The roles of Braun Lipoprotein in inducing tolerance of bovine endometrium infected by Escherichia coli.

Escherichia coli (E. coli), a Gram-negative bacterium, is the primary pathogen responsible for endometritis in dairy cattle. The outer membrane components of E. coli, namely lipopolysaccharide (LPS) and bacterial lipoprotein, have the capacity to trigger the host's innate immune response through pattern recognition receptors (PRRs). Tolerance to bacterial cell wall components, including LPS, may play a crucial role as an essential regulatory mechanism during bacterial infection. However, the precise role of Braun lipoprotein (BLP) tolerance in E. coli-induced endometritis in dairy cattle remains unclear. In this study, we aimed to investigate the impact of BLP on the regulation of E. coli infection-induced endometritis in dairy cattle. The presence of BLP was found to diminish the expression and release of proinflammatory cytokines (IL-8 and IL-6), while concurrently promoting the expression and release of the anti-inflammatory cytokine IL-10 in endometrial epithelial cells (EECs). Furthermore, BLP demonstrated the ability to impede the activation of MAPK (ERK and p38) and NF-κB (p65) signaling pathways, while simultaneously enhancing signaling through the STAT3 pathway in EECs. Notably, BLP exhibited a dual role, acting both as an activator of TLR2 and as a regulator of TLR2 activation in LPS- and E. coli-treated EECs. In E. coli-infected endometrial explants, the presence of BLP was noted to decrease the release of proinflammatory cytokines and the expression of HMGB1, while simultaneously enhancing the release of anti-inflammatory cytokines. Collectively, our findings provide evidence that the bacterial component BLP plays a protective role in E. coli-induced endometritis in dairy cattle.

Prostaglandin D2 is involved in the regulation of inflammatory response in Staphylococcus aureus-infected mice macrophages.

Staphylococcus aureus (S. aureus) is one of the most infamous and widespread bacterial pathogens, causing a hard-to-estimate number of uncomplicated skin infections and probably hundreds of thousands to millions of more severe, invasive infections globally per year. S. aureus may also be acquired from animals, especially in the livestock industry. The interaction mechanism of host and S. aureus has significance for finding ways to against S. aureus infection and control inflammatory response of host, while the molecular biological activities after S. aureus infection, particular in inflammatory and immune cells are not fully clear. The present study aimed to explore whether pattern recognition receptors (PRRs) mediate prostaglandin D2 (PGD2) synthesis and PGD2 participates in the regulation of inflammatory response in macrophages during S. aureus infection or synthetic bacterial lipopeptide (Pam2CSK4) stimulation. PGD2 secretion level was enhanced by mice peritoneal macrophages infected with the S. aureus. The results indicated that PGD2 secretion was impaired in S. aureus infected-macrophages from toll-like receptors 2 (TLR2)-deficient and NLR pyrin domain-containing 3 (NLRP3)-deficient mice. PGD2 synthetase (hematopoietic PGD synthase, HPGDS) inhibitors could reduce the activation of macrophage mitogen-activated protein kinase (MAPK)/nuclear factor-κ-gene binding (NF-κB) signaling pathways. HPGDS inhibition impaired cytokines (TNF-α, IL-1ß, IL-10 and RANTES) secretion and macrophage phagocytosis during S. aureus infection. In addition, inhibition of endogenous PGD2 synthesis was unable to affect the TLR2 and NLRP3 expression in S. aureus-infected macrophages. Taken together, macrophage PGD2 secretion after S. aureus infection depended on receptors TLR2 and NLRP3, and the induced PGD2 participated in the regulation of inflammatory response in S. aureus-infected macrophages. Interestingly, it was found that exogenous PGD2 down-regulated the cytokines secretion and had no effect on phagocytosis in the S. aureus-infected macrophages.

Determination of ochratoxin A in licorice extract based on modified immunoaffinity column clean-up and HPLC analysis.

