Inconsistencies In Databases Research Articles

Side-effects of Phytotherapeutics in Cancer Care - A Review of Inconsistencies in National and International Databases.

The interest in phytotherapy in patients with cancer is immensely high. When using herbal medicine, knowledge of potential side-effects is essential as part of counselling the patient. The aim of this study was therefore to collect information on the side-effects of popular medicinal plants and to compare different official and the most popular sources of information reporting the side-effects of phytotherapeutics. Four different databases were reviewed from 09 February 2021 to 01 March 2022. These were the German monographs of the German Federal Institute for Drugs and Medical Devices, the European monographs of the European Medicines Agency, the website About Herbs (https://www.mskcc.org/cancer-care/diagnosis-treatment/symptom-management/integrative-medicine/herbs) and the German Red List. A total of 171 medicinal plants were chosen from the German monographs. These plants were relevant for supportive cancer treatment, targeting symptoms e.g. pain, nausea, vomiting, mucositis or fatigue. Out of the 171 selected plants, only 20 were found in all four sources. The compilation of the data showed there to be a huge disparity in the number of plants listed by each database and the type and frequency of the side-effects described. The reasons for this are manifold: Lack of interest, different interest groups, different origins (Europe versus America) and no standardized terminology. Physicians should not rely exclusively on one source to inform themselves about potential side-effects of phytotherapeutics but should use several sources to ensure the best possible safety of the patient. Since there seems to be too few data on certain medicinal plants for which no side-effects have been documented, more clinical studies are needed.

Molecular identification of some species of dragonfly (Odonata: Anisoptera) using COXI gene and DNA barcodes from Basrah Province, Southern Iraq

The taxonomy of the Odonata group is still poorly understood; therefore, molecular identification has been developed to address the shortcomings of traditional taxonomy, such as misidentification and inconsistencies in reference databases. The mtCOXI gene has been used in animal barcoding studies because of its wide range of phylogenetic signals. Odonata, a diverse group of aquatic insects, has been studied at the molecular level to understand the evolutionary relationships between their species and global species. In the current study in Basrah Province, Iraq, we extracted mitochondrial DNA from adult and nymph Dragonflies and designed species-specific primers to distinguish some of the endemic species at the molecular level. This study identified five species, Crocothemis erythraea, Diplacodes trivialis, Orthetrum sabina, Trithemis annulata and Hemianax ephippiger. The DNA sequences were deposited in the NCBI database for the first time. The nucleotide sequences of the mtCOXI genes were analyzed via BLAST. The similarity results ranged from 91.8% to 100%, indicating related species on the basis of the branches of the phylogenetic tree. The molecular identity of the selected species was confirmed, and DNA barcodes for Anisoptera species from Basrah Province were successfully developed and documented in GenBank and IBIN.

On measuring inconsistency in graph databases with regular path constraints

Real-world data are often inconsistent. Although a substantial amount of research has been done on measuring inconsistency, this research concentrated on knowledge bases formalized in propositional logic. Recently, inconsistency measures have been introduced for relational databases. However, nowadays, real-world information is always more frequently represented by graph-based structures which offer a more intuitive conceptualization than relational ones. In this paper, we explore inconsistency measures for graph databases with regular path constraints, a class of integrity constraints based on a well-known navigational language for graph data. In this context, we define several inconsistency measures dealing with specific elements contributing to inconsistency in graph databases. We also define some rationality postulates that are desirable properties for an inconsistency measure for graph databases. We analyze the compliance of each measure with each postulate and find various degrees of satisfaction; in fact, one of the measures satisfies all the postulates. Finally, we investigate the data and combined complexity of the calculation of all the measures as well as the complexity of deciding whether a measure is lower than, equal to, or greater than a given threshold. It turns out that for a majority of the measures these problems are tractable, while for the other different levels of intractability are exhibited.

Lifecycle carbon footprints of buildings and sustainability pathways in China

The Paris Agreement, with a 2 °C temperature increase baseline and a 1.5 °C target temperature increase, imposes challenges to the sustainability of buildings. However, as an end-user or end-of-the-art, the building sector overlaps with other sectors, such as industry and transportation in building materials manufacturing (such as steel, concrete, cement, etc. ) and electricity use (such as building-to-vehicle charging), imposing difficulties in lifecycle carbon footprint quantification in buildings. In this study, the lifecycle carbon footprint of buildings and sustainability pathways in China are provided. Tools and platforms for lifecycle carbon footprint quantification in buildings are reviewed. A global database for lifecycle carbon footprint quantification in buildings is reviewed and compared, together with an analysis of the advantages and disadvantages. Furthermore, decarbonization technologies in the building operation stage are comprehensively reviewed, including the decarbonization potential, technology readiness level (TRL), techno-economic performance, and current technical status. Pathways on carbon neutrality in building sectors in China are provided. The results indicate that inconsistencies in global databases, unclear definitions of lifecycle carbon emissions, and inaccurate models of building energy consumption are the main challenges for accurate lifecycle carbon analysis in buildings. Various carbon neutrality transformation technologies can be classified into ultralow energy building and near zero-energy building technologies and into efficient heat pump and smart metering technologies. Pathways for low-carbon transition in building sectors include building energy saving (330 million tCO2 with 22%), renewable energy supply (299 million tCO2 with 20%), building electrification and power sector decarbonization (450 million tCO2 with 30%), and carbon capture, utilization and storage (CCUS) technology (420 million tCO2 with 28%). These research results can pave the way for upcoming studies on the lifecycle carbon footprint in buildings and sustainability pathways in China.

