b-move: faster bidirectional character extensions in a run-length compressed index.

Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.'s r-index and Nishimoto and Tabei's move structure, alleviate memory constraints but focus primarily on backward search for MEM-finding. Arakawa et al.'s br-index initiates complete approximate pattern matching using bidirectional search in run-length compressed space, but with significant computational overhead due to complex memory access patterns. We introduce b-move, a novel bidirectional extension of the move structure, enabling fast, cache-efficient bidirectional character extensions in run-length compressed space. It achieves bidirectional character extensions up to 8 times faster than the br-index, closing the performance gap with FM-index-based alternatives, while maintaining the br-index's favorable memory characteristics. For example, all available complete E. coli genomes on NCBI's RefSeq collection can be compiled into a b-move index that fits into the RAM of a typical laptop. Thus, b-move proves practical and scalable for pan-genome indexing and querying. We provide a C++ implementation of b-move, supporting efficient lossless approximate pattern matching including locate functionality, available at https://github.com/biointec/b-move under the AGPL-3.0 license.

Accuracy Assessment of Geometric-Distortion Identification Methods for Sentinel-1 Synthetic Aperture Radar Imagery in Highland Mountainous Regions.

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods-LSM, R-Index, and P-NG-against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.

Centrifuger: lossless compression of microbial genomes for efficient and accurate metagenomic sequence classification.

Centrifuger is an efficient taxonomic classification method that compares sequencing reads against a microbial genome database. In Centrifuger, the Burrows-Wheeler transformed genome sequences are losslessly compressed using a novel scheme called run-block compression. Run-block compression achieves sublinear space complexity and is effective at compressing diverse microbial databases like RefSeq while supporting fast rank queries. Combining this compression method with other strategies for compacting the Ferragina-Manzini (FM) index, Centrifuger reduces the memory footprint by half compared to other FM-index-based approaches. Furthermore, the lossless compression and the unconstrained match length help Centrifuger achieve greater accuracy than competing methods at lower taxonomic levels.

Finding maximal exact matches in graphs.

BACKGROUND: We study the problem of finding maximal exact matches (MEMs) between a query string Q and a labeled graph G. MEMs are an important class of seeds, often used in seed-chain-extend type of practical alignment methods because of their strong connections to classical metrics. A principled way to speed up chaining is to limit the number of MEMs by considering only MEMs of length at least κ ( κ -MEMs). However, on arbitrary input graphs, the problem of finding MEMs cannot be solved in truly sub-quadratic time under SETH (Equi et al., TALG 2023) even on acyclic graphs. RESULTS: In this paper we show an O ( n · L · d L - 1 + m + M κ , L ) -time algorithm finding all κ -MEMs between Q and G spanning exactly L nodes in G, where n is the total length of node labels, d is the maximum degree of a node in G, m = | Q | , and M κ , L is the number of output MEMs. We use this algorithm to develop a κ -MEM finding solution on indexable Elastic Founder Graphs (Equi et al., Algorithmica 2022) running in time O ( n H 2 + m + M κ ) , where H is the maximum number of nodes in a block, and M κ is the total number of κ -MEMs. Our results generalize to the analysis of multiple query strings (MEMs between G and any of the strings). Additionally, we provide some experimental results showing that the number of graph MEMs is an order of magnitude smaller than the number of string MEMs of the corresponding concatenated collection. CONCLUSIONS: We show that seed-chain-extend type of alignment methods can be implemented on top of indexable Elastic Founder Graphs by providing an efficient way to produce the seeds between a set of queries and the graph. The code is available in https://github.com/algbio/efg-mems .

Centrifuger: lossless compression of microbial genomes for efficient and accurate metagenomic sequence classification.

Centrifuger is an efficient taxonomic classification method that compares sequencing reads against a microbial genome database. In Centrifuger, the Burrows-Wheeler transformed genome sequences are losslessly compressed using a novel scheme called run-block compression. Run-block compression achieves sublinear space complexity and is effective at compressing diverse microbial databases like RefSeq while supporting fast rank queries. Combining this compression method with other strategies for compacting the Ferragina-Manzini (FM) index, Centrifuger reduces the memory footprint by half compared to other FM-index-based approaches. Furthermore, the lossless compression and the unconstrained match length help Centrifuger achieve greater accuracy than competing methods at lower taxonomic levels.