Contamination of ochratoxin A (OTA) is a common concern for the quality and safety of licorice and its derivatives, while their complex sample matrices always restrict the monitoring and regulation of OTA. Taking the much more concentrated and complicated licorice extract as the representative, a modified analysis method was established for OTA by HPLC. Parameters were comprehensively investigated based on liquid-liquid extraction and immunoaffinity column clean-up. In comparison to other methods, the developed method achieved effective clean-up efficiency and selectivity without tedious procedures and specialized instrumentation. Good linearity (R2 ≥0.9995), low LOD/LOQ (0.10 µg/kg/0.33 µg/kg), and satisfactory recovery (90.0%-96.4%, RSDs <7.0%) indicated the satisfactory sensitivity and reliability of the method. In addition, the applicability and robustness of the method was demonstrated by the analysis of large numbers of licorice extract samples. It is noteworthy that 66.5% of 176 samples were contaminated with OTA, while the concentrations of 9.1% of samples exceeded the maximum limit (ML, 80 µg/kg) defined by the EU. On account of the high contamination frequency and broad concentration range of OTA, the daily intake limit of licorice extract was preliminarily determined to be 123.18-123.93 g/day (chronic exposure) and 24.24 g/day (acute exposure), indicating a potential of acute risk through daily exposure. This calls for improved supervision and regulation for OTA contamination in licorice samples. This study suggests a prospective option for the efficient determination and routine monitoring of OTA in licorice and its derivatives, simultaneously providing a valuable data base for its health risk assessment.

Prostaglandin D2 regulates Escherichia coli-induced inflammatory responses through TLR2, TLR4, and NLRP3 in macrophages.

Prostaglandin D2 (PGD2) synthesis is closely associated with the innate immune response mediated by pattern recognition receptors (PPRs). We determined PGD2 synthesis whether mediated by Toll-like receptor 2 (TLR2), TLR4 and Nod-like receptor pyrin domain-containing protein 3 (NLRP3) in Escherichia coli (E. coli)-, lipopolysaccharide (LPS)- and Braun lipoprotein (BLP)-stimulated macrophages. Our data demonstrate that TLR2, TLR4, and NLRP3 could regulate the synthesis of PGD2 through cyclo-oxygenase-2 (COX-2) and hematopoietic PGD synthase (H-PGDS) in E. coli-, LPS- or BLP-stimulated macrophages, suggesting that TLR2, TLR4, and NLRP3 are critical in regulating PGD2 secretion by controlling PGD2 synthetase expression in E. coli-, LPS- or BLP-stimulated macrophages. The H-PGDS (a PGD2 specific synthase) inhibitor pre-treatment could down-regulate the secretion of TNF-α, RANTES and IL-10 in LPS- and E. coli-stimulated macrophage. Meanwhile, H-PGDS inhibitor could down-regulate the secretion of TNF-α, while up-regulated RANTES and IL-10 secretion in BLP-stimulated macrophages, suggesting that PGD2 could regulate the secretion of cytokines and chemokines in E. coli-, LPS- or BLP-stimulated macrophages. Furthermore, exogenous PGD2 regulates the secretion of cytokines and chemokines through activation of MAPK and NF-κB signaling pathways after E. coli-, LPS- or BLP stimulation in macrophages. Taken together, PGD2 is found able to regulate E. coli-induced inflammatory responses through TLR2, TLR4, and NLRP3 in macrophages.

Besides TLR2 and TLR4, NLRP3 is also involved in regulating Escherichia coli infection-induced inflammatory responses in mice.

