regulation of miR-34a/HMGB1 axis. • SNHG14 silencing enhanced NSCLC cell sensitivity to CDDP. • Targeting SNHG14 might be a potential target for NSCLC management. Long non-coding RNAs (lncRNAs) small nucleolar RNA host gene 14 (SNHG14) has been identified as an oncogene involved in the progression of various human cancers. Nevertheless, the functional role and molecular mechanism of SNHG14 on NSCLC remain largely elusive. qRT-PCR assay was performed to detect the levels of SNHG14, miR-34a and high mobility group box 1 (HMGB1) mRNA. HMGB1 protein level was assessed by western blot analysis. CCK-8 assay was used to determine the IC50 value of cisplatin (CDDP), and transwell assays were employed to detect cell migration and invasion abilities. Cell apoptosis was determined by flow cytometric analysis. Dual-luciferase reporter assay, RNA immuoprecipitation assay and RNA pull-down assay were performed to confirm the interaction between SNHG14 and miR-34a, or miR-34a and HMGB1. Our data demonstrated that SNHG14 was upregulated in NSCLC cells, and SNHG14 silencing repressed the migration, invasion while accelerated the apoptosis of NSCLC cells. Moreover, we manifested that SNHG14 silencing promoted NSCLC cell sensitivity to CDDP. SNHG14 repressed miR-34a expression by binding to miR-34a. Additionally, SNHG14 regulated HMGB1 expression by sponging miR-34a. SNHG14 silencing exerted its regulatory effect by miR-34a and HMGB1 mediated the regulatory effect of miR-34a on NSCLC cells. In conclusion, SNHG14 silencing suppressed NSCLC progression at least partly by miR-34a/HMGB1 axis in vitro and promoted NSCLC cell sensitivity to CDDP, highlighting that SNHG14 might be a potential target for NSCLC therapy. [ABSTRACT FROM AUTHOR]

Link prediction in bipartite networks is attracting tremendous research interests. Most previous studies mainly assume the generation of link follows a predefined prior mechanism while neglecting complexity of the link generation mechanisms. To address this limitation, we present a parameter-free method, termed S tructural E nhancement and S tructural P erturbation (SESP), which jointly exploits explicit relations (low-order information) and implicit relations (high-order information) from the perspective of perturbation. The essence of SESP is that it transforms bipartite link prediction into monopartite link prediction without losing any information and predicts the missing links from a perturbed perspective. Compared with traditional link prediction methods, SESP does not assume a particular link generation mechanism, but learns this mechanism from the network itself. Extensive experiments on several disparate real-world bipartite networks demonstrate the effectiveness of the SESP model. • We transform bipartite LP into the monopartite without losing structure information. • We propose SESP framework to model explicit and implicit relations simultaneously. • Experiments on eight real-world bipartite networks show the performance of SESP. [ABSTRACT FROM AUTHOR]

Overlapping community detection has triggered recent research in network analysis. One of the promising techniques for finding overlapping communities is the popular stochastic models, which, unfortunately, have some common drawbacks. They do not support an important observation that highly connected nodes are more likely to reside in the overlapping regions of communities in the network. These methods are in essence not truly unsupervised, since they require a threshold on probabilistic memberships to derive overlapping structures and need the number of communities to be specified a priori. We develop a new method to address these issues for overlapping community detection. We first present a stochastic model to accommodate the relative importance and the expected degree of every node in each community. We then infer every overlapping community by ranking the nodes according to their importance. Second, we determine the number of communities under the Bayesian framework. We evaluate our method and compare it with five state-of-the-art methods. The results demonstrate the superior performance of our method. We also apply this new method to two applications, showing its superb performance on practical problems. [ABSTRACT FROM AUTHOR]

Abstract Evolutionary clustering is a classic and helpful framework for modeling dynamic data and has been devoted to analyzing the temporal networks recently. However, all methods based on evolutionary clustering either does not directly model the evolution, predict the varying of dynamic communities, deal with the case of the time-varying number of communities or have a high computational complexity and are not easily extended, which limits the applications for exploring the dynamic networks. In this paper, we propose a new novel framework of Evolutionary Clustering based on Graph regularized Nonnegative Matrix Factorization (ECGNMF), to detect dynamic communities and the evolution patterns and predict the varying structure across the temporal networks. More concretely, we construct a generated model to fit the observed snapshot networks based on nonnegative matrix factorization (NMF). Any one of series intuitive and interpretable penalty items as we denoted could be integrated into the proposed framework via a graph regularization way, which can be optimized efficiently and model changes of the number of communities across different snapshots of temporal networks. We also detect the evolution patterns of the dynamic communities in the temporal networks in a principled manner. Experimental results show that our framework has better performance on community detection in temporal networks compared to some widely used models based on evolutionary clustering and heuristic methods. [ABSTRACT FROM AUTHOR]

