GEMRec: A graph-based emotion-aware music recommendation approach

© Springer International Publishing AG 2016. Music recommendation has gained substantial attention in recent times. As one of the most important context features,user emotion has great potential to improve recommendations,but this has not yet been sufficiently explored due to the difficulty of emotion acquisition and incorporation. This paper proposes a graph-based emotion-aware music recommendation approach (GEMRec) by simultaneously taking a user’s music listening history and emotion into consideration. The proposed approach models the relations between user,music,and emotion as a three-element tuple (user,music,emotion),upon which an Emotion Aware Graph (EAG) is built,and then a relevance propagation algorithm based on random walk is devised to rank the relevance of music items for recommendation. Evaluation experiments are conducted based on a real dataset collected from a Chinese microblog service in comparison to baselines. The results show that the emotional context from a user’s microblogs contributes to improving the performance of music recommendation in terms of hitrate,precision,recall,and F1 score

dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction

Abstract: Nanomedicine development currently suffers from a lack of efficient tools to predict pharmacokinetic behavior without relying upon testing in large numbers of animals, impacting success rates and development costs. This work presents dendPoint, the first in silico model to predict the intravenous pharmacokinetics of dendrimers, a commonly explored drug vector, based on physicochemical properties. We have manually curated the largest relational database of dendrimer pharmacokinetic parameters and their structural/physicochemical properties. This was used to develop a machine learning-based model capable of accurately predicting pharmacokinetic parameters, including half-life, clearance, volume of distribution and dose recovered in the liver and urine. dendPoint successfully predicts dendrimer pharmacokinetic properties, achieving correlations of up to r = 0.83 and Q2 up to 0.68. dendPoint is freely available as a user-friendly web-service and database at http://biosig.unimelb.edu.au/dendpoint. This platform is ultimately expected to be used to guide dendrimer construct design and refinement prior to embarking on more time consuming and expensive in vivo testing

Characterization of endogenous players in Fibroblast Growth Factor‐regulated functions of hypothalamic tanycytes and energy‐balance nuclei

The mammalian hypothalamus regulates key homeostatic and neuroendocrine functions ranging from circadian rhythm and energy‐balance to growth and reproductive cycles via the hypothalamo‐pituitary and hypothalamo‐thyroid axes. In addition to its neurons, tanycytes are taking centre stage in the short and long term augmentation and integration of diverse hypothalamic functions, but the genetic regulators and mediators of their involvement are poorly understood. Exogenous interventions have implicated Fibroblast growth factor (FGF) signalling, but the focal point of FGF action and any role for putative endogenous players also remains elusive. We carried out a comprehensive high‐resolution screen of FGF signalling pathway mediators and modifiers using a combination of in situ hybridization, immunolabelling and transgenic reporter mice, to map their spatial distribution in the adult hypothalamus. Our findings suggest that beta tanycytes are the likely focal point of exogenous and endogenous FGF action in the third ventricular wall, utilising FGF‐receptors (FGFRs) ‐1 and ‐2 IIIc isoforms, but not FGFR3. Key IIIc‐activating endogenous ligands include FGFs 1, 2, 9 and 18, which are expressed by a subset of ependymal and parenchymal cells. In the parenchymal compartment, FGFRs 1‐3 show divergent patterns, with FGFR1 predominant in neuronal nuclei and FGFR3 expression being associated with glial cell function. Intracrine FGFs are also present, suggestive of multiple modes of FGF function. Our findings provide a testable framework for understanding the complex role of FGFs in regulating the metabolic endocrine and neurogenic functions of hypothalamus in vivo

Polymer-drug conjugates as inhalable drug delivery systems: A review

Accelerating interest by the pharmaceutical industry in the identification and development of less invasive routes of nanomedicine administration, coupled with defined efforts to improve the treatment of respiratory diseases through inhaled drug administration has fuelled growing interests in inhalable polymer-drug conjugates. Polymer-drug conjugates can alter the pharmacokinetic profile of the loaded drug after inhaled administration and enable the controlled and sustained exposure of the lungs to drugs when compared to the inhaled or oral administration of the drug alone. However, the major concern with the use of inhalable polymer-drug conjugates is their biocompatibility and long-term safety in the lungs, which is closely linked to lung retention times. A detailed understanding about the pharmacokinetics, lung disposition, clearance and safety of inhaled polymer-drug conjugates with significant translational potential is therefore required. This review therefore provides a comprehensive summary of the latest developments for several types of polymer-drug conjugates that are currently being explored as inhalable drug delivery systems. Finally, the current status and future perspective of the polymer-drug conjugates is also discussed with a focus on current knowledge gaps

Liquid Hydrogen Target Experience at SLAC

Liquid hydrogen targets have played a vital role in the physics program at SLAC for the past 40 years. These targets have ranged from small "beer can" targets to the 1.5 m long E158 target that was capable of absorbing up to 800 W without any significant density changes. Successful use of these targets has required the development of thin-wall designs, liquid hydrogen pumps, remote positioning and alignment systems, safety systems, control and data acquisition systems, cryogenic cooling circuits and heat exchangers. Detailed operating procedures have been created to ensure safety and operational reliability.This paper surveys the evolution of liquid hydrogen targets at SLAC and discusses advances in several of the enabling technologies that made these targets possible

