Database Retrieval Research Articles

Spatial orientation tasks show moderate to high accuracy for the diagnosis of mild cognitive impairment: a systematic literature review.

Spatial disorientation has been observed in mild cognitive impairment (MCI) and is associated with a higher risk of progression to Alzheimer's disease (AD). However, there is no gold standard assessment for spatial orientation and paper-and-pencil tests lack ecological validity. Recently, there has been an increasing number of studies demonstrating the role of spatial disorientation as a cognitive marker of pathological decline, shedding new light on its importance for MCI. This systematic review aimed to investigate the accuracy of spatial orientation tasks for the diagnosis of MCI by comparison with cognitively healthy elderly. The search was conducted in the databases Medical Literature Analysis and Retrieval System Online (MEDLINE/PubMed), Web of Science, Scopus, Excerpta Medica Database (Embase), Literatura Latino-Americana e do Caribe em Ciências da Saúde (Lilacs) and Scientific Electronic Library Online (SciELO). Only original studies reporting spatial orientation assessment in MCI patients compared to a healthy control group were included. Studies were excluded if the MCI classification did not follow well described criteria and/or if accuracy results of spatial orientation assessment were not provided. Seven studies met the eligibility criteria, describing a variety of spatial orientation assessments including questionnaires, paper-and-pencil, office-based route learning, and computer-based and virtual reality tasks. Spatial orientation tasks demonstrated moderate to high accuracy in detecting elderly with MCI compared to cognitively healthy elderly, with areas under the curve (AUC) ranging from 0.77 to 0.99. However, important methodological issues were found in the selected studies which should be considered when interpreting results. Although the inclusion of spatial orientation assessments in MCI evaluations seems to have significant value, further studies are needed to clarify their true capacity to distinguish pathological from non-pathological aging.

A systematic review of association between fine particle exposure and children's behavior

Background: Fine particles (PM2.5) have a health effect on the nerves, which may further cause various behavioral problems. Children are susceptible groups, and behavioral problems can seriously affect children's growth and development and interpersonal communication. Methods: Online retrieval databases include PubMed, Web of Science, CNKI and Wanfang. All the published epidemiological literature on the relationship between fine particulate matter and children's behavior was collected by means of searching keywords and tracing related references. Results: A total of 25 qualified literatures were retrieved to study the relationship between PM2.5 and children's behavioral problems mainly from two aspects: behavioral disorder disease and children's abnormal behavior score. The existence of this correlation requires reasonable physiological mechanisms. In future studies in China, longitudinal cohort studies should be carried out to enhance the demonstration of the causal relationship between fine particulate pollution and children's behavior problems. Conclusions: This review suggests that there is an association between PM2.5 pollution and children's behavioral problems, and its impact on children's health cannot be ignored.Unlike diseases, there is no clear treatment for behavioral problems, which should be prevented during children's growth and development. Key words: Children; Behavior problems; Fine particulate matter. Fund program: National Natural Science Foundation of China (41907368, 21906156,41907367)

A Database Retrieval Method for the Prediction of Reduced Luminance Coefficient Tables of A Road Surface Based on Measurements in Situ

ABSTRACT The in-situ measurements for r-tables have attracted special attention owing to their advantages, including increased speed, simplicity of use, and nondestructive road effects. This study presents a database retrieval method proposed for the construction of a full-scale r-table from an r-table database with 25 r coefficients used as input parameters. The proposed method was used in an in-situ, r-table measurement device as a data processing algorithm. To evaluate the accuracy of the proposed method, we performed a) a table-to-table comparison of Q0, S1, and r-tables constructed by different methods and b) comparisons of calculated luminance values (Lave, U0, Ul ) for a typical road-lighting scenario based on the use of different r-tables. The table-to-table comparison results indicate that following the application of the proposed method, its calculated Q0, S1, and r-table values were very close to their actual values (EQ0 = 2.1%, ES1 = 5.0%, Error = 2.3%). Moreover, the proposed method also exhibits an improved luminance response and achieves a 2.1% difference in Lave , a 3% difference in U0 , and a 1.8% difference in Ul . This study proves that the proposed database retrieval method improves the model prediction accuracy.

Using the contextual language model BERT for multi-criteria classification of scientific articles.