The host Toll-like Receptor-2 (TLR2) and Toll-like Receptor-4 (TLR4) play critical roles in defense against Escherichia coli (E. coli) infection is well-known. The NLR pyrin domain-containing 3 (NLRP3) inflammasome is also an important candidate during the host-recognized pathogen, while the roles of NLRP3 in the host inflammatory response to E. coli infection remains unclear. This study aimed to explore the roles of NLRP3 in regulating the inflammatory response in E. coli infection-induced mice. Our result indicated that compared to wild-type mice, the TLR2-deficient (TLR2-/-), TLR4-deficient (TLR4-/-), and NLRP3-deficient (NLRP3-/-) mice had significant decrease in liver damage after stimulation with Lipopolysaccharide (LPS, 1 µg/mL), Braun lipoprotein (BLP, 1 µg/mL), or infected by WT E. coli (1 × 107 CFU, MOI 5:1). Meanwhile, compared with wild-type mice, the TNF-α and IL-1ß production in serum decreased in TLR2-/-, TLR4-/-, and NLRP3-/- mice after LPS, BLP treatment, or WT E. coli infection. In macrophages from NLRP3-/- mice showed significantly reduced secretion of TNF-α and IL-1ß in response to stimulation with LPS, BLP, or WT E. coli infection compared with macrophages from wild-type mice. These results indicate that besides TLR2 and TLR4, NLRP3 also plays a critical role in host inflammatory responses to defense against E. coli infection, and might provide a therapeutic target in combating disease with bacterium infection.

Braun Lipoprotein Protects against Escherichia coli-Induced Inflammatory Responses and Lethality in Mice.

Escherichia coli (E. coli), a Gram-negative bacterium, is an important pathogen that causes several mammalian diseases. The outer membrane components of E. coli, namely, lipopolysaccharide (LPS) and bacterial lipoprotein, can induce the host innate immune response through pattern recognition receptors (PRRs). However, the detailed roles of the E. coli Braun lipoprotein (BLP) in the regulation of host inflammatory response to E. coli infection remain unclear. In this study, we sought to determine the effects of BLP on E. coli-induced host inflammatory response and lethality using mouse models. Experiments using the E. coli DH5α strain (BLP-positive), E. coli JE5505 strain (BLP-negative), and E. coli JE5505 strain combined with BLP indicated that the presence of BLP could alleviate mortality and organ (liver and lung) damage and decrease proinflammatory cytokine (tumor necrosis factor alpha [TNF-α] and interleukin-1ß [IL-1ß]) and chemokine (regulated on activation normal T-cell expressed and secreted [RANTES]) production in mouse serum and organs. Conversely, E. coli JE5505, E. coli DH5α strain, and E. coli JE5505 combined with BLP treatment induce enhanced anti-inflammatory cytokine (interleukin 10 [IL-10]) production in mouse serum and organs. In addition, BLP could regulate the secretion of proinflammatory cytokines (TNF-α and IL-1ß), chemokines (RANTES), and anti-inflammatory factors (IL-10) through mitogen-activated protein kinase (MAPK) and nuclear factor-kappaB (NF-κB) signaling pathways in macrophages. Altogether, our results demonstrate that the bacterial component BLP plays crucial and protective roles in E. coli-infected mice, which may influence the outcome of inflammation in host response to E. coli infection. IMPORTANCE In this study, we investigated the roles of bacterial outer membrane component BLP in regulating inflammatory responses and lethality in mice that were induced by a ubiquitous and serious pathogen, Escherichia coli. BLP could alleviate the mortality of mice and organ damage, as well as decrease proinflammatory cytokines and chemokine production and enhance anti-inflammatory cytokine production in mouse serum and organs. Overall, our results demonstrate that the bacterial component BLP plays crucial and protective roles in E. coli-infected mice through regulating the production of an inflammatory mediator, which may influence the outcome of inflammation in host response to E. coli infection. Our findings provide new information about the basic biology involved in immune responses to E. coli and host-bacterial interactions, which have the potential to translate into novel approaches for the diagnosis and treatment of E. coli-related medical conditions, such as bacteremia and sepsis.

Micro-Supervised Disturbance Learning: A Perspective of Representation Probability Distribution.