Many link prediction methods have been developed to infer unobserved links or predict missing links based on the observed network structure that is always incomplete and subject to interfering noise. Thus, the performance of existing methods is usually limited in that their computation depends only on input graph structures, and they do not consider external information. The effects of social influence and homophily suggest that both network structure and node attribute information should help to resolve the task of link prediction. This work proposes SASNMF, a link prediction unified framework based on non-negative matrix factorization that considers not only graph structure but also the internal and external auxiliary information, which refers to both the node attributes and the structural latent feature information extracted from the network. Furthermore, three different combinations of internal and external information are proposed and input into the framework to solve the link prediction problem. Extensive experimental results on thirteen real networks, five node attribute networks and eight non-attribute networks show that the proposed framework has competitive performance compared with benchmark methods and state-of-the-art methods, indicating the superiority of the presented algorithm. [ABSTRACT FROM AUTHOR]

Link prediction aims to extract missing informations, identify spurious interactions and potential informations in complex networks. Similarity-based methods, maximum likelihood methods and probabilistic models are the mainstreaming classes algorithms for link prediction. Meanwhile, low rank matrix approximation has been widely used in networks analysis and it can extract more useful features hidden in the original data through some kernel-induced nonlinear mapping. In this paper, based on the non-negative matrix factorization (NMF), we propose a kernel framework for link prediction and network reconstruction by using different kernels which could get both global and local information of the network through kernel mapping. In detailed, we map the adjacency matrix of the network to another feature space by two kernel functions, the Linear Kernel and Covariance Kernel, which have the principled interpretations for the network analysis and link predication. We test the AUC and Precision of widely used methods on a series of real world networks with different proportions of the training sets, experimental results show that our proposed framework has more robust and accurate performance compared with state-of-the-art methods. Remarkably, our approach also has the potential to address the problem of link prediction using small fraction of training set. [ABSTRACT FROM AUTHOR]

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard's coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science. [ABSTRACT FROM AUTHOR]

[Display omitted] • Co-fermentation with LAB strains increased the flavones concentrations in RYR. • LAB strains improved the flavor profiles and metabolites of RYR. • Inoculation of LAB strains effectively enhanced the amino acid content within RYR. Red yeast rice (RYR) as a traditional fermented food produced by inoculation of the Monascus genus into steamed rice through solid-state fermentation, has been used intensively for thousands of years in China. Herein, we investigated the flavor profiles, metabolites and quality characteristics of RYR produced by mix fermentation with lactic acid bacteria (LAB) using the headspace solid phase microextraction gas chromatography–mass spectrometry (HS–SPME–GCMS) approach integrated with ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC–MS). 56 volatile organic compounds (VOCs) and 1287 non-volatile metabolites were identified and classified into eight classes, the inoculation of Lactiplantibacillus plantarum (LP) and Limosilactobacillus fermentum (LF) increased VOCs and enhanced the flavor characteristics of RYR, with LP being the more significant. The KEGG pathway enrichment analysis revealed that most of the differential metabolites were enriched in amino acid metabolism when integrating volatile and non-volatile omics data, meanwhile, the addition of LAB promoted the metabolism of amino acid, alcohols, fatty acid and some flavonoids, in particular, amino acid metabolism was almost upregulated in the LP group. Therefore, this study highlights the tremendous potential of LAB in improving the flavor quality of RYR. This study provides novel insights into RYR fermentation processes and establishes a theoretical foundation for developing and applying new RYR products. [ABSTRACT FROM AUTHOR]

