Database Subsets Research Articles

Cold-formed steel framed shear wall test database

Many monotonic and cyclic tests have been conducted on cold-formed steel framed shear walls in the last 20 years. Cold-formed steel framed shear wall provisions in AISI S240, AISI S400, and ASCE 41 are supported by the data obtained through these tests. The main objective of this article is to introduce a recently compiled cold-formed steel framed shear wall test database, to reveal the database structure, and to explain how to access and present the data. Most recently, the database has been standardized and expanded to include additional tests, complete cyclic information from tests, limit states, and code prediction information. The database structure incorporates a central Excel spreadsheet that includes descriptive information; ordered plain text files for each individual test; and custom MATLAB codes, which can read, process, and plot designated database subsets. The provided database can advance the understanding and modeling of cold-formed steel framed shear walls.

Ensemble classification combining ResNet and handcrafted features with three-steps training

Objective. This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardiology Challenge/CinC Challenge 2021. It aims to integrate deep and classic machine learning features into a single model, exploring the proper structure and training procedure. Approach. From the initial 88 253 signals, only 84 210 were included. Low quality and unscored recordings were excluded. Three different database subsets of 40 365 recording each were created by dividing in three normal sinus rhythm and sinus bradycardia recordings. Each subset was used to train a different model with shared architecture integrated as an ensemble to provide the final classification through major voting. Models contained a deep branch composed of a modified ResNet with dilation convolutional layers and squeeze and excitation Block that took as input windowed ECG signals. This was concatenated with a wide branch that integrated 20 cardiac rhythm features into a fully connected 3-layered network. Three different training steps were studied: just the deep branch (D), wide integration and training (D+W), and a final fine tuning of the deep branch posterior to wide training (D+W+D). Main Results. Results obtained in a local test set formed by a stratified 12.5% split of the given full dataset were presented for 2-lead and 12-lead models. The best training method was the 3-step D + W + D procedure obtaining a challenge metric of 0.709 and 0.677 for 12 and 2-lead models respectively. Significance. Integration of handcrafted features and deep learning model not only may increase the generalization capacity of the network but also provide a path to add explicit information into the classification decision process. To the best of our knowledge this is the first work studying the training procedure to properly integrate both types of information for ECG signals classification.

Novel lncRNAs with diagnostic or prognostic value screened out from breast cancer via bioinformatics analyses.

BackgroundRecent studies have shown that long non-coding RNAs (lncRNAs) may play key regulatory roles in many malignant tumors. This study investigated the use of novel lncRNA biomarkers in the diagnosis and prognosis of breast cancer.Materials and MethodsThe database subsets of The Cancer Genome Atlas (TCGA) by RNA-seq for comparing analysis of tissue samples between breast cancer and normal control groups were downloaded. Additionally, anticoagulant peripheral blood samples were collected and used in this cohort study. The extracellular vesicles (EVs) from the plasma were extracted and sequenced, then analyzed to determine the expressive profiles of the lncRNAs, and the cancer-related differentially expressed lncRNAs were screened out. The expressive profiles and associated downstream-mRNAs were assessed using bioinformatics (such as weighted correlation network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichments, Receiver-Operating Characteristic (ROC) curve and survival analysis, etc.) to investigate the diagnostic and prognostic values of these EV lncRNAs and their effectors.ResultsIn this study, 41 breast cancer-related lncRNAs were screen out from two datasets of tissue and fresh collected plasma samples of breast cancer via the transcriptomic and bioinformatics techniques. A total of 19 gene modules were identified with WGCNA analysis, of which five modules were significantly correlated with the clinical stage of breast cancer, including 28 lncRNA candidates. The ROC curves of these lncRNAs revealed that the area under the curve (AUC) of all candidates were great than 70%. However, eight lncRNAs had an AUC >70%, indicating that the combined one has a good diagnostic value. In addition, the results of survival analysis suggested that two lncRNAs with low expressive levels may indicate the poor prognosis of breast cancer. By tissue sample verification, C15orf54, AL157935.1, LINC01117, and SNHG3 were determined to have good diagnostic ability in breast cancer lesions, however, there was no significant difference in the plasma EVs of patients. Moreover, survival analysis data also showed that AL355974.2 may serve as an independent prognostic factor and as a protective factor.ConclusionA total of five lncRNAs found in this study could be developed as biomarkers for breast cancer patients, including four diagnostic markers (C15orf54, AL157935.1, LINC01117, and SNHG3) and a potential prognostic marker (AL355974.2).