Finding specific scientific articles in a large collection is an important natural language processing challenge in the biomedical domain. Systematic reviews and interactive article search are the type of downstream applications that benefit from addressing this problem. The task often involves screening articles for a combination of selection criteria. While machine learning was previously used for this purpose, it is not known if different criteria should be modeled together or separately in an ensemble model. The performance impact of the modern contextual language models on the task is also not known. We framed the problem as text classification and conducted experiments to compare ensemble architectures, where the selection criteria were mapped to the components of the ensemble. We proposed a novel cascade ensemble analogous to the step-wise screening process employed in developing the gold standard. We compared performance of the ensembles with a single integrated model, which we refer to as the individual task learner (ITL). We used SciBERT, a variant of BERT pre-trained on scientific articles, and conducted experiments using a manually annotated dataset of ~49K MEDLINE abstracts, known as Clinical Hedges. The cascade ensemble had significantly higher precision (0.663 vs. 0.388 vs. 0.478 vs. 0.320) and F measure (0.753 vs. 0.553 vs. 0.628 vs. 0.477) than ITL and ensembles using Boolean logic and a feed-forward network. However, ITL had significantly higher recall than the other classifiers (0.965 vs. 0.872 vs. 0.917 vs. 0.944). In fixed high recall studies, ITL achieved 0.509 precision @ 0.970 recall and 0.381 precision @ 0.985 recall on a subset that was studied earlier, and 0.295 precision @ 0.985 recall on the full dataset, all of which were improvements over the previous studies. Pre-trained neural contextual language models (e.g. SciBERT) performed well for screening scientific articles. Performance at high fixed recall makes the single integrated model (ITL) more suitable among the architectures considered here, for systematic reviews. However, high F measure of the cascade ensemble makes it a better approach for interactive search applications. The effectiveness of the cascade ensemble architecture suggests broader applicability beyond this task and the dataset, and the approach is analogous to query optimization in Information Retrieval and query optimization in databases.

Comparing Video Activity Classifiers within a Novel Framework

Video activity classification has many applications. It is challenging because of the diverse characteristics of different events. In this paper, we examined different approaches to event classification within a general framework for video activity detection and classification. In our experiments, we focused on event classification in which we explored a deep learning-based approach, a rule-based approach, and a hybrid combination of the previous two approaches. Experimental results using the well-known Video Image Retrieval and Analysis Tool (VIRAT) database showed that the proposed classification approaches within the framework are promising and more research is needed in this area

O cuidado de saúde de crianças estomizadas: uma revisão integrativa da literatura

Objetivo: Analisar a partir de publicações científicas como o cuidado de saúde se expressa no cotidiano de crianças com estomias. Método: revisão integrativa da literatura realizada entre os anos de 2009 a 2019, nas bases de dados Medical Literature and Retrivial System on Line, Literatura Latino-Americana e do Caribe em Ciências da Saúde e Base de dados em Enfermagem, utilizando os descritores: estomia, criança e cuidados de saúde. Análises e Resultados: A amostra foi constituída de oito artigos, sendo construídas duas categorias para análise: o cuidado da criança estomizada no contexto familiar e o contexto do cuidado da criança estomizadas sob o olhar dos profissionais de saúde e de educação. Conclusão: Verificaram-se poucas produções relacionadas aos cuidados de saúde para crianças estomizadas. No entanto fica contemplado o reconhecimento da importância de valorizar as diretrizes da integralidade no cuidado, na perspectiva de consolidação do Sistema Único de Saúde.

DbCAN-PUL: a database of experimentally characterized CAZyme gene clusters and their substrates

PULs (polysaccharide utilization loci) are discrete gene clusters of CAZymes (Carbohydrate Active EnZymes) and other genes that work together to digest and utilize carbohydrate substrates. While PULs have been extensively characterized in Bacteroidetes, there exist PULs from other bacterial phyla, as well as archaea and metagenomes, that remain to be catalogued in a database for efficient retrieval. We have developed an online database dbCAN-PUL (http://bcb.unl.edu/dbCAN_PUL/) to display experimentally verified CAZyme-containing PULs from literature with pertinent metadata, sequences, and annotation. Compared to other online CAZyme and PUL resources, dbCAN-PUL has the following new features: (i) Batch download of PUL data by target substrate, species/genome, genus, or experimental characterization method; (ii) Annotation for each PUL that displays associated metadata such as substrate(s), experimental characterization method(s) and protein sequence information, (iii) Links to external annotation pages for CAZymes (CAZy), transporters (UniProt) and other genes, (iv) Display of homologous gene clusters in GenBank sequences via integrated MultiGeneBlast tool and (v) An integrated BLASTX service available for users to query their sequences against PUL proteins in dbCAN-PUL. With these features, dbCAN-PUL will be an important repository for CAZyme and PUL research, complementing our other web servers and databases (dbCAN2, dbCAN-seq).

