Influenza forecasting with Google Flu Trends

BACKGROUND

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy.

METHODS

Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information.

RESULTS

A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets.

CONCLUSIONS

Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Memetic algorithms for continuous optimisation based on local search chains

Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the way they exploit local information to guide the search process. In this paper, they are called intensive continuous local search methods. Given the potential of this type of local optimisation methods, it is interesting to build prospective memetic algorithm models with them. This paper presents the concept of local search chain as a springboard to design memetic algorithm approaches that can effectively use intense continuous local search methods as local search operators. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration (initial solution, strategy parameter values, internal variables, etc.) reached by this one. The proposed memetic algorithm favours the formation of local search chains during the memetic algorithm run with the aim of concentrating local tuning in search regions showing promise. In order to study the performance of the new memetic algorithm model, an instance is implemented with CMA-ES as an intense local search method. The benefits of the proposal in comparison to other kinds of memetic algorithms and evolutionary algorithms proposed in the literature to deal with continuous optimisation problems are experimentally shown. Concretely, the empirical study reveals a clear superiority when tackling high-dimensional problems.

Search Engine Ranking, Quality, and Content of Web Pages That Are Critical Versus Noncritical of Human Papillomavirus Vaccine

PURPOSE

Online information can influence attitudes toward vaccination. The aim of the present study was to provide a systematic evaluation of the search engine ranking, quality, and content of Web pages that are critical versus noncritical of human papillomavirus (HPV) vaccination.

METHODS

We identified HPV vaccine-related Web pages with the Google search engine by entering 20 terms. We then assessed each Web page for critical versus noncritical bias and for the following quality indicators: authorship disclosure, source disclosure, attribution of at least one reference, currency, exclusion of testimonial accounts, and readability level less than ninth grade. We also determined Web page comprehensiveness in terms of mention of 14 HPV vaccine-relevant topics.

RESULTS

Twenty searches yielded 116 unique Web pages. HPV vaccine-critical Web pages comprised roughly a third of the top, top 5- and top 10-ranking Web pages. The prevalence of HPV vaccine-critical Web pages was higher for queries that included term modifiers in addition to root terms. Compared with noncritical Web pages, Web pages critical of HPV vaccine overall had a lower quality score than those with a noncritical bias (p < .01) and covered fewer important HPV-related topics (p < .001). Critical Web pages required viewers to have higher reading skills, were less likely to include an author byline, and were more likely to include testimonial accounts. They also were more likely to raise unsubstantiated concerns about vaccination.

CONCLUSIONS

Web pages critical of HPV vaccine may be frequently returned and highly ranked by search engine queries despite being of lower quality and less comprehensive than noncritical Web pages.

Looking at plastic surgery through Google Glass: part 1. Systematic review of Google Glass evidence and the first plastic surgical procedures

BACKGROUND

Google Glass has the potential to become a ubiquitous and translational technological tool within clinical plastic surgery. Google Glass allows clinicians to remotely view patient notes, laboratory results, and imaging; training can be augmented via streamed expert master classes; and patient safety can be improved by remote advice from a senior colleague. This systematic review identified and appraised every Google Glass publication relevant to plastic surgery and describes the first plastic surgical procedures recorded using Google Glass.

METHODS

A systematic review was performed using PubMed National Center for Biotechnology Information, Ovid MEDLINE, and the Cochrane Central Register of Controlled Trials, following modified Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Key search terms "Google" and "Glass" identified mutually inclusive publications that were screened for inclusion.

RESULTS

Eighty-two publications were identified, with 21 included for review. Google Glass publications were formal articles (n = 3), editorial/commentary articles (n = 7), conference proceedings (n = 1), news reports (n = 3), and online articles (n = 7). Data support Google Glass' positive impact on health care delivery, clinical training, medical documentation, and patient safety. Concerns exist regarding patient confidentiality, technical issues, and limited software. The first plastic surgical procedure performed using Google Glass was a blepharoplasty on October 29, 2013.