The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expressive representation learning methods which depends on as few labels as possible. To address above issues, the small-perturbation ideology is firstly introduced on the representation learning model based on the representation probability distribution. The positive small-perturbation information (SPI) which only depend on two labels of each cluster is used to stimulate the representation probability distribution and then two variant models are proposed to fine-tune the expected representation distribution of Restricted Boltzmann Machine (RBM), namely, Micro-supervised Disturbance Gaussian-binary RBM (Micro-DGRBM) and Micro-supervised Disturbance RBM (Micro-DRBM) models. The Kullback-Leibler (KL) divergence of SPI is minimized in the same cluster to promote the representation probability distributions to become more similar in Contrastive Divergence (CD) learning. In contrast, the KL divergence of SPI is maximized in the different clusters to enforce the representation probability distributions to become more dissimilar in CD learning. To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has no external stimulation. Experimental results demonstrate that the proposed deep Micro-DL architecture shows better performance in comparison to the baseline method, the most related shallow models and deep frameworks for clustering.

Prostaglandin E receptor 2 mediates the inducible effects of prostaglandin E2 on expression of growth factors and enzymes in cattle endometrial epithelial cells and explants.

Prostaglandin E2 (PGE2 ) is able to induce the expression of several growth factors and enzymes in cattle endometria. However, the specific type of PGE2 receptors which mediates this effect is not fully clear. In this study, the role of prostaglandin E receptor 2 (PTGER2) in PGE2 -mediated induction of growth factors and enzymes expression in cattle endometrial explants and epithelial cells were investigated. PTGER2 was blocked by a PTGER2 antagonist, AH6809, before PGE2 treatment, then the mRNA and protein expression levels of several growth factors and enzymes were compared with that in PGE2 alone treatment group by real-time RT-PCR and Western blotting analysis in endometrial epithelial cells and explants. Results indicated that PGE2 significantly increased the mRNA and protein levels of these growth factors and enzymes, while the rates of increment in the expression of these growth factors and enzymes were inhibited by AH6809. In addition, a PTGER2 agonist, butaprost, significantly increased the expression levels of these growth factors and enzymes, and the effect could be blocked by AH6809. In conclusion, PTGER2 was found to be one dominant receptor mediating the inducible effects of PGE2 on the expression of these growth factors and enzymes in cattle endometrial explants and epithelial cells.

Codonopsis pilosula polysaccharides attenuate Escherichia coli-induced acute lung injury in mice.

Acute lung injury (ALI) is an inflammatory lung disease that is caused by bacterial infection. Lipopolysaccharide (LPS), a prototype pathogen-associated molecular pattern (PAMP) from Gram-negative bacteria such as Escherichia coli (E. coli), is an essential risk factor for ALI. LPS and E. coli induced the activation of mitogen-activated protein kinase (MAPK) and nuclear factor kappaB (NF-κB) signaling pathways, which led to the increasing immune molecule transcription, including pro-inflammatory cytokine and chemokine secretion. Codonopsis pilosula polysaccharides (CPPS) exhibit various biological activities and pharmacological effects. However, the effect of CPPS on ALI caused by LPS stimulation or E. coli infection remains unclear. Our results showed that CPPS (6.25, 12.5, 25, or 50 µg mL-1) could attenuate the secretion of TNF-α and IL-1ß and impair the phosphorylation of ERK, p38 and p65 in E. coli-infected macrophages without causing toxic reactions. In addition to regulating the secretion of pro-inflammatory cytokines and the activation of MAPK and NF-κB signaling pathways, CPPS could enhance bacterial phagocytosis and intracellular killing in macrophages, and inhibit the bacterial growth of E. coli. In vivo experiments showed that CPPS attenuated LPS- and E. coli-induced lung damage in mice, which was characterized by decreased pro-inflammatory cytokine (TNF-α, IL-1ß and IL-6) and chemokine (RANTES) production and production of the biomarkers of tissue damage (HABP2 and HMGB1) in the lungs. Altogether, this study demonstrated that CPPS have a protective effect on the lungs in LPS- and E. coli-induced ALI mouse models, suggesting that CPPS could be a potential drug for the treatment of ALI.

