The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their ...movement behaviors can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modeling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. In this work, we first develop a set of novel techniques to tackle the challenge of efficient discovery of gathering patterns on archived trajectory dataset. Afterwards, since trajectory databases are inherently dynamic in many real-world scenarios such as traffic monitoring, fleet management and battlefield surveillance, we further propose an online discovery solution by applying a series of optimization schemes, which can keep track of gathering patterns while new trajectory data arrive. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.
Adapting to User Interest Drift for POI Recommendation Yin, Hongzhi; Zhou, Xiaofang; Cui, Bin ...
IEEE transactions on knowledge and data engineering,
2016-Oct.-1, 2016-10-1, 20161001, Letnik:
28, Številka:
10
Journal Article
Recenzirano
Point-of-Interest recommendation is an essential means to help people discover attractive locations, especially when people travel out of town or to unfamiliar regions. While a growing line of ...research has focused on modeling user geographical preferences for POI recommendation, they ignore the phenomenon of user interest drift across geographical regions, i.e., users tend to have different interests when they travel in different regions, which discounts the recommendation quality of existing methods, especially for out-of-town users. In this paper, we propose a latent class probabilistic generative model Spatial-Temporal LDA (ST-LDA) to learn region-dependent personal interests according to the contents of their checked-in POIs at each region. As the users' check-in records left in the out-of-town regions are extremely sparse, ST-LDA incorporates the crowd's preferences by considering the public's visiting behaviors at the target region. To further alleviate the issue of data sparsity, a social-spatial collective inference framework is built on ST-LDA to enhance the inference of region-dependent personal interests by effectively exploiting the social and spatial correlation information. Besides, based on ST-LDA, we design an effective attribute pruning (AP) algorithm to overcome the curse of dimensionality and support fast online recommendation for large-scale POI data. Extensive experiments have been conducted to evaluate the performance of our ST-LDA model on two real-world and large-scale datasets. The experimental results demonstrate the superiority of ST-LDA and AP, compared with the state-of-the-art competing methods, by making more effective and efficient mobile recommendations.
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•We aimed to improve the flavor profile of Xiaoqu liquor.•We added Saccharomycopsis fibuligera and Saccharomyces cerevisiae to the culture.•Analysis revealed increased diversity of ...Xiaoqu liquor.•We elucidated the chemical composition and main microorganisms involved.
Xiaoqu liquor is a type of distilled spirit in China prepared on a small scale from a small solid starter culture. Although this liquor is popular in southwestern China, it can have a dull taste, limiting its market. To improve the flavour profile of Xiaoqu liquor, we selected two functional yeast strains (Saccharomycopsis fibuligera and Saccharomyces cerevisiae) from Zaopei (fermented grain) of Baijiu liquor and used them for Xiaoqu liquor fermentation. Compared with traditional Xiaoqu (Starter), bioaugmentation inoculation increased the glucoamylase and acidic protease activities and the ethanol synthesis rate, while decreasing the acidity of the Zaopei (fermented grains) in the early stage of fermentation. By the end of the fermentation process, the alcohol and ester content had also increased by 42.5% and 11.8%, respective, and that of aldehydes and ketones, and heterocyclic compounds decreased by 73.7% and 77.1%, respectively. Traditional isolation and high-throughput sequencing were employed to analyse the microorganisms in the Zaopei. Bioaugmentation inoculation increased the microbial diversity of Xiaoqu liquor during the fermentation process. The dominant fungus during fermentation using the two types of starter cultures was S. cerevisiae, whereas the dominant bacteria was Pseudomonas, followed by Bacillus, Weissella, Lactobacillus, and Bacteroides. Principal component analysis of the bacterial community structure and flavour substances in the Zaopei produced using the two strains revealed that there were few differences between the two liquors and that inoculation with functional yeasts may not change the flavour substances in Xiaoqu liquor. However, correlation analysis showed that Escherichia Shigella, Terrisporobacter, Bacillus, Clostridium, and Prevotellaceae are the main microorganisms in the Xiaoqu liquor fermentation process. These results lay the foundation to improve the quality of Xiaoqu liquor.
A novel technique to design substrate integrated waveguide (SIW) resonator with harmonic suppression is proposed. The spurious band arising from the harmonics is designed as well as the fundamental ...passband. The bandwidth of the spurious band is designed to be zero to realise harmonic suppression. At the same time, the fundamental properties of the SIW are almost not affected, and no additional circuit area is required. The harmonic suppression and the fundamental passband can be designed independently of each other. An SIW resonator with harmonic suppression and an SIW filter with wide stopband are fabricated and measured to verify the proposed technique. The results agree with expectation well.
The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are ...trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold
θ
, the TS-Join returns all pairs of trajectories from the two sets with similarity above
θ
. This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction.
With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.