CONCLUSIONS

Google Glass is an exciting translational technology with the potential to positively impact health care delivery, medical documentation, surgical training, and patient safety. Further high-quality scientific research is required to formally appraise Google Glass in the clinical setting.

Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing

Open modification searching (OMS) is a powerful search strategy that identifies peptides carrying any type of modification by allowing a modified spectrum to match against its unmodified variant by using a very wide precursor mass window. A drawback of this strategy, however, is that it leads to a large increase in search time. Although performing an open search can be done using existing spectral library search engines by simply setting a wide precursor mass window, none of these tools have been optimized for OMS, leading to excessive runtimes and suboptimal identification results. We present the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. This approach is combined with a cascade search strategy to maximize the number of identified unmodified and modified spectra while strictly controlling the false discovery rate as well as a shifted dot product score to sensitively match modified spectra to their unmodified counterparts. ANN-SoLo achieves state-of-the-art performance in terms of speed and the number of identifications. On a previously published human cell line data set, ANN-SoLo confidently identifies more spectra than SpectraST or MSFragger and achieves a speedup of an order of magnitude compared with SpectraST. ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo .

Picbreeder: a case study in collaborative evolutionary exploration of design space

For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others' images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems.

Bubble effect: including internet search engines in systematic reviews introduces selection bias and impedes scientific reproducibility

BACKGROUND

Using internet search engines (such as Google search) in systematic literature reviews is increasingly becoming a ubiquitous part of search methodology. In order to integrate the vast quantity of available knowledge, literature mostly focuses on systematic reviews, considered to be principal sources of scientific evidence at all practical levels. Any possible individual methodological flaws present in these systematic reviews have the potential to become systemic.

MAIN TEXT

This particular bias, that could be referred to as (re)search bubble effect, is introduced because of inherent, personalized nature of internet search engines that tailors results according to derived user preferences based on unreproducible criteria. In other words, internet search engines adjust their user's beliefs and attitudes, leading to the creation of a personalized (re)search bubble, including entries that have not been subjected to rigorous peer review process. The internet search engine algorithms are in a state of constant flux, producing differing results at any given moment, even if the query remains identical. There are many more subtle ways of introducing unwanted variations and synonyms of search queries that are used autonomously, detached from user insight and intent. Even the most well-known and respected systematic literature reviews do not seem immune to the negative implications of the search bubble effect, affecting reproducibility.

CONCLUSION

Although immensely useful and justified by the need for encompassing the entirety of knowledge, the practice of including internet search engines in systematic literature reviews is fundamentally irreconcilable with recent emphasis on scientific reproducibility and rigor, having a profound impact on the discussion of the limits of scientific epistemology. Scientific research that is not reproducible, may still be called science, but represents one that should be avoided. Our recommendation is to use internet search engines as an additional literature source, primarily in order to validate initial search strategies centered on bibliographic databases.

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse

Objective

The repurposing of electronic health records (EHRs) can improve clinical and genetic research for rare diseases. However, significant information in rare disease EHRs is embedded in the narrative reports, which contain many negated clinical signs and family medical history. This paper presents a method to detect family history and negation in narrative reports and evaluates its impact on selecting populations from a clinical data warehouse (CDW).

Materials and Methods

We developed a pipeline to process 1.6 million reports from multiple sources. This pipeline is part of the load process of the Necker Hospital CDW.

Results

We identified patients with "Lupus and diarrhea," "Crohn's and diabetes," and "NPHP1" from the CDW. The overall precision, recall, specificity, and F-measure were 0.85, 0.98, 0.93, and 0.91, respectively.

Conclusion

The proposed method generates a highly accurate identification of cases from a CDW of rare disease EHRs.

