Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Dissolved Air Flotation for Rapid Dewatering and Separation of Legacy Sludge Wastes

As a low footprint, high efficiency separation process, Dissolved Air Floatation (DAF) could effectively be retrofitted into existing waste management facilities at nuclear sites such Sellafield and Hanford to rapidly separate particulates from aqueous suspensions. The simplicity (no moving parts) and size of this technology coupled with low cost of construction and reagent purchase would also be ideal for ease of facility decommissioning with minimum impact to secondary waste generation. For this study, methyl isobutyl carbinol (MIBC) was used in this research as a frothing agent to produce a preferable stable foam. Mg(OH)2 was selected as a test material as it is the result of long term corrosion of Magnox fuel in UK nuclear fuel storage ponds. Due to the cationic nature of the Mg(OH)2 test material, the anionic surfactant, Sodium Dodecyl Sulphate (SDS) was used as collector to modify the surface properties to increase hydrophobicity. The performance of anionic SDS in floating 2.5%v/v Mg(OH)2 was compared to the adsorption isotherm of SDS on Mg(OH)2 to determine monolayer coverage surfactant dose which was investigated using Total Organic Carbon (TOC). SDS was found to increase particulate recovery to 93% with some water carry-over observed and it was found that particle bubble attachment was optimum for a select particle size distribution. This study proved that potential application of flotation as an efficient viable dewatering technique for common magnesium hydroxide based legacy wastes, using cheap, readily available collector and frother agents

Flotation using sodium dodecyl sulphate and sodium lauroyl isethionate for rapid dewatering of Mg(OH)2 radwaste suspensions

In this study, Mg(OH)2 suspensions were floated utilising sodium dodecyl sulphate (SDS) and sodium lauroyl isethionate (SLI) collectors, for rapid dewatering of radwaste suspensions. The following provides a brief description of the included data files: Figure 1, schematic of batch flotation cell used for dispersed air flotation tests. Figure 2 particle size distributions of sonicated Mg(OH)2 dispersions agitated at 900 rpm, change in the d50 particle size with time, and the change in the volume based particle size distribution with time. Figure 3, (A) Two region fitted Freundlich adsorption isotherm including both monolayer and bilayer adsorption profiles for (i) sodium dodecyl sulphate and (ii) sodium lauroyl isethionate collectors on Mg(OH)2. (B) Calculated equilibrium concentration. Figure 4, particle size distributions for Mg(OH)2 suspensions sonicated for 20 minutes, dosed with varied concentrations of SDS and SLI. Figure 5, change in foam height with superficial air velocity for (A) SDS, (B) SLI, and (C) MIBC, and the calculated retention time. Figure 6, the flotation performance with increasing collector concentration for 2.5 vol.% suspensions, as a measure of (A) mass percentage of Mg(OH)2 particles recovered, (B) mass percentage of water remaining in the cell, and (C) the residual Mg(OH)2 concentration. (D) The corresponding mass percentage of water recovered with increasing mass percentage of Mg(OH)2 particles recovered. Figure 7, the effect of collector concentration on the collection efficiency factor. Figure 8, schematic illustrating the mechanistic differences between SDS and SLI collector flotation systems. Also included are datafiles for the Electronic Supplementary Information (ESI) file, Figures S1-S3 respectively

Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells

Background Abnormal proliferation of vascular smooth muscle cells (VSMC) is a major cause of cardiovascular diseases (CVDs). Many studies suggest that vascular injury triggers VSMC dedifferentiation, which results in VSMC changes from a contractile to a synthetic phenotype; however, the underlying molecular mechanisms are still unclear. Methods In this study, we examined how VSMC responds under mechanical stress by using time-course microarray data. A three-phase study was proposed to investigate the stress-induced differentially expressed genes (DEGs) in VSMC. First, DEGs were identified by using the moderated t-statistics test. Second, more DEGs were inferred by using the Gaussian Graphical Model (GGM). Finally, the topological parameters-based method and cluster analysis approach were employed to predict the last batch of DEGs. To identify the potential drugs for vascular diseases involve VSMC proliferation, the drug-gene interaction database, Connectivity Map (cMap) was employed. Success of the predictions were determined using in-vitro data, i.e. MTT and clonogenic assay. Results Based on the differential expression calculation, at least 23 DEGs were found, and the findings were qualified by previous studies on VSMC. The results of gene set enrichment analysis indicated that the most often found enriched biological processes are cell-cycle-related processes. Furthermore, more stress-induced genes, well supported by literature, were found by applying graph theory to the gene association network (GAN). Finally, we showed that by processing the cMap input queries with a cluster algorithm, we achieved a substantial increase in the number of potential drugs with experimental IC50 measurements. With this novel approach, we have not only successfully identified the DEGs, but also improved the DEGs prediction by performing the topological and cluster analysis. Moreover, the findings are remarkably validated and in line with the literature. Furthermore, the cMap and DrugBank resources were used to identify potential drugs and targeted genes for vascular diseases involve VSMC proliferation. Our findings are supported by in-vitro experimental IC50, binding activity data and clinical trials. Conclusion This study provides a systematic strategy to discover potential drugs and target genes, by which we hope to shed light on the treatments of VSMC proliferation associated diseases

Drug repurposing and therapeutic anti-microRNA predictions in oxidized low-density lipoprotein-induced the proliferation of vascular smooth muscle cell associated diseases

[[abstract]]Drug repurposing is a new method for disease treatments, which accelerates the identification of new uses for existing drugs with minimal side effects for patients. MicroRNA-based therapeutics are a class of drugs that have been used in gene therapy following the FDA’s approval of the first anti-sense therapy. This study examines the effects of oxLDL on vascular smooth muscle cells (VSMCs) and identifies potential drugs and antimiRs for treating VSMC-associated diseases. The Connectivity Map (cMap) database is utilized to identify potential new uses of existing drugs. The success of the identifications was supported by MTT assay, clonogenic assay and clinical trial data. Specifically, 37 drugs, some of which are undergoing clinical trials, were identified. Three of the identified drugs exhibit IC50 activities. Among the 37 drugs’ targets, three differentially expressed genes (DEGs) are identified as drug targets by using both the DrugBank and the NCBI PubChem Compound databases. Also, one DEG, DNMT1, which is regulated by 17 miRNAs, where these miRNAs are potential targets for developing antimiR-based miRNA therapy, is found. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219720016500438?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press