Predicting the severity of COVID-19 patients using the CD24-CSF1R index in whole blood samples.

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has become one of the most serious public health crises worldwide. Most infected people are asymptomatic but are still able to spread the virus. People with mild or moderate illnesses are likely to recover without hospitalization, while critically ill patients face a higher risk of organ injury or even death. In this study, we aimed to identify a novel biomarker that can predict the severity of COVID-19 patients. Clinical information and RNA-seq data of leukocytes from whole blood samples with and without a COVID-19 diagnosis (n = 100 and 26, respectively) were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus database. Raw data were processed using the Transcripts Per Million (TPM) method and then transformed using log2 (TPM+1) for normalization. The CD24-CSF1R index was established. Violin plots, Kaplan-Meier curves, ROC curves, and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of the established index. The CD24-CSF1R index was significantly associated with ICU admission (n = 50 ICU, 50 non-ICU) and ventilatory status (n = 42 ventilation, 58 non-ventilation) with p = 4.186e-11 and p = 1.278e-07, respectively. The ROC curve produced a relatively accurate prediction of ICU admission with an AUC of 0.8524. Additionally, patients with a high index had significantly fewer mechanical ventilation-free days than patients with a low index (p = 6.07e-07). Furthermore, the established index showed a strong prognostic ability for the risk of using a ventilator in the multivariate Cox regression model (p < 0.001). The CD24-CSF1R index was significantly associated with COVID-19 severity. The established index could have potential implications for prognosis, disease severity stratification, and clinical management.

MONI: A Pangenomic Index for Finding Maximal Exact Matches.

Recently, Gagie et al. proposed a version of the FM-index, called the r-index, that can store thousands of human genomes on a commodity computer. Then Kuhnle et al. showed how to build the r-index efficiently via a technique called prefix-free parsing (PFP) and demonstrated its effectiveness for exact pattern matching. Exact pattern matching can be leveraged to support approximate pattern matching, but the r-index itself cannot support efficiently popular and important queries such as finding maximal exact matches (MEMs). To address this shortcoming, Bannai et al. introduced the concept of thresholds, and showed that storing them together with the r-index enables efficient MEM finding-but they did not say how to find those thresholds. We present a novel algorithm that applies PFP to build the r-index and find the thresholds simultaneously and in linear time and space with respect to the size of the prefix-free parse. Our implementation called MONI can rapidly find MEMs between reads and large-sequence collections of highly repetitive sequences. Compared with other read aligners-PuffAligner, Bowtie2, BWA-MEM, and CHIC- MONI used 2-11 times less memory and was 2-32 times faster for index construction. Moreover, MONI was less than one thousandth the size of competing indexes for large collections of human chromosomes. Thus, MONI represents a major advance in our ability to perform MEM finding against very large collections of related references.

Finding Maximal Exact Matches Using the r-Index.

Efficiently finding maximal exact matches (MEMs) between a sequence read and a database of genomes is a key first step in read alignment. But until recently, it was unknown how to build a data structure in [Formula: see text] space that supports efficient MEM finding, where r is the number of runs in the Burrows-Wheeler Transform. In 2021, Rossi et al. showed how to build a small auxiliary data structure called thresholds in addition to the r-index in [Formula: see text] space. This addition enables efficient MEM finding using the r-index. In this article, we present the tool that implements this solution, which we call MONI. Namely, we give a high-level view of the main components of the data structure and show how the source code can be downloaded, compiled, and used to find MEMs between a set of sequence reads and a set of genomes.

The 9-point hedonic scale: Using R-Index Preference Measurement to compute effect size and eliminate artifactual ties.

214 consumers used the verbal 9-point hedonic scale to assess 4 types of flavor coated peanuts and 4 types of flavored teas. They used the traditional ANOVA/LSD analysis to provide mean values derived from the 9-point hedonic scale along with measures of significant difference. However, these data did not provide effect sizes. They did not give direct measures of the strength of preference between the various products, which was the main interest. Accordingly, effect sizes were computed. For this, each consumer had also ranked their preferences as they made their ratings on the 9-point hedonic scale. From these, R-Index values were computed to provide the percentages of consumers, who preferred each product to every other product. These direct measures of effect size completed the analysis begun by the ANOVA analysis of the set of mean scores. Also, the measures were nonparametric and avoided issues of the validity of a parametric statistical analysis. They also avoided the problem with the traditional analysis when products in the same scale category are attributed the same scores, when they are not equally liked. Experiment 2, using 207 consumers indicated that this problem was only serious enough to reduce the power of the traditional analysis, compared with the R-Index Preference Measurement, when the number of products being tested approached a dozen say, for product optimization.