Adaptive niche radii and niche shapes approaches for niching with the CMA-ES

While the motivation and usefulness of niching methods is beyond doubt, the relaxation of assumptions and limitations concerning the hypothetical search landscape is much needed if niching is to be valid in a broader range of applications. Upon the introduction of radii-based niching methods with derandomized evolution strategies (ES), the purpose of this study is to address the so-called niche radius problem. A new concept of an adaptive individual niche radius is applied to niching with the covariance matrix adaptation evolution strategy (CMA-ES). Two approaches are considered. The first approach couples the radius to the step size mechanism, while the second approach employs the Mahalanobis distance metric with the covariance matrix mechanism for the distance calculation, for obtaining niches with more complex geometrical shapes. The proposed approaches are described in detail, and then tested on high-dimensional artificial landscapes at several levels of difficulty. They are shown to be robust and to achieve satisfying results.

Google Trends can improve surveillance of Type 2 diabetes

Recent studies demonstrate that people are increasingly looking online to assess their health, with reasons varying from personal preferences and beliefs to inability to book a timely appointment with their local medical practice. Records of these activities represent a new source of data about the health of populations, but which is currently unaccounted for by disease surveillance models. This could potentially be useful as evidence of individuals' perception of bodily changes and self-diagnosis of early symptoms of an emerging disease. We make use of the Experian geodemographic Mosaic dataset in order to extract Type 2 diabetes candidate risk variables and compare their temporal relationships with the search keywords, used to describe early symptoms of the disease on Google. Our results demonstrate that Google Trends can detect early signs of diabetes by monitoring combinations of keywords, associated with searches for hypertension treatment and poor living conditions; Combined search semantics, related to obesity, how to quit smoking and improve living conditions (deprivation) can be also employed, however, may lead to less accurate results.

Comparison of New Era's Education Platforms, YouTube® and WebSurg®, in Sleeve Gastrectomy

INTRODUCTION

The Internet is a widely used resource for obtaining medical information. However, the quality of information on online platforms is still debated. Our goal in this quality-controlled WebSurg® and YouTube®-based study was to compare these two online video platforms in terms of the accuracy and quality of information about sleeve gastrectomy videos.

METHODS

Most viewed (popular) videos returned by YouTube® search engine in response to the keyword "sleeve gastrectomy" were included in the study. The educational accuracy and quality of the videos were evaluated according to known scoring systems. A novel scoring system measured technical quality. The ten most viewed (popular) videos in WebSurg® in response to the keyword "sleeve gastrectomy" were compared with ten YouTube® videos with the highest educational/technical scores.

RESULTS

Scoring systems measuring the educational accuracy and quality of WebSurg® videos were significantly higher than ten YouTube® videos which have the most top technical scores (p < 0.05), and no significant difference was found in the assessment of ten YouTube® videos that have the highest technical ratings compared with WebSurg® videos (p 0.481).

CONCLUSIONS

WebSurg® videos, which were passed through a reviewing process and were mostly prepared by academicians, remained below the expected quality. The main limitation of WebSurg® and YouTube® is the lack of information on preoperative and postoperative processes.

The performance of adverse effects search filters in MEDLINE and EMBASE

BACKGROUND

 Search filters can potentially improve the efficiency of searches involving electronic databases such as medline and embase. Although search filters have been developed for identifying records that contain adverse effects data, little is known about the sensitivity of such filters.

OBJECTIVES

 This study measured the sensitivity of using available adverse effects filters to retrieve papers with adverse effects data.

METHODS

  A total of 233 included studies from 26 systematic reviews of adverse effects were used for analysis. Search filters from medline and embase were tested for their sensitivity in retrieving the records included in these reviews. In addition, the sensitivity of each individual search term used in at least one search filter was measured.

RESULTS

 Subheadings proved the most useful search terms in both medline and embase. No indexing terms in medline achieved over 12% sensitivity. The sensitivity of published search filters varied in medline from 3% to 93% and in embase from 57% to 97%. Whether this level of sensitivity is acceptable will be dependent on the purpose of the search.

CONCLUSIONS

 Although no adverse effects search filter captured all the relevant records, high sensitivity could be achieved. Search filters may therefore be useful in retrieving adverse effects data.