Research on the Characteristic Model of Learners in Modern Distance Music Classroom Based on Big Data

This paper makes in-depth research on data mining, especially association rule mining, improves the FP-tree algorithm in both the algorithm itself and the data source, and finds out a mining algorithm suitable for learner characteristics. Association rule algorithm for actor feature model mining. By establishing the characteristic model of learners in modern distance music classroom, simulation experiments are carried out on FP-tree and three improved algorithms. This paper improves the FP-tree algorithm. Firstly, we improve the algorithm itself; aiming at the problem of too many frequent itemsets, an improved key item extraction algorithm KEFP-growth based on FP-growth is proposed, which ignores the frequent itemsets that are not concerned in the analysis. Then, improvements were made in terms of data sources. In view of the problem that the data source is too large, the mining efficiency is low, and the FP-tree cannot be loaded in memory, this paper proposes a data projection algorithm, which adopts the idea of divide and conquer, divides the frequent 1-itemsets of the database into database subsets of each frequent 1-itemsets, and then mines the database subsets separately and then merges them. Finally, the KEFP-growth algorithm and the projection algorithm are combined, which can not only eliminate the mining of meaningless frequent items but also divide the data when there is a large amount of data. This paper also compares the performance of the three improved algorithms and the original FP-tree algorithm through experiments. The experiments show that the combination of the KEFP-growth algorithm and the database projection algorithm is the most suitable one for the learner feature mining of the adaptive learning system. (1) The KEFP-growth algorithm reduces the number of frequent items output by the original FP-tree algorithm by about 50%, and the mining time is reduced by 50%. (2) The data projection algorithm is more suitable for data mining with less support. When the support is 10%, the mining time of the data projection algorithm is reduced by 80% compared with the FP-tree algorithm. (3) When the support degree is 10%, the running time of the hybrid algorithm is reduced by 10% compared with the KEFP-growth algorithm and the data projection algorithm.

The Mitochondrial DNA Landscape of Modern Mexico.

Mexico is a rich source for anthropological and population genetic studies with high diversity in ethnic and linguistic groups. The country witnessed the rise and fall of major civilizations, including the Maya and Aztec, but resulting from European colonization, the population landscape has dramatically changed. Today, the majority of Mexicans do not identify themselves as Indigenous but as admixed, and appear to have very little in common with their pre-Columbian predecessors. However, when the maternally inherited mitochondrial (mt)DNA is investigated in the modern Mexican population, this is not the case. Control region sequences of 2021 samples deriving from all over the country revealed an overwhelming Indigenous American legacy, with almost 90% of mtDNAs belonging to the four major pan-American haplogroups A2, B2, C1, and D1. This finding supports a very low European contribution to the Mexican gene pool by female colonizers and confirms the effectiveness of employing uniparental markers as a tool to reconstruct a country’s history. In addition, the distinct frequency and dispersal patterns of Indigenous American and West Eurasian clades highlight the benefit such large and country-wide databases provide for studying the impact of colonialism from a female perspective and population stratification. The importance of geographical database subsets not only for forensic application is clearly demonstrated.