Network alignment plays an important role in many fields. The main task of network alignment is to find the node correspondence between different networks, i.e. whether they represent the same natural entity. Most existing alignment methods obtain the relevant information of nodes in the network through network representation learning technology, and then calculate the relationship between different nodes based on the node representations. These methods mostly focus on Euclidean geometry to learn node representations, does Euclidean space fit well with the network alignment? Considering that real-world networks often exhibit hierarchical structure and hyperbolic geometry shows the advantage of expressing network hierarchical structure, some researchers use hyperbolic geometry to learn node representations. But the network is not all hierarchical structure, so we exploit Euclidean and hyperbolic geometries jointly for node representation learning through an interactive learning mechanism to exploit the reliable spatial features in networks. The proposed method can well adapt to the complex structure in the network, providing efficient node representations for network alignment. Extensive experiments on synthetic and real-world datasets show the superiority of our method for network alignment. [ABSTRACT FROM AUTHOR]

[Display omitted] • An integrated deep eutectic solvent extraction and adsorption method was proposed. • The integrated process was optimized by response surface methodology. • Yield of integrated process was 2.15 times higher than that of sequential process. • Antioxidant capacity of polyphenols extract reached 79.54% of that of vitamin C. • Inhibition of extract on α-glucosidase and α-amylase approaches that of acarbose. The recovery of polyphenols from Citrus reticulata Blanco peels was studied based on integrated deep eutectic solvent (DES) extraction and resin adsorption. The DES prepared with choline chloride and ethylene glycol (molar ratio 1:4) and hydrophobic interaction/weakly acidic cation exchange mixed-mode adsorption resin HD-1 were used as the extractant and adsorbent respectively. The operating parameters of integrated process were optimized using completely randomized design and response surface methodology (RSM). The extraction kinetics was studied and the composition of extract was analyzed by UPLC-MS. Finally, the antioxidant activity of extract and its inhibitory ability on α-glucosidase and α-amylase were determined. Under the optimal operating conditions (liquid–solid ratio of 20:1, extraction temperature of 43 °C, extraction time of 4.8 h, resin amount of 6.6 g), the polyphenols yield of integrated process was increased by 2.15-fold and the time was shortened by 52.48 % compared with that of traditional sequential process. The extraction kinetic curves of both integrated and sequential processes conformed to pseudo-first-order kinetic model, and the extraction speed of integrated process was significantly higher than that of sequential process. The main polyphenols in extract were neohesperidoside, poncirin, narirutin, and hesperidin, etc. The extract exhibited a strong antioxidant capacity with DPPH free radical scavenging rate of extract reached 79.54 % of that of vitamin C. The inhibition ratios of extract against α-glucosidase and α-amylase were 97.81 % and 427 % of that of acarbose respectively. The results indicated that the extract has a potential application for prevention and control of type 2 diabetes mellitus. [ABSTRACT FROM AUTHOR]

The present study is a preparation of bioactive peptides from Cornus officinalis proteins by the compound enzymatic hydrolysis method. Response surface methodology (RSM) coupled with Box–Behnken design (BBD) is used to optimize the preparation process of Cornus officinalis peptides. The effects of independent variables, such as the amount of enzyme, pH value, time, extraction times and the ratio of material to liquid on the yield of peptides, are also investigated. The analysis results of the RSM model show that the optimum conditions for the extraction of Cornus officinalis peptides were a pH value of 6.76, temperature of 48.84 °C and the amount of enzyme of 0.19%. Under optimal conditions, the yield of peptides was 36.18 ± 0.26 %, which was close to the predicted yield by the RSM model. Additionally, the prepared Cornus officinalis peptides showed significant antioxidant activity; the scavenging rates of the peptides for DPPH and ·OH were 48.47% and 29.41%, respectively. The results of the cell proliferation assay revealed that the prepared Cornus officinalis peptides could promote embryo fibroblast cells proliferation and repair oxidative damage cells. These results have a practical application value in the design of novel functional food formulations by using Cornus officinalis. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is primarily transmitted through droplets. All human tissues with the angiotensin‐converting enzyme 2 (ACE2) and transmembrane protease serines 2 (TRMPRSS2) are potential targets of SARS‐CoV‐2. The role of saliva in SARS‐CoV‐2 transmission remains obscure. In this study, we attempted to reveal ACE2 and TRMPRSS2 protein expression in human parotid, submandibular, and sublingual glands (three major salivary glands). Then, the binding function of spike protein to ACE2 in three major salivary glands was detected. The expression of ACE2 and TMPRSS2 in human saliva from parotid glands were both examined. Exogenous recombined ACE2 and TMPRSS2 anchoring and fusing to oral mucosal epithelial cells in vitro were also unraveled. ACE2 and TMPRSS2 were found mainly to be expressed in the cytomembrane and cytoplasm of epithelial cells in the serous acinus cells in parotid and submandibular glands. Our research also discovered that the spike protein of SARS‐CoV‐2 binds to ACE2 in salivary glands in vitro. Furthermore, exogenous ACE2 and TMPRSS2 can anchor and fuse to oral mucosa in vitro. Thus, the expression of ACE2 and TMPRSS2 in human saliva might have implications for SARS‐CoV‐2 infection. [ABSTRACT FROM AUTHOR]