The added value of online user-generated content in traditional methods for influenza surveillance

There has been considerable work in evaluating the efficacy of using online data for health surveillance. Often comparisons with baseline data involve various squared error and correlation metrics. While useful, these overlook a variety of other factors important to public health bodies considering the adoption of such methods. In this paper, a proposed surveillance system that incorporates models based on recent research efforts is evaluated in terms of its added value for influenza surveillance at Public Health England. The system comprises of two supervised learning approaches trained on influenza-like illness (ILI) rates provided by the Royal College of General Practitioners (RCGP) and produces ILI estimates using Twitter posts or Google search queries. RCGP ILI rates for different age groups and laboratory confirmed cases by influenza type are used to evaluate the models with a particular focus on predicting the onset, overall intensity, peak activity and duration of the 2015/16 influenza season. We show that the Twitter-based models perform poorly and hypothesise that this is mostly due to the sparsity of the data available and a limited training period. Conversely, the Google-based model provides accurate estimates with timeliness of approximately one week and has the potential to complement current surveillance systems.

Automating the search for a patent's prior art with a full text similarity search

More than ever, technical inventions are the symbol of our society’s advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality.

Identifying co-targets to fight drug resistance based on a random walk model

BACKGROUND

Drug resistance has now posed more severe and emergent threats to human health and infectious disease treatment. However, wet-lab approaches alone to counter drug resistance have so far still achieved limited success due to less knowledge about the underlying mechanisms of drug resistance. Our approach apply a heuristic search algorithm in order to extract active network under drug treatment and use a random walk model to identify potential co-targets for effective antibacterial drugs.

RESULTS

We use interactome network of Mycobacterium tuberculosis and gene expression data which are treated with two kinds of antibiotic, Isoniazid and Ethionamide as our test data. Our analysis shows that the active drug-treated networks are associated with the trigger of fatty acid metabolism and synthesis and nicotinamide adenine dinucleotide (NADH)-related processes and those results are consistent with the recent experimental findings. Efflux pumps processes appear to be the major mechanisms of resistance but SOS response is significantly up-regulation under Isoniazid treatment. We also successfully identify the potential co-targets with literature confirmed evidences which are related to the glycine-rich membrane, adenosine triphosphate energy and cell wall processes.

CONCLUSIONS

With gene expression and interactome data supported, our study points out possible pathways leading to the emergence of drug resistance under drug treatment. We develop a computational workflow for giving new insights to bacterial drug resistance which can be gained by a systematic and global analysis of the bacterial regulation network. Our study also discovers the potential co-targets with good properties in biological and graph theory aspects to overcome the problem of drug resistance.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

Evaluating the Dental Caries-Related Information on Brazilian Websites: Qualitative Study

BACKGROUND

Dental caries is the most common chronic oral disease, affecting 2.4 billion people worldwide who on average have 2.11 decayed, missing, or filled teeth. It impacts the quality of life of patients, socially and economically. However, the comprehension of dental caries may be difficult for most people, as it involves a multifactorial etiology with the interplay between the tooth surface, the dental biofilm, dietary fermentable carbohydrates, and genetic and behavioral factors. Therefore, the production of effective materials addressed to the education and counseling of patients for the prevention of dental caries requires a high level of specialization. In this regard, the dental caries-related contents produced by laypersons and their availability on the Internet may be low-quality information.

OBJECTIVE

The aim of this study was to assess the readability and the quality of dental caries-related information on Brazilian websites.

METHODS

A total of 75 websites were selected through Google, Bing, Yahoo!, and Baidu. The websites were organized in rankings according to their order of appearance in each one of the 4 search engines. Furthermore, 2 independent examiners evaluated the quality of websites using the DISCERN questionnaire and the Journal of American Medical Association (JAMA) benchmark criteria. The readability of the websites was assessed by the Flesch Reading Ease adapted to Brazilian Portuguese (FRE-BP). In addition, the information presented on the websites was categorized as etiology, prevention, and treatment of dental caries. The statistical analysis was performed using Spearman rank correlation coefficient, Mann-Whitney U test, hierarchical clustering analysis by Ward minimum variance method, Kruskal-Wallis test, and post hoc Dunn test. P<.05 was considered significant.