Feature selection on database optimization for Wi-Fi fingerprint indoor positioning

Indoor location-based services have become very popular, principally, because of its wide and valuable applications. On that context, Wi-fi fingerprinting based on the received signal strength indicator (RSSI) has become very popular, due the fact that RSSI values are easily acquired. On the Wi-fi fingerprint method, machine learning algorithms are trained on the constructed fingerprint database and then used on a new entry to give the indoor location based on its estimations. Choosing the correct machine learning algorithm is one of the main problems in the literature. However the database sizes used during the training phase is also one of the main concerns. In this paper, a proposed feature selection method used on the original UJIIndoorLoc database created a smaller version of it, with the 30 highest RSSIs after the APIDs responsible for then in descending order, and created even smaller database subsets. Both databases, the original UJIIndoor Loc database and ours, were split into smaller subsets that were used on the classification problem according the DESIP method proposed in [1]. Six machine learning algorithms were deployed for training and testing the two database subsets with the classification attributes modified for symbolic localization. The J48 with the AdaBoost iterative algorithm gave the best results on both database subsets. The minimized database subsets showed smaller elapsed time results for all the classifications that were done. The accuracy results show similar results for both database subsets, on building and floor classification. Although, on the region attribute, the database subset with 520 attributes got better accuracy results than the reduced one.

AntiMalarial Mode of Action (AMMA) Database: Data Selection, Verification and Chemical Space Analysis

This paper presents the effort of collecting and curating a data set of 15461 molecules tested against the malaria parasite, with robust activity and mode of action annotations. The set is compiled from in-house experimental data and the public ChEMBL database subsets. We illustrate the usefulness of the dataset by building QSAR models for antimalarial activity and QSPR models for modes of actions, as well as by the analysis of the chemical space with the Generative Topographic Mapping method. The GTM models perform well in prediction of both activity and mode of actions, on par with the classical SVM methods. The visualization of obtained maps helps to understand the distribution of molecules corresponding to different modes of action: molecules with similar targets are located close to each other on the map. Therefore, this analysis may suggest new modes of action for non-annotated or even annotated compounds. In perspective, this can be used as a tool for prediction of both antimalarial activity and target for novel, untested compounds.

Emergency Risk Communication: Lessons Learned from a Rapid Review of Recent Gray Literature on Ebola, Zika, and Yellow Fever

ABSTRACTA rapid review of gray literature from 2015 to 2016 was conducted to identify the lessons learned for emergency risk communication from recent outbreaks of Ebola, Zika, and yellow fever. Gray literature databases and key websites were searched and requests for documents were posted to expert networks. A total of 83 documents met inclusion criteria, 68 of which are cited in this report. This article focuses on the 3 questions, out of 12 posed by World Health Organization as part of a Guideline development process, dealing most directly with communicating risk during health emergencies: community engagement, trust building, and social media. Documents were evaluated for credibility using an Authority, Accuracy, Coverage, Objectivity, Date, Significance (AACODS) checklist? and if the document contained a study, a method-specific tool was applied. A rapid content analysis of included sources was undertaken with relevant text either extracted verbatim or summarized and mapped against the questions. A database subset was created for each question and citations were assigned to the subset(s) for which they contained relevant information. Multiple designations per document were common. Database subsets were used to synthesize the results into a coherent narrative.The gray literature strongly underlines the central importance of local communities. A one-size-fits-all approach does not work. For maximum effectiveness, local communities need to be involved with and own emergency risk communication processes, preferably well before an emergency occurs. Social media can open new avenues for communication, but is not a general panacea and should not be viewed as a replacement for traditional modes of communication. In general, the gray literature indicates movement toward greater recognition of emergency risk communication as a vitally important element of public health.

Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies

Breast cancer is globally a major threat for women's health. Screening and adequate follow-up can significantly reduce the mortality from breast cancer. Human second reading of screening mammograms can increase breast cancer detection rates, whereas this has not been proven for current computer-aided detection systems as "second reader". Critical factors include the detection accuracy of the systems and the screening experience and training of the radiologist with the system. When assessing the performance of systems and system components, the choice of evaluation methods is particularly critical. Core assets herein are reference image databases and statistical methods. We have analyzed characteristics and usage of the currently largest publicly available mammography database, the Digital Database for Screening Mammography (DDSM) from the University of South Florida, in literature indexed in Medline, IEEE Xplore, SpringerLink, and SPIE, with respect to type of computer-aided diagnosis (CAD) (detection, CADe, or diagnostics, CADx), selection of database subsets, choice of evaluation method, and quality of descriptions. 59 publications presenting 106 evaluation studies met our selection criteria. In 54 studies (50.9%), the selection of test items (cases, images, regions of interest) extracted from the DDSM was not reproducible. Only 2 CADx studies, not any CADe studies, used the entire DDSM. The number of test items varies from 100 to 6000. Different statistical evaluation methods are chosen. Most common are train/test (34.9% of the studies), leave-one-out (23.6%), and N-fold cross-validation (18.9%). Database-related terminology tends to be imprecise or ambiguous, especially regarding the term "case". Overall, both the use of the DDSM as data source for evaluation of mammography CAD systems, and the application of statistical evaluation methods were found highly diverse. Results reported from different studies are therefore hardly comparable. Drawbacks of the DDSM (e.g. varying quality of lesion annotations) may contribute to the reasons. But larger bias seems to be caused by authors' own decisions upon study design. RECOMMENDATIONS/CONCLUSION: For future evaluation studies, we derive a set of 13 recommendations concerning the construction and usage of a test database, as well as the application of statistical evaluation methods.

Investigation of long-term field experiments on response of breeding lines to common scab in a potato breeding program

Common scab, caused by Streptomyces scabies, is a disease that produces scab-like surface lesions on potato tubers. Testing for susceptibility/resistance of breeding lines at the Potato Research Centre of Agriculture & Agri-Food Canada (AFC) is carried out in a scab nursery maintained at AFC for the annual scab evaluation field trials. A replicated field trial routinely consists of breeding lines from previous testing season(s) plus newly selected lines. Data of scab scores generated from long-term experiments thus formulate an incomplete 2-way table over combinations of breeding lines (genotypes) and trials (years). This requires an advanced statistical method to estimate genetic parameters for evaluation purposes. A data base with 1,435 scab index scores from 344 breeding lines were extracted from 5 years (1995–1999) of field experiments in the scab nursery maintained at AFC. The statistical method Residual Maximum Likelihood (REML) was employed to estimate variance components of the breeding population and Best Linear Unbiased Predictor (BLUP) was used to predict genetic merit of breeding lines. High heritability was obtained from variance components estimated by REML. The BLUP scores of breeding lines provided reliable evaluation of their responses to common scab. Two data base sub-sets were separately formulated from the original data base for those parents and grandparents of the breeding lines having pedigree records available. They were again subjected to REML and BLUP analyses to compare the responses to common scab and identify sources of resistance at the parental and grandparental levels. Two random data sets with equal sized samples of breeding lines were also generated from the over-all data base. The two sets of BLUP scores between corresponding breeding lines and parents showed high association which provides an assessment of the validity of the evaluation process.

Database Assessment of CPT-Based Design Methods for Axial Capacity of Driven Piles in Siliceous Sands

Numerous cone penetration test (CPT)-based methods exist for calculation of the axial pile capacity in sands, but no clear guidance is presently available to assist designers in the selection of the most appropriate method. To assist in this regard, this paper examines the predictive performance of a range of pile design methods against a newly compiled database of static load tests on driven piles in siliceous sands with adjacent CPT profiles. Seven driven pile design methods are considered, including the conventional American Petroleum Institute (API) approach, simplified CPT alpha methods, and four new CPT-based methods, which are now presented in the commentary of the 22nd edition of the API recommendations. Mean and standard deviation database statistics for the design methods are presented for the entire 77 pile database, as well as for smaller subset databases separated by pile material (steel and concrete), end condition (open versus closed), and direction of loading (tension versus compression). Certain methods are seen to exhibit bias toward length, relative density, cone tip resistance, and pile end condition. Other methods do not exhibit any apparent bias (even though their formulations differ significantly) due to the limited size of the database subsets and the large number of factors known to influence pile capacity in sand. The database statistics for the best performing methods are substantially better than those for the API approach and the simplified alpha methods. Improved predictive reliability will emerge with an extension of the database and the inclusion of additional important controlling factors affecting capacity.