Efficient Construction of a Complete Index for Pan-Genomics Read Alignment.

Short-read aligners predominantly use the FM-index, which is easily able to index one or a few human genomes. However, it does not scale well to indexing collections of thousands of genomes. Driving this issue are the two chief components of the index: (1) a rank data structure over the Burrows-Wheeler Transform (BWT) of the string that will allow us to find the interval in the string's suffix array (SA), and (2) a sample of the SA that-when used with the rank data structure-allows us to access the SA. The rank data structure can be kept small even for large genomic databases, by run-length compressing the BWT, but until recently there was no means known to keep the SA sample small without greatly slowing down access to the SA. Now that (SODA 2018) has defined an SA sample that takes about the same space as the run-length compressed BWT, we have the design for efficient FM-indexes of genomic databases but are faced with the problem of building them. In 2018, we showed how to build the BWT of large genomic databases efficiently (WABI 2018), but the problem of building the sample efficiently was left open. We compare our approach to state-of-the-art methods for constructing the SA sample, and demonstrate that it is the fastest and most space-efficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over the FM-index-based Bowtie method with respect to both memory and time and over the hybrid index-based CHIC method with respect to query time and memory required for indexing.

Matching Reads to Many Genomes with the r-Index.

The r-index is a tool for compressed indexing of genomic databases for exact pattern matching, which can be used to completely align reads that perfectly match some part of a genome in the database or to find seeds for reads that do not. This article shows how to download and install the programs ri-buildfasta and ri-align; how to call ri-buildfasta on an FASTA file to build an r-index for that file; and how to query that index with ri-align.

Diagnostic accuracy of the CRS-R index in patients with disorders of consciousness.

Objective: To obtain a CRS-R index suitable for diagnosis of patients with disorders of consciousness (DOC) and compare it to other CRS-R based scores to evaluate its potential for clinics and research. Design: We evaluated the diagnostic accuracy of several CRS-R-based scores in 124 patients with DOC. ROC analysis of the CRS-R total score, the Rasch-based CRS-R score, CRS-R-MS and the CRS-R index evaluated the diagnostic accuracy for patients with the Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS). Correlations were computed between the CRS-R-MS, CRS-R index, the Rasch-based score and the CRS-R total score. Results: Both the CRS-R-MS and CRS-R index ranged from 0 to 100, with a cut-off of 8.315 that perfectly distinguishes between patients with UWS and MCS. The CRS-R total score and Rasch-based score did not provide a cut-off score for patients with UWS and MCS. The proposed CRS-R index correlated with the CRS-R total score, Rasch-based score and the CRS-R-MS. Conclusion: The CRS-R index is reliable to diagnose patients with UWS and MCS and can be used in compliance with the CRS-R scoring guidelines. The obtained index offers the opportunity to improve the interpretation of clinical assessment and can be used in (longitudinal) research protocols. Abbreviations: CRS-R: Coma Recovery Scale-Revised; CRS-R-MS: Coma Recovery Scale-Revised Modified Score; DOC: Disorders of Consciousness; MCS: Minimally Conscious State; UWS: Unresponsive Wakefulness Syndrome; ROC: Receiver Operating Characteristic; AUC: Area Under the Curve; IRT: Item Response Theory.

Postgraduate publishing output in pediatric neurosurgery: correlation with fellowship site and individual scholars.