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approaches from four method families: (i) factorization-based, (ii) random walk-based, (iii) deep learning-based, and (iv) the Large-scale Information Network Embedding (LINE) approach. We find that across the four families, random-walk-based approaches consistently achieve the best accuracy. Besides, they result in highly novel and diverse recommendations. Furthermore, our results show that the use of graph embeddings in trust-based collaborative filtering significantly improves user coverage.Comment: 10 pages, Accepted as a full paper on the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS'20

Tumour regression and improved gastrointestinal tolerability from controlled release of SN-38 from novel polyoxazoline-modified dendrimers

Irinotecan is used clinically for the treatment of colorectal cancer; however, its utility is limited by its narrow therapeutic index. We describe the use of a generation 5 l-lysine dendrimer that has been part-modified with a polyoxazoline as a drug delivery vehicle for improving the therapeutic index of SN-38, the active metabolite of irinotecan. By conjugating SN-38 to the dendrimer via different linker technologies we sought to vary the release rate of the drug to generate diverse pharmacokinetic profiles. Three conjugates with plasma release half-lives of 2.5 h, 21 h, and 72 h were tested for efficacy and toxicity using a mouse SW620 xenograft model. In this model, the linker with a plasma release half-life of 21 h achieved sustained SN-38 exposure in blood, above the target concentration. Control over the release rate of the drug from the linker, combined with prolonged circulation of the dendrimer, enabled administration of an efficacious dose of SN-38, achieving significant regression of the SW620 tumours. The conjugates with 2.5 and 72 h release half-lives did not achieve an anti-tumour effect. Intraperitoneal dosing of the clinically used prodrug irinotecan produces high initial and local concentrations of SN-38, which are associated with gastrointestinal toxicity. Administration of the 21 h release dendrimer conjugate did not produce a high initial Cmax of SN-38. Consequently, a marked reduction in gastrointestinal toxicity was observed relative to irinotecan treatment. Additional studies investigating the dose concentrations and dose scheduling showed that a weekly dosing schedule of 4 mg SN-38/kg was the most efficacious regimen. After 4 doses at weekly intervals, the survival period of the mice extended beyond 70 days following the final dose. These extensive studies have allowed us to identify a linker, dose and dosing regimen for SN-38 conjugated to polyoxazoline-modified dendrimer that maximised efficacy and minimised adverse side effects

AntRS: Recommending Lists through a Multi-Objective Ant Colony System

International audienceWhen people use recommender systems, they generally expect coherent lists of items. Depending on the application domain, it can be a playlist of songs they are likely to enjoy in their favorite online music service, a set of educational resources to acquire new competencies through an intelligent tutoring system, or a sequence of exhibits to discover from an adaptive mobile museum guide. To make these lists coherent from the users' perspective, recommendations must find the best compromise between multiple objectives (best possible precision, need for diversity and novelty). We propose to achieve that goal through a multi-agent recommender system, called AntRS. We evaluated our approach with a music dataset with about 500 users and more than 13,000 sessions. The experiments show that we obtain good results as regards to precision, novelty and coverage in comparison with typical state-of-the-art single and multi-objective algorithms

Doxorubicin conjugation and drug linker chemistry alter the intravenous and pulmonary pharmacokinetics of a PEGylated Generation 4 polylysine dendrimer in rats

PEGylated polylysine dendrimers have demonstrated potential as inhalable drug delivery systems that can improve the treatment of lung cancers. Their treatment potential may be enhanced by developing constructs that display prolonged lung retention, together with good systemic absorption, the capacity to passively target lung tumours from the blood and highly selective, yet rapid liberation in the tumour microenvironment. This study sought to characterise how the nature of cathepsin B cleavable peptide linkers, used to conjugate doxorubicin to a PEGylated (PEG570) G4 polylysine dendrimer, affect drug liberation kinetics and intravenous and pulmonary pharmacokinetics in rats. The construct bearing a self-emolative diglycolic acid-V-Citrulline linker exhibited faster doxorubicin release kinetics compared to constructs bearing self emolative diglycolic acid-GLFG, or non-self emolative glutaric acid-GLFG linkers. The V-Citrulline construct exhibited slower plasma clearance, but faster absorption from the lungs than a GLFG construct, although mucociliary clearance and urinary elimination were unchanged. Doxorubicin-conjugation enhanced localisation in the bronchoalveolar lavage fluid compared to lung tissue, suggesting that projection of doxorubicin from the dendrimer surface reduced tissue uptake. These data show that the linker chemistry employed to conjugate drugs to PEGylated carriers can affect drug release profiles and systemic and lung disposition

Alleviating the new user problem in collaborative filtering by exploiting personality information

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for their attention regarding the dataset