Précis: from unstructured keywords as queries to structured databases as answers

Precis queries represent a novel way of accessing data, which combines ideas and techniques from the fields of databases and information retrieval. They are free-form, keyword-based, queries on top of relational databases that generate entire multi-relation databases, which are logical subsets of the original ones. A logical subset contains not only items directly related to the given query keywords but also items implicitly related to them in various ways, with the purpose of providing to the user much greater insight into the original data. In this paper, we lay the foundations for the concept of logical database subsets that are generated from precis queries under a generalized perspective that removes several restrictions of previous work. In particular, we extend the semantics of precis queries considering that they may contain multiple terms combined through the AND, OR, and NOT operators. On the basis of these extended semantics, we define the concept of a logical database subset, we identify the one that is most relevant to a given query, and we provide algorithms for its generation. Finally, we present an extensive set of experimental results that demonstrate the efficiency and benefits of our approach.

The dimensions of cited reference enhanced database subsets

PurposeThe purpose of this paper is to discuss databases with cited references.Design/methodology/approachThe paper considers the dimensions of cited reference enhanced database subsets.FindingsThe typical database has the ski slope shape, as there is a fairly steady, gradual growth in the yearly number of records added. This is not true for the shape of the cited references subset, as in most databases cited references have been added to the records only for the past few years.Practical implicationsThere are many features which are specific to cited references, and thus the traditional bibliographic database design is just not sufficient.Originality/valueThe paper examines the issues surrounding cited reference enhanced database subsets.

How big is a database versus how is a database big

PurposeThe purpose of this paper is to examine the sizes of databases.Design/methodology/approachThe paper illustrates the various dimensions of some library and information science (LIS) databases and database subsets, and the implications of these differences.FindingsEnhancements of database records by cited references have great potential for guiding the searcher to cited and cited references.Originality/valueIllustrates the various dimensions of some LIS databases and database subsets, and the implications of these differences.

MinSet: a general approach to derive maximally representative database subsets by using fragment dictionaries and its application to the SCOP database

The size of current protein databases is a challenge for many Bioinformatics applications, both in terms of processing speed and information redundancy. It may be therefore desirable to efficiently reduce the database of interest to a maximally representative subset. The MinSet method employs a combination of a Suffix Tree and a Genetic Algorithm for the generation, selection and assessment of database subsets. The approach is generally applicable to any type of string-encoded data, allowing for a drastic reduction of the database size whilst retaining most of the information contained in the original set. We demonstrate the performance of the method on a database of protein domain structures encoded as strings. We used the SCOP40 domain database by translating protein structures into character strings by means of a structural alphabet and by extracting optimized subsets according to an entropy score that is based on a constant-length fragment dictionary. Therefore, optimized subsets are maximally representative for the distribution and range of local structures. Subsets containing only 10% of the SCOP structure classes show a coverage of >90% for fragments of length 1-4. http://mathbio.nimr.mrc.ac.uk/~jkleinj/MinSet. Supplementary data are available at Bioinformatics online.

Prosodic and accentual information for automatic speech recognition

Various aspects relating to the human production and perception of speech have gradually been incorporated into automatic speech recognition systems. Nevertheless, the set of speech prosodic features has not yet been used in an explicit way in the recognition process itself. This study presents an analysis of prosody's three most important parameters, namely energy, fundamental frequency and duration, together with a method for incorporating this information into automatic speech recognition. On the basis of a preliminary analysis, a design is proposed for a prosodic feature classifier in which these parameters are associated with orthographic accentuation. Prosodic-accentual features are incorporated in a hidden Markov model recognizer; their theoretical formulation and experimental setup are then presented. Several experiments were conducted to show how the method performs with a Spanish continuous-speech database. Using this approach to process other database subsets, we obtained a word recognition error reduction rate of 28.91%.