Background: Drug resistance is a major clinical drawback behind the failure of chemotherapy in non‐small cell lung cancer (NSCLC). In this study, we undertook to identify the precise role of circular RNA (circRNA) circ_0058357 in the functional properties of DDP‐resistant NSCLC cells. Methods: Circ_0058357, miR‐361‐3p and ATP‐binding cassette (ABC) subfamily C member 1 (ABCC1) were quantified by qRT‐PCR and western blot. Cell survival and viability were gauged by MTT assay. Cell proliferation, apoptosis, invasion and migration were measured by EdU, flow cytometry, transwell and wound‐healing assays, respectively. The direct relationship between miR‐361‐3p and circ_0058357 or ABCC1 was validated by dual‐luciferase reporter assay. Results: Our data showed that circ_0058357 was highly expressed in DDP‐resistant NSCLC tissues and cells. Inhibition of circ_0058357 repressed cell growth, invasion, migration, and promoted DDP sensitivity and cell apoptosis of H1299/DDP and A549/DDP cells in vitro. Moreover, inhibition of circ_0058357 diminished the growth of A549/DDP cells and sensitized them to the cytotoxic effect of DDP in vivo. Mechanistically, circ_0058357 contained a miR‐361‐3p binding site and miR‐361‐3p was identified as a molecular mediator of circ_0058357 regulation. MiR‐361‐3p suppressed ABCC1 expression by binding to ABCC1 3′UTR, and miR‐361‐3p‐mediated inhibition of ABCC1 affected the growth, invasion, migration, apoptosis and DDP sensitivity of H1299/DDP and A549/DDP cells. Furthermore, circ_0058357 regulated ABCC1 expression by competitively binding to shared miR‐361‐3p. Conclusions: Our findings identified that inhibition of circ_0058357 suppresses the growth and metastasis of H1299/DDP and A549/DDP cells and sensitizes them to DDP therapy partially by targeting the miR‐361‐3p/ABCC1 axis. [ABSTRACT FROM AUTHOR]

This research aimed to investigate the expression and function of FAM83A in the proliferation and metastasis in head and neck squamous cell carcinoma (HNSCC). FAM83A mRNA and protein expressions in HNSCC were detected in primary HNSCC samples and cell lines. The associations between FAM83A expression and clinicopathologic variables were evaluated through tissue microarrays. Besides, FAM83A knockdown and overexpression cell lines were constructed to assess cell growth and metastasis in vitro and the relationship between FAM83A and epithelial-mesenchymal transition (EMT). Furthermore, two models of xenograft tumors in nude mice were used to assess the tumorigenicity and metastasis ability of FAM83A in vivo. In the present study, overexpression of FAM83A in HNSCC samples was significantly associated with tumor size, lymph node status and clinical tumor stages. Mechanically, FAM83A could promote HNSCC cell growth and metastasis by inducing EMT via activating Wnt/β-catenin signaling pathway. Rescue experiment demonstrated the inhibition of β-catenin could counteract the function of FAM83A. Also, the FAM83A knockdown could suppress tumor growth and distant metastasis in the xenograft animal models of HNSCC. In conclusion, this study identifies FAM83A as an oncogene of HNSCC. This study provides new insights into the molecular pathways that contribute to EMT in HNSCC. We revealed a previously unknown FAM83A-Wnt-β-catenin signaling axis involved in the EMT of HNSCC. There may be a potential bi-directional signaling loop between FAM83A and Wnt/β-catenin signaling pathway in HNSCC. [ABSTRACT FROM AUTHOR]