Uncertain database retrieval with measure – Based belief function attribute values under intuitionistic fuzzy set

The uncertain database retrieval with measure-based belief function attribute values can resolve concerns of database retrieval and decision making, particularly in the circumstances of classical fuzzy set, which has an assuring perspective. However, how to implement the design to explore the things based on the intuitionistic fuzzy set (IFS) in an ambiguous database is yet an open problem. This paper addresses the issues of database retrieval based on the uncertain database with the things related to IFS. In the design, a query associated with each attribute of the objects is located in terms of IFS. Based on the gathered data, the “plausibilities (Pl)” and “beliefs (Bel)” measures are measured for each attribute and then aggregated with the cooperation of the Choquet integral (CI) operator. The “satisfaction degree” of the query in the form of an interval is circumscribed as [Bel, Pl]. The defuzzified value of this interval is affirmed with the aid of golden rule representative value, to link these satisfaction degrees. Hence, the appearance of the stated algorithm is more trustworthy than the others under an unpredictable environment. The description of the asserted algorithm is illustrated with an application correlated to the library database.

Medical image retrieval for detecting pneumonia using binary classification with deep convolutional neural networks

In the concepts of Deep Learning, the ConvNets are a classified group of deep neural networks that are used to analyze the images. They find their major applications in image classification and in the medical image analysis. ConvNets are regularized, multilayer preceptors. The major challenge today is the increasing size of medical image repositories which is leading to troubles in large database management and then querying those databases for storage and retrieval and this can only be sorted out using the Content Based Medical Image Retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low-level visual information captured by the imaging devices and high-level semantic information captured by human. This paper demonstrates a new and efficient framework with the algorithm of Deep CNN for the Feature Based Medical Image Retrieval (FBMIR) system for fast and efficient retrieval of medical and clinical images for detecting Pneumonia. The intermodal datasets are used and divided into two classes to train the network. One class consists of the lungs infected by Pneumonia and the other class contains the images of lungs which are normal. The learned features from the trained deep convolutional neural network with the fast processing feature of the hash method are used to reduce the feature space. To achieve the best retrieval results with class-based predictions 93.9% of average classification accuracy is achieved and 0.87 of mean average precision is achieved for this retrieval task. The proposed framework fit the best for top-ranked multimodal medical image retrieval.

Drug-induced skin ulcers: A disproportionality analysis from the WHO pharmacovigilance database

Drug-induced skin ulcers: A disproportionality analysis from the WHO pharmacovigilance database

Genotoxicity Evaluation of Titanium Dioxide Nanoparticles In Vitro: a Systematic Review of the Literature and Meta-analysis.

With the wide use of titanium dioxide nanoparticles (TiO2-NPs), the genotoxicity of TiO2-NPs, which is a factor for safety assessment, has attracted people's attention. However, their genotoxic effects in vitro remain controversial due to inconsistent reports. Therefore, a systematic review was conducted followed by a meta-analysis to reveal whether TiO2-NPs cause genotoxicity in vitro. A total of 59 studies were identified in this review through exhaustive database retrieval and exclusion. Meta-analysis results were presented based on different evaluation methods. The results showed that TiO2-NP exposure considerably increased the percentage of DNA in tail and olive tail moment in comet assay. Gene mutation assay revealed that TiO2-NPs could also induce gene mutation. However, TiO2-NP exposure had no effect on micronucleus (MN) formation in the MN assay. Subgroup analysis showed that normal cells were more vulnerable to toxicity induced by TiO2-NPs. Moreover, mixed form and small particles of TiO2-NPs increased the percentage of DNA in tail. In addition, short-term exposure could detect more DNA damage. The size, coating, duration, and concentration of TiO2-NPs influenced MN formation. This study presented that TiO2-NP exposure could cause genotoxicity in vitro. The physicochemical properties of TiO2-NPs and experimental protocols influence the genotoxic effects in vitro. Comet and gene mutation assays may be more sensitive to the detection of TiO2-NP genotoxic effects.