OBJECTIVE: The objective of this study was to analyze the publication output of postgraduate pediatric neurosurgery fellows for a 10-year period as well as identify 25 individual highly productive pediatric neurosurgeons. The correlation between academic productivity and the site of fellowship training was studied. METHODS: Programs certified by the Accreditation Council for Pediatric Neurosurgery Fellowships that had 5 or more graduating fellows from 2006 to 2015 were included for analysis. Fellows were queried using Scopus for publications during those 10 years with citation data through 2017. Pearson correlation coefficients were calculated, comparing program rankings of faculty against fellows using the revised Hirsch index (r-index; primary) and Hirsch index (h-index; secondary). A list of 25 highly accomplished individual academicians and their fellowship training locations was compiled. RESULTS: Sixteen programs qualified with 152 fellows from 2006 to 2015; 136 of these surgeons published a total of 2009 articles with 23,735 citations. Most publications were pediatric-specific (66.7%) clinical articles (93.1%), with middle authorship (55%). Co-investigators were more likely from residency than fellowship. There was a clustering of the top 7 programs each having total publications of around 120 or greater, publications per fellow greater than 12, more than 1200 citations, and adjusted ir10 (revised 10-year institutional h-index) and ih10 (10-year institutional h-index) values of approximately 2 or higher. Correlating faculty and fellowship program rankings yielded correlation coefficients ranging from 0.53 to 0.80. Fifteen individuals (60%) in the top 25 (by r5 index) list completed their fellowship at 1 of these 7 institutions. CONCLUSIONS: Approximately 90% of fellowship-trained pediatric neurosurgeons have 1 or more publications, but the spectrum of output is broad. There is a strong correlation between where surgeons complete their fellowships and postgraduate publications.

Consumer profile and acceptability of cooked beef steaks with edible and active coating containing oregano and rosemary essential oils.

Fresh animal products are highly perishable and characterized by a short shelf-life. Edible coatings with natural antioxidants (essential oils: EOs) could improve stability, ensure quality, and increase the shelf-life of fresh products. Due to the strong flavor of EOs, their use should consider consumer preferences and sensory acceptability. This study evaluated the effects of edible coating (with oregano and rosemary essential oil) on beef in relation to consumer preferences, besides the determination of habits of consumption and buying intentions of consumers. Acceptability scores from three clusters of consumers was described. Coating with oregano was the preferred. The higher consumer acceptance and willingness to buy this product indicate a great potential and possibility of using coatings with essential oils in fresh animal products.

Taking the next step in publication productivity analysis in pediatric neurosurgery.

OBJECTIVE There has been an increasing interest in the quantitative analysis of publishing within the field of neurosurgery at the individual, group, and institutional levels. The authors present an updated analysis of accredited pediatric neurosurgery training programs. METHODS All 28 Accreditation Council for Pediatric Neurosurgery Fellowship programs were contacted for the names of pediatric neurosurgeons who were present each year from 2011 through 2015. Faculty names were queried in Scopus for publications and citations during this time period. The 5-year institutional Hirsch index [i h(5)-index] and revised 5-year institutional h-index [i r(5)-index] were calculated to rank programs. Each publication was reviewed to determine authorship value, tier of research, clinical versus basic science research, subject matter, and whether it was pediatrics-specific. A unique 3-tier article classification system was introduced to stratify clinical articles by quality and complexity, with tier 3 being the lowest tier of publication (e.g., case reports) and tier 1 being the highest (e.g., randomized controlled trials). RESULTS Among 2060 unique publications, 1378 (67%) were pediatrics-specific. The pediatrics-specific articles had a mean of 15.2 citations per publication (median 6), whereas the non-pediatrics-specific articles had a mean of 23.0 citations per publication (median 8; p < 0.0001). For the 46% of papers that had a pediatric neurosurgeon as first or last author, the mean number of citations per publication was 12.1 (median 5.0) compared with 22.5 (median 8.0) for those in which a pediatric neurosurgeon was a middle author (p < 0.0001). Seventy-nine percent of articles were clinical research and 21% were basic science or translational research; however, basic science and translational articles had a mean of 36.9 citations per publication (median 15) compared with 12.6 for clinical publications (median 5.0; p < 0.0001). Among clinical articles, tier 1 papers had a mean of 15.0 citations per publication (median 8.0), tier 2 papers had a mean of 18.7 (median 8.0), and tier 3 papers had a mean of 7.8 (median 3.0). Neuro-oncology papers received the highest number of citations per publication (mean 25.7). The most common journal was the Journal of Neurosurgery: Pediatrics (20%). MD/PhD faculty members had significantly more citations per publication than MD faculty members (mean 26.7 vs 14.0; p < 0.0001) and also a higher number of publications per author (mean 38.6 vs 20.8). The median i h(5)- and i r(5)-indices per program were 14 (range 5-48) and 10 (range 5.6-37.2), respectively. The mean i r(5)/i h(5)-index ratio was 0.8. The top 5 fellowship programs (in descending order) as ranked by the i h(5)-index corrected for number of faculty members were The Hospital for Sick Children, Toronto; Children's Hospital of Pittsburgh; University of California, San Francisco Benioff Children's Hospital; Seattle Children's Hospital; and St. Louis Children's Hospital. CONCLUSIONS About two-thirds of publications authored by pediatric neurosurgeons are pediatrics-specific, although non-pediatrics-specific articles averaged more citations. Most of the articles authored by pediatric neurosurgeons are clinical, with basic and translational articles averaging more citations. Neurosurgeons with PhD degrees averaged more total publications and more citations per publication. In all, this is the most advanced and informative analysis of publication productivity in pediatric neurosurgery to date.