TLR2, TLR4, and NLRP3 mediated the balance between host immune-driven resistance and tolerance in Staphylococcus aureus-infected mice.

Staphylococcus aureus (S. aureus) is a gram-positive pathogen that can cause infectious diseases in mammals. S. aureus-induced host innate immune responses have a relationship with Toll-like receptor 2 (TLR2), TLR4, and Nod-like receptor pyrin domain-containing protein 3 (NLRP3). However, the detailed roles of TLR2, TLR4, and NLRP3 in regulating the host inflammatory response to S. aureus infection remain unclear. Our data indicated that the S. aureus-induced mortality was aggravated by deficiency of TLR2, TLR4, and NLRP3 in mice. In the subsequent experiment, we found that during S. aureus infection, the roles of TLR2, TLR4, and NLRP3 seemed to be different at multiple timepoints. The deficiency of TLR2, TLR4, or NLRP3 attenuated the expression of High-mobility group box protein 1 (HMGB1) and Hyaluronic acid-binding protein 2 (HABP2), which is accompanied by decreased proinflammatory cytokine (TNF-α), chemokine (RANTES), and anti-inflammatory cytokine (IL-10) production in lungs and serum at 3 h and 6 h post-infection. However, with S. aureus infection prolonged (24 h post-infection), the trend was diametrically opposite. The results showed that deficiency of TLR2, TLR4, or NLRP3 aggravated HABP2 and HMGB1 expression, which is accompanied by enhanced proinflammatory cytokine (TNF-α), chemokine (RANTES), and anti-inflammatory cytokine (IL-10) production in lungs and serum. These results were consistent with the data observed in S. aureus-infected bone marrow-derived macrophages (BMDMs). All these results suggested that during S. aureus infection, TLR2, TLR4, and NLRP3 has time-dependent effect in regulating the balance between immune-driven resistance and tolerance.

Occurrence and exposure risk assessment of phthalate esters in edible plant oils with a high-frequency import rate in west China.

Phthalate esters (PAEs) are ubiquitous pollutants in the environment with toxicological and epidemiological effects for humans. As one of the daily necessities, edible plant oil is an important exposure source of PAEs, due to the inevitable contact with PAE-containing materials and the intrinsic lipid solubility of PAEs. However, limited information is currently available on the exposure risk of PAEs in commercial plant oil. This study was aimed at investigating the occurrence and risk assessment of PAEs in plant oils with a high-frequency import rate in west China. The analysis method was referenced to the Chinese national standard for the determination of PAEs in food. Results indicated that PAEs (mainly including DBP and DEHP) were ubiquitous contaminants in imported plant oils with the detectable rate being up to 56.83% in 366 samples. The detected concentrations were in the range of 0.10-3.20 mg kg-1 (median 0.28 mg kg-1) for dibutyl phthalate (DBP) and 0.25-1.95 mg kg-1 (median 0.44 mg kg-1) for bis(2-ethylhexyl)phthalate (DEHP). Based on an integrated probabilistic analysis method, the values of non-carcinogenic risk were lower than 1 in all cases, indicating that there would be an unlikely incremental non-carcinogenic risk to humans. Generally, the carcinogenic risk of DEHP was lower than the upper acceptable carcinogenic risk level (<10-4), while 50.40% of the carcinogenic risk exceeded the lower acceptable carcinogenic risk level (>10-6). Besides, diverse health risks were obviously shown and discussed for different categories of plant oils. The obtained results in this study could provide valuable information to understand the contamination status and health risk of PAEs in plant oil and improve the relative supervision and regulation. And the proposed strategy suggests a potential application for health risk assessment of other contaminants in food or even environments.

CRL: Collaborative Representation Learning by Coordinating Topic Modeling and Network Embeddings.