Analysis and Visualization of Scientific Products Related to the Effect of Biorhythm and Sleep Cycle on Information Retrieval in Scopus and PubMed Databases

Analysis and Visualization of Scientific Products Related to the Effect of Biorhythm and Sleep Cycle on Information Retrieval in Scopus and PubMed Databases

Uso de las tecnologías de la información y comunicación como herramienta de apoyo en el aprendizaje de los estudiantes de medicina

El uso de medios electrónicos como herramienta educativa es algo muy frecuente en la sociedad global del conocimiento, favorecido por el avance tecnológico. Se emplean nuevas alternativas en la práctica docente y en los procesos educativos, y las instituciones dedicadas a la educación médica no son la excepción. El objetivo de este trabajo fue establecer el uso de las tecnologías de la información y la comunicación (TIC) para el aprendizaje en estudiantes de medicina de la Universidad Autónoma de Tamaulipas, campus sur. Se siguió un estudio descriptivo, observacional y transversal. Los datos se recabaron a través de un cuestionario aplicado a 109 estudiantes. Del total de participantes, el 90.83 % contaba con computadora portátil, el 94.50 % con internet, ya sea en computadora o celular. El tiempo empleado diariamente usando las TIC para actividades educativas era entre 2 h y 4 h y el Google académico fue la base de datos más empleada para búsqueda de información. Por otro lado, se encontró que entre el 50 % y el 75 % de los encuestados tienen comprensión y dominio del idioma inglés. El uso de las TIC es de gran apoyo para los estudiantes, ya que les permite facilitar las búsquedas de información científica y médica, además de desarrollar competencias en el procesamiento y manejo de la información, para intercambiar información científica actualizada y continuar con su educación médica.

The effectiveness of Du moxibustion for ankylosing spondylitis: A protocol for systematic review and meta-analysis of randomized clinical trials.

Background:Ankylosing spondylitis (AS) is a common progressive autoimmune inflammatory disease. Du moxibustion can effectively treat AS with few adverse reactions. The aim of this protocol is to systematically investigate the effectiveness and safety for management of AS with Du moxibustion.Methods:Seven relevant databases, namely, PubMed, Cochrane Library, Embase, Chinese Biomedical Literatures Database (CBM), China National Knowledge Infrastructure (CNKI), WangFang Database (WF), Chinese Scientific Journal Database (VIP) will be searched from their inception until May 1st, 2020. All clinical randomized controlled trials containing eligible interventions(s) and outcome(s) will be included, regardless of blinding or publication types. Two reviewers will independently retrieval databases, extract data, and then assess the quality of studies. Data synthesis will be conducted by RevMan 5.3 software. We regard the effective rate, Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Visual Analogue Scale (VAS) as the primary outcomes, and the secondary outcomes contain C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), finger-to-floor distance (FFD), occiput to wall distance (OWD), and side effects. The result about the curative effect and safety of Du moxibustion for AS will be presented as risk ratio for dichotomous data and mean differences with a 95% confidence interval for continuous data.Results:The finding will be presented in a journal or related conferences.Conclusions:This study expects to provide high-quality, evidence-based recommendations on further treatment for clinical guidance.PROSPERO registration number:CRD42020158727.

A systematic review and meta-analysis of the efficacy and safety of arbidol in the treatment of coronavirus disease 2019.

Background:Coronavirus disease 2019 (COVID-19) is highly contagious, and the epidemic has spread to hundreds of countries around the world, and seriously threatens the life safety of people around the world. Arbidol is an antiviral drug with high potential against COVID-19, but evidence of effectiveness and safety is lacking. The systematic review protocol aims to formulate a research plan that can evaluate the efficacy and safety of arbidol for COVID-19.Methods:The retrieval time will be from the database establishment to June 2020. The retrieval database will include the Cochrane Library, PubMed, Embase, OVID, CNKI, Wanfang, VIP, CBM, etc. The primary outcome will be clinical efficacy, and the secondary results will be accompanying symptoms, time for the temperature to return to normal, time of novel coronavirus nucleic acid turning negative, blood sample test, Computed Tomography examination, length of hospitalization, adverse reactions, and adverse events. RevManV.5.3 software will be used for meta-analysis, and fixed effects model, random-effects model, subgroup analysis, and descriptive analysis will be adopted according to the heterogeneity of the research results.Results:To provide the latest evidence of clinical efficacy and safety of arbidol in the treatment of COVID-19.Conclusion:Our study will provide the latest evidence analysis of the efficacy and safety of arbidol for COVID-19, to provide evidence-based medicine for the prevention and control of this epidemic.Registration details:PROSPERO CRD42020189203.

Link Budget Analysis in Designing a Web-application Tool for Military X-Band Satellite Communication

In satellite communication, Link Budget analysis is the most important part to determine gains and losses of signals from the transmitter to the receiver. Most importantly, it investigates system performance and optimum power which must be received at the receiver channel. In some cases, this information could be generated, saved for past data analysis, and share with peer users which are not found in existing web tools. Thus, it is obvious to design a new Link Budget calculator with users, database, and data retrieval support. This work focuses on designing a Link Budget web tool for X-band satellite communication through literature study and comparative analysis. The X-Band calculator is designed based on HTML, PHP, Javascript, and MySQL by ensuring several security issues, and can be accessed through mobile devices. This paper also focuses on the necessary equations of Link Budget for Uplink (Tx); Satellite; Downlink (Rx); Azimuth, Elevation, Distance analysis; and Rain attenuation. Though, comparative assessments among various web tools show some fluctuations, overall outputs show satisfactory results with small % of Errors (PoE) ensuring reliability and viability of the proposed X-Band tool for practical use.