Sodium Threshold in Model Reduced and Low Fat Oil-in-Water Emulsion Systems.

Sodium and fat reduction in the diet are key factors in the nutrition management of hypertensive individuals. Several reduced and lower fat foods have higher amounts of sodium than their regular fat counterparts, which contradict sodium and fat reduction goals for hypertensive individuals. The objective of this research was to determine the threshold of sodium in a model reduced and low fat oil-in-water emulsion system analogous to salad dressings, so as to identify a reduction level of sodium that may not compromise consumer acceptability. Thirty panelists used the R-index by rating method to evaluate a model reduced fat emulsion system with 7 sodium concentrations (175, 200, 230, 265, 305, and 350 mg) and a model low fat emulsion system with 6 sodium concentrations (160, 170, 180, 190, and 200 mg). For both emulsion systems, 30 g servings of each concentration were presented to panelists. Panelists received 10 replicates of noise and signal samples for both fat levels. The group sodium threshold for the reduced and low fat emulsions was 241.11 and 183.56 mg, respectively. Results indicate saltiness perception is increased when fat content is decreased, and threshold for sodium in the reduced fat emulsion system is higher than the low fat emulsion system with lower fat content. Study findings show opportunities for sodium reduction in reduced and low fat food emulsion systems, particularly additional reductions of sodium without consumer detection. PRACTICAL APPLICATION: Study results demonstrated sodium difference thresholds for the reduced and low fat emulsions were at levels lower than the mean sodium content found in comparable processed food emulsion systems. Results indicate sodium content can potentially be decreased in reduced and lower fat food emulsion systems without consumer detection. Having insight for where consumers are able to detect a difference in sodium levels within reduced and low fat food systems can contribute to a successful reduction of sodium in reduced and lower fat food systems and benefit individuals requiring reductions of sodium and fat in processed food systems.

Influence of the fiber from agro-industrial co-products as functional food ingredient on the acceptance, neophobia and sensory characteristics of cooked sausages.

The sensory analysis of new products is essential for subsequent acceptance by consumers, moreover in the functional food market. The acceptance and food neophobia of cooked sausages formulated with cactus pear fiber or pineapple pear fiber, as functional ingredient, was complemented with a sensory characterization by R-index and qualitative descriptive analysis (QDA). Female consumers aged between 40 and 50 years showed greater interest in the consumption of healthy foods, with a higher level of food neophobia towards pineapple fiber sausages. R-index for taste was higher in pineapple fiber samples. Cactus pear fiber samples presented higher R-index score for texture. In QDA, color, sweet, astringent and bitter flavors, pork meat smell and a firm and plastic texture were significant, with a good relationship (38%) between the evaluated attributes. Sensory attributes are important on the acceptance and neophobia of functional foods like cooked sausages with fruit peel fiber as functional ingredient.

Reverberation index: a novel metric by which to quantify the impact of a scientific entity on a given field.

The authors propose a novel bibilometric index, the reverberation index (r-index), as a comparative assessment tool for use in determining differential reverberation between scientific fields for a given scientific entity. Conversely, this may allow comparison of 2 similar scientific entities within a single scientific field. This index is calculated using a relatively simple 3-step process. Briefly, Thompson Reuters' Web of Science is used to produce a citation report for a unique search parameter (this may be an author, journal article, or topical key word). From this citation report, a list of citing journals is retrieved from which a weighted ratio of citation patterns across journals can be calculated. This r-index is then used to compare the reverberation of the original search parameter across different fields of study or wherever a comparison is required. The advantage of this novel tool is its ability to transcend a specific component of the scientific process. This affords application to a diverse range of entities, including an author, a journal article, or a topical key word, for effective comparison of that entity's reverberation within a scientific arena. The authors introduce the context for and applications of the r-index, emphasizing neurosurgical topics and journals for illustration purposes. It should be kept in mind, however, that the r-index is readily applicable across all fields of study.