Network representation learning (NRL) has shown its effectiveness in many tasks, such as vertex classification, link prediction, and community detection. In many applications, vertices of social networks contain textual information, e.g., citation networks, which form a text corpus and can be applied to the typical representation learning methods. The global context in the text corpus can be utilized by topic models to discover the topic structures of vertices. Nevertheless, most existing NRL approaches focus on learning representations from the local neighbors of vertices and ignore the global structure of the associated textual information in networks. In this article, we propose a unified model based on matrix factorization (MF), named collaborative representation learning (CRL), which: 1) considers complementary global and local information simultaneously and 2) models topics and learns network embeddings collaboratively. Moreover, we incorporate the Fletcher-Reeves (FR) MF, a conjugate gradient method, to optimize the embedding matrices in an alternative mode. We call this parameter learning method as AFR in our work that can achieve convergence after a few numbers of iterations. Also, by evaluating CRL on topic coherence and vertex classification using several real-world data sets, our experimental study shows that this collaborative model not only can improve the performance of topic discovery over the baseline topic models but also can learn better network representations than the state-of-the-art context-aware NRL models.

Self-Training Enhanced: Network Embedding and Overlapping Community Detection With Adversarial Learning.

Network embedding (NE) aims to encode the relations of vertices into a low-dimensional space. After NE, we can obtain the learned vectors of vertices that preserve the proximity of network structures for subsequent applications, e.g., vertex classification and link prediction. In existing NE models, they usually exploit the skip-gram with a negative sampling method to optimize their objective functions. Generally, this method learns the vertex representation only from the local connectivity of vertices (i.e., neighbors). However, there is a larger scope of vertex connectivity in real-world scenarios: a vertex may have multifaceted aspects and should belong to overlapping communities. Taking a social network as the overlapping example, a user may subscribe to the channels of politics, economy, and sports simultaneously, but the politics share more common attributes with the economy and less with the sports. In this article, we propose an adversarial learning approach (ACNE) for modeling overlapping communities of vertices. Specifically, we map the association between communities and vertices into an embedding space. Moreover, we take further research on enhancing our ACNE with the following two operations. First, in the initialization stage, we adopt a walking strategy with perception to obtain paths containing more possible boundary vertices to improve overlapping community detection. Then, after representation learning with ACNE, we use soft community assignments from a simple classifier as supervision to update the weights of ACNE. This self-training mechanism referred to as ACNE-ST can help ACNE to achieve better performance. Experimental results demonstrate that the proposed methods, including ACNE and ACNE-ST, can outperform the state-of-the-art models on the subsequent tasks of vertex classification and overlapping community detection.

Adversarial Caching Training: Unsupervised Inductive Network Representation Learning on Large-Scale Graphs.

Network representation learning (NRL) has far-reaching effects on data mining research, showing its importance in many real-world applications. NRL, also known as network embedding, aims at preserving graph structures in a low-dimensional space. These learned representations can be used for subsequent machine learning tasks, such as vertex classification, link prediction, and data visualization. Recently, graph convolutional network (GCN)-based models, e.g., GraphSAGE, have drawn a lot of attention for their success in inductive NRL. When conducting unsupervised learning on large-scale graphs, some of these models employ negative sampling (NS) for optimization, which encourages a target vertex to be close to its neighbors while being far from its negative samples. However, NS draws negative vertices through a random pattern or based on the degrees of vertices. Thus, the generated samples could be either highly relevant or completely unrelated to the target vertex. Moreover, as the training goes, the gradient of NS objective calculated with the inner product of the unrelated negative samples and the target vertex may become zero, which will lead to learning inferior representations. To address these problems, we propose an adversarial training method tailored for unsupervised inductive NRL on large networks. For efficiently keeping track of high-quality negative samples, we design a caching scheme with sampling and updating strategies that has a wide exploration of vertex proximity while considering training costs. Besides, the proposed method is adaptive to various existing GCN-based models without significantly complicating their optimization process. Extensive experiments show that our proposed method can achieve better performance compared with the state-of-the-art models.