Changes of Anti-tuberculosis Herbs Formula During Past Three Decades in Contrast to Ancient Ones.

To investigate the evolution of herbal medicine in treating tuberculosis (TB) and encourage anti-TB drug discovery and development. In this study, 477 ancient traditional Chinese medicine formulae were collected from the Dictionary of Traditional Chinese Medicine Prescriptions and 172 modern Chinese medicine formulae (from 1986 to 2016) were collected by searching 4 databases: WanFang Data Knowledge Service Platform, China National Knowledge Infrastructure Database (CNKI), China Science and Technology Journal Database (VIP) and Chinese Bio-medical Literature and Retrieval System (SinoMed) in Chinese. We restricted the search to publications in Chinese. Further data analysis was done using the Traditional Chinese Medicine Inheritance Support System version 2 Software. There were 425 herbs in the 477 ancient formulae and 257 herbs in the 172 modern formulae. Half of the top 30 herbs were shared by both modern and ancient prescriptions. They are Radix Ophiopogonis, Astragalus membranaceus, Fritillaria cirrhosa, Dried rehmannia glutinosa, Poria cocos, Angelica sinensis, Prepared rehmannia glutinosa, Platycodon Root, Radix paeoniae alba, Schisandra chinensis, Bighead atractylodes rhizome, Rhizoma anemarrhenae, Cortex lycii radicis and Radix Scutellariae. Only two groups of herbs with a high correlation coefficient were found in both modern and ancient prescriptions, the Dried rehmannia glutinosa with Radix ophiopogonis, and Radix ophiopogonis with Prepared rehmannia glutinosa. There were 9 and 15 core herb combinations in modern and ancient prescriptions, respectively, but no one was found simutaniously in both modern and ancient prescriptions. Although there were wide variations in the herb groups and herb combinations in the formulae, half of the top 30 herbs were found in both modern and ancient prescriptions. The core herb combinations in modern and ancient prescriptions could help us to improve the priscription for treatment of TB.

Large-Scale Video Retrieval via Deep Local Convolutional Features

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

Content-based Image Retrieval Speedup Based on Optimized Combination of Wavelet and Zernike Features Using Particle Swarm Optimization Algorithm

This paper presents a novel method to speedup content-based image retrieval (CBIR) systems. The proposed method can be very useful for retrieving images from a large database. For this task, Zernike and Wavelet features are first extracted from the query image, then an interval of potential matching images is computed from the database images using the extracted feature. Therefore, the query image is compared with images in the interval rather than the whole database which to speedup the retrieval process. Particle swarm optimization is employed to select relevant features among Zernike and Wavelet features, which leads to decrease feature extraction time. Three types of experiments are conducted to evaluate effectiveness the proposed method in terms of database reduction, retrieval accuracy and retrieval time. In the best case, the Corel-1k database is averagely reduced up to 33.98% from its original size, and preserving 71.92% of relevant images. Retrieval accuracy in reduced database is increased by 1% in comparison with retrieving from the original database. Meanwhile, the retrieval time is reduced up to 58.57% in comparison with retrieval time from the original database.

Extensive screening of cyclopeptide toxins in mushrooms by ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap mass spectrometry

A non-target screening method of cyclopeptide toxins and their analogues in mushroom was developed, using ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap mass spectrometry (UHPLC-Q-Orbitrap MS) followed by mass spectrometry databases retrieval and software tools analysis for the candidate analogues. Three cyclopeptide toxins in the toxic mushroom Amanita rimosa were firstly screened without standard, and two of them were unknown analogues which were tentatively identified by the accurate masses, isotopic patterns and characteristic fragments. A validated quantitative method was performed to rapidly quantify three major cyclopeptide toxins in the Amanita rimosa sample including α-manitin, β-amanitin and phalloidin, and their contents were detected to be 4.52 mg/kg, 2.37 mg/kg and 2.53 mg/kg, respectively. The developed method has good selectivity and sensitivity for rapid and comprehensive screening the cyclopeptide toxins and their analogues in mushrooms at trace levels. Successful non-target screening of trace cyclopeptide toxin analogues will guarantee the food safety in mushrooms consumption.