Caracterización bibliométrica de la comunidad científica especializada en el estudio de la alopecia areata / Bibliometric characterization of the scientific community specialized in the study of alopecia areata

Los dominios del conocimiento están estrechamente relacionados con las comunidades que se integran para su configuración y desarrollo. La bibliometría constituye una de las formas más objetivas para el análisis y caracterización de estas comunidades. El presente estudio caracterizó, a través de la perspectiva bibliométrica, el comportamiento de la comunidad científica especializada en alopecia areata durante el período 2001-2014. Se utilizó el Web of Science como fuente de información. Se empleó una batería de indicadores bibliométricos para determinar la productividad, influencia y rendimiento de los autores, así como para caracterizar sus investigaciones más relevantes. Se determinó la productividad anual, que evidenció durante el período un crecimiento exponencial de la producción científica sobre Alopecia Areata. Se identificaron las 15 revistas más productivas, que generaron alrededor del 50 por ciento del volumen total de artículos. Se identificó el núcleo de autores más visibles del dominio. Se demostró la utilidad del índice H no solo como indicador para determinar el rendimiento científico, sino también como medida para la identificación del liderazgo en dominios del conocimiento científico, especialmente en el campo de las Ciencias Médicas(AU)

Knowledge domains are strongly related to the communities integrated for their configuration and development. Bibliometrics is one of the more objective ways to the analysis and characterization of these communities. The current study characterized the behavior of the scientific community specialized on alopecia areata during the period 2001-2014, through a bibliometric perspective. The Web of Science was used as information source. A battery of bibliometric indicators was used to determine productivity, influence and performance of authors, as well as to characterize their most relevant researches. Annual productivity was determined. An exponential growth of the scientific output on alopecia areata was observed. The fifteen most productive journals were identified, which produced around 50 percent of the total amount of articles. The most visible author´s core was identified. The value of H index was demonstrated, not only as indicator to determine scientific performance, but also as a measure to identify leadership in scientific knowledge domains, especially in the field of Medical Sciences(AU)

Caracterización bibliométrica de la comunidad científica especializada en el estudio de la alopecia areata / Bibliometric characterization of the scientific community specialized in the study of alopecia areata

Los dominios del conocimiento están estrechamente relacionados con las comunidades que se integran para su configuración y desarrollo. La bibliometría constituye una de las formas más objetivas para el análisis y caracterización de estas comunidades. El presente estudio caracterizó, a través de la perspectiva bibliométrica, el comportamiento de la comunidad científica especializada en alopecia areata durante el período 2001-2014. Se utilizó el Web of Science como fuente de información. Se empleó una batería de indicadores bibliométricos para determinar la productividad, influencia y rendimiento de los autores, así como para caracterizar sus investigaciones más relevantes. Se determinó la productividad anual, que evidenció durante el período un crecimiento exponencial de la producción científica sobre Alopecia Areata. Se identificaron las 15 revistas más productivas, que generaron alrededor del 50 % del volumen total de artículos. Se identificó el núcleo de autores más visibles del dominio. Se demostró la utilidad del índice H no solo como indicador para determinar el rendimiento científico, sino también como medida para la identificación del liderazgo en dominios del conocimiento científico, especialmente en el campo de las Ciencias Médicas.

Knowledge domains are strongly related to the communities integrated for their configuration and development. Bibliometrics is one of the more objective ways to the analysis and characterization of these communities. The current study characterized the behavior of the scientific community specialized on alopecia areata during the period 2001-2014, through a bibliometric perspective. The Web of Science was used as information source. A battery of bibliometric indicators was used to determine productivity, influence and performance of authors, as well as to characterize their most relevant researches. Annual productivity was determined. An exponential growth of the scientific output on alopecia areata was observed. The fifteen most productive journals were identified, which produced around 50 % of the total amount of articles. The most visible author´s core was identified. The value of H index was demonstrated, not only as indicator to determine scientific performance, but also as a measure to identify leadership in scientific knowledge domains, especially in the field of Medical Sciences.