Staphylococcus aureus lipoproteins play crucial roles in inducing inflammatory responses and bacterial internalization into bovine mammary epithelial cells.

Bovine mastitis is caused by bacterial infection and characterized by inflammatory and infectious processes. Staphylococcus aureus frequently causes subclinical mastitis in dairy cows. In this study, we aimed to investigate the roles of S. aureus lipoproteins in inducing inflammatory responses and in mediating bacterial internalization into bovine mammary epithelial cells (bMECs). The results showed that TLR2 expression in bMECs infected with S. aureus isogenic mutant deficient in lipoprotein maturation was decreased compared to that in bMECs infected with wild-type S. aureus. Lipoproteins from S. aureus and the engagement of TLR2 were essential for inducing the activation of MAPK and NF-κB signaling, and stimulating the secretion of the inflammatory mediators tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and C-X-C motif chemokine ligand 8 (CXCL8). The production of prostaglandin E2 (PGE2) and the expression of PTGS2 in S. aureus-infected bMECs were dependent on the presence of bacterial lipoproteins. Furthermore, bacterial lipoproteins contributed to S. aureus internalization into bMECs. These findings suggest the S. aureus lipoproteins are key immunobiologically active compounds that trigger inflammatory responses in bMECs and play an important role in S. aureus internalization into bMECs.

TAM: Targeted Analysis Model With Reinforcement Learning on Short Texts.

Mining topics on social media (e.g., Twitter and Facebook) is an important task for various applications, such as hot topic discovery, advertising, and promotion activities. Topic modeling techniques are helpful to find out topics that people are talking about. However, current full-analysis models cannot perform well on a focused analysis task-find out all topics related to one particular area in short documents. One reason is that the targeted topic is usually sparse in the corpus of short texts. Another one is, during clustering, even minor errors may compound and render the model useless. This article studies these problems and proposes a targeted analysis model (TAM) with reinforcement learning (RL) to extract any specific topic in a given corpus and perform fine-grained topic generation. In this work, we design a reward function of RL to prevent the false propagation problem induced by Gibbs sampling during the clustering. We amend the targeted topic modeling techniques to the case of RL and use policy search combined with the Gibbs EM algorithm for parameter estimation. Metrics of F1 score and the proposed normalized mutual information-F1 are exploited for the evaluation of clustering and topic generation, respectively. Our experiments have demonstrated that TAM can outperform state-of-the-art models-specifically achieving 25.7% improvement on the F1 score for binary clustering on average.

A finite element analysis on comparing the stability of different posterior fixation methods for thoracic total en bloc spondylectomy.

OBJECTIVE: To compare the spinal stability with different fixation methods after thoracic TES using finite element analysis METHODS: The spinal finite element model was established from a healthy volunteer, and the validity was verified. The models of T8 thoracic total en bloc spondylectomy (TES) with and without artificial vertebral body were established combination with different fixation methods: the first was long segment fixation with fixed segments T5-7, T9-11; the second was short segment fixation with fixed segments T6-7, T9-10; the third was modified short segment with a pair of vertebral body screws on T7 and T9 added on the basis of short segment fixation. The motions of each model in standing state were simulated in software. The range of motion (ROM) and internal fixation stress changes were analyzed. RESULTS: When anterior support was effective, the three fixation methods could effectively maintain the stability of the spine. However, when anterior support failed, the ROM of the long segment fixation group and the short segment fixation group in the flexion-extension directions was significantly higher than that of when the anterior support existed, while the modified short segment fixation group had no significant changes. Meanwhile, the stress of internal fixation in the long segment fixation group and the short segment fixation group were greatly increased. However, there were no significant changes in modified short segment fixation group. CONCLUSION: After TES, the presence of the thoracic cage gives partial anterior stabilization. When the anterior support failed, the modified short segment fixation method can provide better stability.