The CH⋯π interaction as an important factor in the crystal packing and in determining the structure of clathratesA comprehensive literature list for the CH⋯π interaction is available on the following website: http://www.tim.hi-ho.ne.jp/dionisio

Crystal structures in the Cambridge Structural Database (CSD) were examined to investigate the role of the CH⋯π interaction in the crystal packing and the structure of clathrates. More than 77% of organic crystals have been found to bear CH⋯π interatomic distances shorter than 3.05 A. Database subsets were edited by monitoring the entire CSD with the structure of included solvents (CHCl3, CH2Cl2, MeNO2, MeCN, MeOH, Me2CO, DMSO, DMF, 1,4-dioxane, benzene, toluene and p-xylene). A great many of the examined compounds have been shown to bear short CH⋯π distances among themselves and between supramolecular components. A variety of different solvents (protic, dipolar aprotic as well as non-polar) has been found to be included by CH⋯π interactions. The crystal structures were retrieved and examined in detail for clathrates of 1,1,6,6-tetraphenylhexa-2,4-diyne-1,6-diol and 1,1′-binaphthyl-2,2′-dicarboxylic acid in complexation with their specific guests. The results are discussed in the context of the CH⋯π interaction in controlling the specific structure of supramolecular aggregates.

Selection of reagents for combinatorial synthesis using clique detection

The identification of a database subset in which all pairs of molecules have similarities less than some user-defined threshold is shown to be equivalent to the graph-theoretic problem of clique-detection. A comparison of several different clique-detection algorithms demonstrates that the algorithm due to Babel is by far the most efficient for this particular problem. The database subsets identified by the Babel algorithm are slightly more diverse than those identified by alternative, less time-consuming algorithms for dissimilarity-based compound selection.

Quantitation of cotton fibre-quality variations arising from boll and plant growth environments

Crop growth simulation models used to manage cultural inputs and to improve yields of cotton, Gossypium hirsutum L., do not address fibre quality, a major determinant of cotton fibre price and end-use. Fibre maturation simulations require rapid, reproducible methods for fibre quality quantitation at the boll or locule level. Combination of fibre quality mapping by fruiting site with quality quantitation by an electron-optical particle sizer provided replicated, reproducible data suitable for use in predictive models and quantitative studies of fibre quality variations attributable to genotype and growth environment. The efficacy and potential of this unique fusion of agronomic and textile technologies were examined through comparisons of three 1992 fibre quality database subsets from the US Southeastern Coastal Plain and Mississippi Delta. Comparisons of ‘Pee Dee 3’ fibre quality, on a locule-by-locule basis at positions 1 and 2 on main-stem nodes 5 through 18, revealed that fibre length, cross-sectional area, and physical maturity varied among fruiting sites. Subsurface microirrigation applied during an early-season drought increased fibre yield by 40%, significantly increased fibre fineness, and decreased fibre maturity indicators. Fibre length variations were compared between ginning methods and among nine genotypes grown in the Coastal Plain. Irrigation-related reductions in physical fibre maturity, found in the Coastal Plain, were contrasted with chronological maturities of ‘DPL5415’ and ‘DES119’ fibre harvested 21, 28, 35, 42, or 56 days post-anthesis in the Mississippi Delta. Fibre-quality mapping with particle-sizing represents a powerful, new tool for constructing fibre development simulations essential for improving cotton fibre quality and processing outcome.

LITTLE KEVIN: a program for the estimation of protein homology by analysing the amino acid compositions and sequences.

We present here a computational method based on the analysis of amino acid composition for performing comparisons between proteins. This user-friendly and reliable test is aimed at rapidly identifying, from data-base subsets, sequences--if necessary, partial sequences--which share similar amino acid compositions to the input composition (deduced from experimental results). Apparent molecular weight (as determined by SDS-PAGE) and artefactual modifications due to the experimental determination of the amino acid composition are taken into account to perform the comparison. This program thus constitutes a useful tool in searching for the probable identification of either non-sequenced proteins or peptides from hydrolysed proteins.