RESULTS

The Web contents were considered to be of poor quality by DISCERN (mean 33.48, standard deviation, SD 9.06) and JAMA (mean 1.12, SD 0.97) scores, presenting easy reading levels (FRE-BP: mean 62.93, SD 10.15). The rankings of the websites presented by Google (ρ=-.22, P=.08), Baidu (ρ=-.19, P=.53), Yahoo! (ρ=.22, P=.39), and Bing (ρ=-.36, P=.23) were not correlated with DISCERN scores. Moreover, the quality of websites with health- and nonhealth-related authors was similar (P=.27 for DISCERN and P=.47 for JAMA); however, the pages with a greater variety of dental caries information showed significantly higher quality scores than those with limited contents (P=.009).

CONCLUSIONS

On the basis of this sample, dental caries-related contents available on Brazilian websites were considered simple, accessible, and of poor quality, independent of their authorship. These findings indicate the need for the development of specific policies focused on the stimulus for the production and publication of Web health information, encouraging dentists to guide their patients in searching for recommended oral health websites.

Extracting tag hierarchies

Tagging items with descriptive annotations or keywords is a very natural way to compress and highlight information about the properties of the given entity. Over the years several methods have been proposed for extracting a hierarchy between the tags for systems with a "flat", egalitarian organization of the tags, which is very common when the tags correspond to free words given by numerous independent people. Here we present a complete framework for automated tag hierarchy extraction based on tag occurrence statistics. Along with proposing new algorithms, we are also introducing different quality measures enabling the detailed comparison of competing approaches from different aspects. Furthermore, we set up a synthetic, computer generated benchmark providing a versatile tool for testing, with a couple of tunable parameters capable of generating a wide range of test beds. Beside the computer generated input we also use real data in our studies, including a biological example with a pre-defined hierarchy between the tags. The encouraging similarity between the pre-defined and reconstructed hierarchy, as well as the seemingly meaningful hierarchies obtained for other real systems indicate that tag hierarchy extraction is a very promising direction for further research with a great potential for practical applications. Tags have become very prevalent nowadays in various online platforms ranging from blogs through scientific publications to protein databases. Furthermore, tagging systems dedicated for voluntary tagging of photos, films, books, etc. with free words are also becoming popular. The emerging large collections of tags associated with different objects are often referred to as folksonomies, highlighting their collaborative origin and the “flat” organization of the tags opposed to traditional hierarchical categorization. Adding a tag hierarchy corresponding to a given folksonomy can very effectively help narrowing or broadening the scope of search. Moreover, recommendation systems could also benefit from a tag hierarchy.

Topic search filters: a systematic scoping review

BACKGROUND

Searching for topics within large biomedical databases can be challenging, especially when topics are complex, diffuse, emerging or lack definitional clarity. Experimentally derived topic search filters offer a reliable solution to effective retrieval; however, their number and range of subject foci remain unknown.

OBJECTIVES

This systematic scoping review aims to identify and describe available experimentally developed topic search filters.

METHODS

Reports on topic search filter development (1990-) were sought using grey literature sources and 15 databases. Reports describing the conception and prospective development of a database-specific topic search and including an objectively measured estimate of its performance ('sensitivity') were included.

RESULTS

Fifty-four reports met inclusion criteria. Data were extracted and thematically synthesised to describe the characteristics of 58 topic search filters.

DISCUSSION

Topic search filters are proliferating and cover a wide range of subjects. Filter reports, however, often lack clear definitions of concepts and topic scope to guide users. Without standardised terminology, filters are challenging to find. Information specialists may benefit from a centralised topic filter repository and appraisal checklists to facilitate quality assessment.

CONCLUSION

Findings will help information specialists identify existing topic search filters and assist filter developers to build on current knowledge in the field.

A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.