A Common Topic Transfer Learning Model for Crossing City POI Recommendations.

With the popularity of location-aware devices (e.g., smart phones), large amounts of location-based social media data (i.e., user check-in data) are generated, which stimulate plenty of works on personalized point of interest (POI) recommendations using machine learning techniques. However, most of the existing works could not recommend POIs in a new city to a user where the user and his/her friends have never visited before. In this paper, we propose a common topic transfer learning graphical model-the common-topic transfer learning model (CTLM)-for crossing-city POI recommendations. The proposed model separates the city-specific topics (or features) of each city from the common topics (or features) shared by all cities, to enable the users' real interests in the source city to be transferred to the target city. By doing so, the ill-matching problem between users and POIs from different cities can be well addressed by preventing the real interests of users from being influenced by the city-specific features. Furthermore, we incorporate the spatial influence into our proposed model by introducing the regions' accessibility. As a result, the co-occurrence patterns of users and POIs are modeled as the aggregated result from these factors. To evaluate the performance of the CTLM, we conduct extensive experiments on Foursquare and Twitter datasets, and the experimental results show the advantages of CTLM over the state-of-the-art methods for the crossing-city POI recommendations.

Simultaneous determination of 106 pesticides in nuts by LC-MS/MS using freeze-out combined with dispersive solid-phase extraction purification.

As a result of the low water content and high fat matrices in nuts, it is very difficult to simultaneously determine multi-pesticides in trace levels. Here, a sample pretreatment method was developed in which, microwave-assisted solvent extraction was firstly used to extract pesticides, and then a two-step cleanup method was conducted combining freeze-out with dispersive solid-phase extraction to remove the lipidic matrix. By this way, 106 pesticides were simultaneously determined in the complicated nut sample by using an ultra-high pressure liquid chromatography coupled with a tandem mass spectrometer. Average recoveries were 75.3-119.3% with relative standard deviations < 14% at three concentration levels. The limits of detection and quantification were in the ranges of 0.3-3.0 and 1.0-10.0 µg/kg, respectively. Furthermore, the method was successfully applied to the determination of pesticides in 180 commercial nut samples.

[Simultaneous determination of eight additives in polymer food packaging materials by ultra-performance liquid chromatography-electrospray ionization tandem mass spectrometry].

An ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was proposed for the simultaneous determination of eight additives (Irgafos 168 (tri(2.4-di-tert-butylphenyl)phosphite), Irganox 1076 (octadecyl-ß-(4-hydroxy-3, 5-di-tert-butylphenyl)propionate), Irganox 1010 (pentaerythritol tetrakys 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate), BHA (butyl hydroxy anisole), TBHQ (tertiary butylhydroquinone), PG (propyl gallate), DG (dodecyl gallate), UV-326 (2-( 2'-hydroxyl-3'-tert-butyl-5'-methylphenyl)-5-chlorobenzotriazole) in food packaging materials. After extracted by chloromethane through ultrasonic extraction, the samples were analyzed by UPLC-MS/MS. The chromatographic conditions were optimized, and the best separation was obtained on a Waters BEH-C18 column (50 mm x 2. 1 mm, 1.7 µm) with gradient elution of 0. 05% acetic acid solu- tion and methanol. The analysis was performed by UPLC-MS/MS with electrospray ionization (ESI) source in switching between the positive and negative ion modes in one run for multiple reaction monitoring. The eight additives showed good linear relationships in the ranges with all the correlation coefficients (R2) more than 0. 993. The limits of detection (LODs, S/N= 3) and limits of quantitation (LOQs, S/N= 10) of this method were 0. 13-5.50 µg/L and 0.45-17.50 µg/L, respectively. The recoveries were in the range of 63. 9% - 127. 0% with all the RSDs < 15. 8% (n= 6). This method is simple, accurate and effective for the analysis of the eight additives in food packaging materials.