Finding semantic communities using network topology and contents together is a hot topic in community detection. Existing methods often use word attributes in an indiscriminate way to help finding communities. Through analysis we find that, words in networked contents often embody a hierarchical semantic structure. Some words reflect a background topic of the whole network with all communities, some imply the high-level general topic covering several topic-related communities, and some imply the high-resolution specialized topic to describe each community. Ignoring such semantic structures often leads to defects in depicting networked contents where deep semantics are not fully utilized. To solve this problem, we propose a new Bayesian probabilistic model. By distinguishing words from either a background topic or some two-level topics (i.e., general and specialized topics), this model not only better utilizes the networked contents to help finding communities, but also provides a clearer multiplex semantic community interpretation. We then give an efficient variational algorithm for model inference. The superiority of this new approach is demonstrated by comparing with ten state-of-the-art methods on nine real networks and an artificial benchmark. A case study is further provided to show its strong ability in deep semantic interpretation of communities. [ABSTRACT FROM AUTHOR]

Bio-based polyamide 56 is a new type of bio-based polyamide that is regarded as a most promising material for the textile industry. High-quality polymer materials require first high-quality monomers. In this report, we investigated the solid–liquid equilibrium of polyamide 56 salt (an ionic solid compound used to prepare polyamide 56), thermodynamic parameters of the dissolution process, and the crystallization process, and screened out a favorable crystallization system to obtain a high-quality product. The dissolution was shown to be endothermic and not a spontaneous process; entropy was the overall driving force for the dissolution. The crystallization process and the effect of impurities were investigated. Cooling crystallization in pure water was beneficial to the nucleation and crystal growth of polyamide 56 salt, and is a good choice from the perspective of economy and the stability of the product. Additionally, the crystal structural characteristics and thermal behavior were systematically characterized by single crystal X-ray diffraction (SSRD), powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). It was found that there were two crystal forms of polyamide 56 salt, dihydrate and anhydrate. The dihydrate was a stable crystal form, and the anhydrous form could be converted into the dihydrate by absorbing moisture. The temperature 183.7 °C was the onset of 1,5-pentanediamine cation deprotonation and adipic acid anion protonation rather than their degradation. These contributions provide a potential industrial preparation strategy for high-quality polyamide 56 salt, as well as laying a foundation for its polymerization. [ABSTRACT FROM AUTHOR]

Rating-based methods (e.g., collaborative filtering) in recommendation can explicitly model users and items from their rating patterns, nevertheless suffer from the natural data sparsity problem. In other hand, user-generated reviews can provide rich semantic information of user preference and item features, and can alleviate the sparsity problems of rating data. In fact, ratings and reviews are complementary and can be viewed as two different sides of users and items, hence fusing rating patterns and text reviews effectively has the potential to learn more accurate representations of users and items for recommendation. In this paper, we propose a hybrid neural recommendation model to learn the deep representations for users and items from both ratings and reviews. Our model contains three major components, i.e., a rating-based encoder to learn deep and explicit features from rating patterns of users and items, a review-based encoder to model users and items from text reviews, and the prediction module for recommendation according to the rating- and review-based representations of users and items. In addition, considering that different reviews have different informativeness for modelling users and items, we introduce a novel review-level attention mechanism incorporating with rating-based representation as query vector to select useful reviews. We conduct extensive experiments on several benchmark datasets and the experimental results demonstrate that our model can outperform the existing competitive baseline methods in recommendations. [ABSTRACT FROM AUTHOR]

Link prediction in bipartite networks aims to identify or predict possible links between nodes of different types based on known network information. However, most existing studies predominantly focus on monopartite networks, neglecting the intrinsic properties unique to bipartite networks, such as the intricate high-order relationships between nodes. Both explicit relations (representing low-order information) and implicit relations (representing high-order information) play essential roles in predicting the evolution of bipartite networks, and they are indispensable and mutually reinforce each other. To fully leverage their potential in addressing the link prediction problem, we propose a novel framework from the perspective of network representation. This framework not only effectively integrates explicit relations and implicit relations, but also preserves the local and global structure of bipartite networks. Specifically, the probability of a link between two nodes of different types is determined by the linear sum of the contribution values of the mutually connected nodes in their respective common neighbors. Implicit relations are then used to preserve the local structure during the network representation. Furthermore, we implement optimization using a relaxed majorization-minimization algorithm, offering the advantage of uncovering high-quality local minima. Our proposed framework has undergone extensive testing on eight real-world datasets, and the results unequivocally demonstrate its significant superiority over state-of-the-art methods. [ABSTRACT FROM AUTHOR]