LAG3 is the most promising immune checkpoint next to PD-1 and CTLA-4. High LAG3 and FGL1 expression boosts tumor growth by inhibiting the immune microenvironment. This review comprises four sections ...presenting the structure/expression, interaction, biological effects, and clinical application of LAG3/FGL1. D1 and D2 of LAG3 and FD of FGL1 are the LAG3-FGL1 interaction domains. LAG3 accumulates on the surface of lymphocytes in various tumors, but is also found in the cytoplasm in non-small cell lung cancer (NSCLC) cells. FGL1 is found in the cytoplasm in NSCLC cells and on the surface of breast cancer cells. The LAG3-FGL1 interaction mechanism remains unclear, and the intracellular signals require elucidation. LAG3/FGL1 activity is associated with immune cell infiltration, proliferation, and secretion. Cytokine production is enhanced when LAG3/FGL1 are co-expressed with PD-1. IMP321 and relatlimab are promising monoclonal antibodies targeting LAG3 in melanoma. The clinical use of anti-FGL1 antibodies has not been reported. Finally, high FGL1 and LAG3 expression induces EGFR-TKI and gefitinib resistance, and anti-PD-1 therapy resistance, respectively. We present a comprehensive overview of the role of LAG3/FGL1 in cancer, suggesting novel anti-tumor therapy strategies.
Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and ...diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and social knowledge to recommend the next POI for a location-based social network user. The framework mainly contains four modules. First, in the graph construction module, a novel type of knowledge graph—the sequential knowledge graph, which is sensitive to the check-in order of POIs—is built to model users’ check-in patterns. To deal with the problem of data sparsity, a meta-learning module based on latent embedding optimization is then introduced to generate user-conditioned parameters of the subsequent sequential-knowledge-aware embedding module, where representation vectors of entities (nodes) and relations (edges) are learned. In this embedding module, gated recurrent units are adapted to distill intra- and inter-sequential knowledge graph information. We also design a novel knowledge-aware attention mechanism to capture information surrounding a given node. Finally, POI recommendation is provided by inferring potential links of knowledge graphs in the prediction module. Evaluations on three real-world check-in datasets show that Meta-SKR can achieve high recommendation accuracy even with sparse data.
Antibody glycosylation is a common post-translational modification and has a critical role in antibody effector function. The use of glycoengineering to produce antibodies with specific glycoforms ...may be required to achieve the desired therapeutic efficacy. However, the modified molecule could have unusual behavior during development due to the alteration of its intrinsic properties and stability. In this study, we focused on the differences between glycosylated and deglycosylated antibodies, as aglycosyl antibodies are often chosen when effector function is not desired or unimportant. We selected three human IgG1 antibodies and used PNGase F to remove their oligosaccharide chains. Although there were no detected secondary or tertiary structural changes after deglycosylation, other intrinsic properties of the antibody were altered with the removal of oligosaccharide chains in the Fc region. The apparent molecular hydrodynamic radius increased after deglycosylation based on size-exclusion chromatography analysis. Deglycosylated antibodies exhibited less thermal stability for the CH2 domain and less resistance to GdnHCl induced unfolding. Susceptibility to proteolytic cleavage demonstrated that the deglycosylated version was more susceptible to papain. An accelerated stability study revealed that deglycosylated antibodies had higher aggregation rates. These changes may impact the development of aglycosyl antibody biotherapeutics.
Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro ...experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease.
In this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA-disease associations.
The experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma, Lung Neoplasm, and Esophageal Neoplasms. As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA-disease association databases.
These prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at http://220.171.34.3:81/ .
Methamphetamine (Meth) is a potent psychostimulant with well-established hepatotoxicity. Gut microbiota-derived short-chain fatty acids (SCFAs) have been reported to yield beneficial effects on the ...liver. In this study, we aim to further reveal the mechanisms of Meth-induced hepatic injuries and investigate the potential protective effects of SCFAs. Herein, mice were intraperitoneally injected with 15 mg/kg Meth to induce hepatic injuries. The composition of fecal microbiota and SCFAs was profiled using 16 S rRNA sequencing and Gas Chromatography/Mass Spectrometry (GC/MS) analysis, respectively. Subsequently, SCFAs supplementation was performed to evaluate the protective effects against hepatic injuries. Additionally, Sigma-1 receptor knockout (S1R-/-) mice and fluvoxamine (Flu), an agonist of S1R, were introduced to investigate the mechanisms underlying the protective effects of SCFAs. Our results showed that Meth activated S1R and induced hepatic autophagy, inflammation, and oxidative stress by stimulating the MAPK/ERK pathway. Meanwhile, Meth disrupted SCFAs product-related microbiota, leading to a reduction in fecal SCFAs (especially Acetic acid and Propanoic acid). Accompanied by the optimization of gut microbiota, SCFAs supplementation normalized S1R expression and ameliorated Meth-induced hepatic injuries by repressing the MAPK/ERK pathway. Effectively, S1R knockout repressed Meth-induced activation of the MAPK/ERK pathway and further ameliorated hepatic injuries. Finally, the overexpression of S1R stimulated the MAPK/ERK pathway and yielded comparable adverse phenotypes to Meth administration. These findings suggest that Meth-induced hepatic injuries relied on the activation of S1R, which could be alleviated by SCFAs supplementation. Our study confirms the crucial role of S1R in Meth-induced hepatic injuries for the first time and provides a potential preemptive therapy.
•S1R activation mediated Meth-induced hepatic injuries through the MAPK/ERK pathway.•Meth disturbed gut microbiota and reduced fecal SCFAs in mice.•SCFAs mitigated Meth-induced hepatic injuries by repressing S1R activation.•S1R knockout mitigated Meth-induced hepatic injuries.•Overexpression of S1R resulted in similar hepatic injuries with Meth exposure.
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