Benford’s law in atomic spectra and opacity databases

The intriguing law of anomalous numbers, also named Benford’s law, states that the significant digits of data follow a logarithmic distribution favoring the smallest values. In this work, we test the compliance with this law of the atomic databases developed at the National Institute of Standards and Technology (NIST) focusing on line energies, oscillator strengths, Einstein coefficients and radiative opacities. The considered databases are the Atomic Spectra Database (ASD) and the NIST-LANL (Los Alamos National Laboratory) Lanthanide/Actinide Opacity Database. The present study is not limited to the first digit and the case of multipole lines is also considered. The fact that the law is verified with a high accuracy means that the occurrence of digits reflects the constraints induced, in a given angular-momentum coupling, by the selection rules for atomic processes. As a consequence, Benford’s law may be of great interest to detect inconsistencies in atomic databases.

A Unifying Framework for Incompleteness, Inconsistency, and Uncertainty in Databases

Deploying possible world semantics and the challenge of computing the certain answers to queries.

On measuring inconsistency in definite and indefinite databases with denial constraints

Real-world databases are often inconsistent. Although there has been an extensive body of work on handling inconsistency, little work has been done on measuring inconsistency in databases. In this paper, building on work done on measuring inconsistency in propositional knowledge bases, we explore inconsistency measures (IMs) for definite and indefinite databases with denial constraints. We first introduce database IMs that are inspired by well-established methods to quantify inconsistency in propositional knowledge bases, but are tailored to the relational database context where data is generally the reason for inconsistency, not the integrity constraints. Then, we analyze the compliance of the database IMs with rationality postulates for both definite and indefinite databases. Finally, we investigate the complexity of the inconsistency measurement problem as well as of the problems of deciding whether the inconsistency is lower than, greater than, or equal to a given threshold for both the definite and the indefinite cases.

Healthcare Professionals' Perspectives on Improving Dietary Supplement Documentation in the Electronic Medical Record: Current Challenges and Opportunities to Enhance Quality of Care and Patient Safety.

Around half the US population uses dietary supplements (DS), and concomitant use with medications is common. Many DS include bioactive substances that can interact with medications; therefore, accurate tracking is critical for patient safety. Unfortunately, documentation of patients' DS use is often missing or incomplete in the electronic medical record (EMR), leaving patients susceptible to potential adverse events. Novel approaches to assist healthcare professionals (HCPs) in capturing patients' DS use are needed. To assess HCPs' perspectives on challenges and facilitators of DS documentation in the EMR and their opinions on a proposed mHealth application (app) to aid in DS capture. HCPs, recruited from professional networks, largely in North Carolina, using purposive sampling, took part in semi-structured interviews. We inquired about HCPs' experiences with DS documentation in the EMR and their opinions about our proposed mHealth app. Interviews were recorded, transcribed, and coded. Thematic analysis included deductive codes based on the interview guide, and inductive codes that emerged during transcript review. HCPs (N = 30) included 60% females, mean age 46 ± 10; 70% White. Pharmacists (20%), nurses (17%), and physicians (17%) were the most represented professions. Years in practice ranged from 3-35years. Most HCPs were concerned about DS safety and potential supplement-drug interactions, and cited several barriers to accurate EMR DS documentation including time constraints, database inconsistencies, and poor patient-HCP communication about DS. HCPs' views on our proposed mHealth app were generally positive. They expressed that our proposed mHealth app could streamline documentation processes and enhance patient-provider communication. HCPs expressed desire for a high-quality mHealth app that includes access to evidence-based DS information, integrates with the EMR, and does not increase time burdens. HCPs believe documentation of patients' DS use is important but not accurately captured in the EMR. Support was expressed for our proposed barcode-scanning DS mHealth app.

The Effect of Age on the Grouping of Open Clusters: II—Are There Old Binary Clusters?

In the present study, we continue testing the Primordial Group hypothesis (Casado 2022), which postulates that only sufficiently young open clusters can be binary or multiple, and old clusters are essentially single. To this end, we revisit all the remaining binary cluster candidates in the Galaxy having at least one cluster older than 100 Myr through Gaia data and careful revision of the literature. We found no convincing case for an old binary system among the 120 pairs/groups revised. Most of the pairs are optical pairs or flyby encounters. However, we found three dubious pairs that could falsify the title hypothesis upon further research. We also found two possible primordial pairs older than expected. Our results confirm that the vast majority of binary/multiple OCs in the Galaxy, if not all, are of primordial origin and are not stable for a long time. This finding is in line with similar studies of the Magellanic Clouds and theoretical N-body simulations in the Galaxy. The pairs of OCs in these groups are generally not binary systems since they are not gravitationally bound. We also point out some inconsistencies in previous works and databases, such as false open clusters and duplicities.

Exploring automatic inconsistency detection for literature-based gene ontology annotation.

MotivationLiterature-based gene ontology annotations (GOA) are biological database records that use controlled vocabulary to uniformly represent gene function information that is described in the primary literature. Assurance of the quality of GOA is crucial for supporting biological research. However, a range of different kinds of inconsistencies in between literature as evidence and annotated GO terms can be identified; these have not been systematically studied at record level. The existing manual-curation approach to GOA consistency assurance is inefficient and is unable to keep pace with the rate of updates to gene function knowledge. Automatic tools are therefore needed to assist with GOA consistency assurance. This article presents an exploration of different GOA inconsistencies and an early feasibility study of automatic inconsistency detection.ResultsWe have created a reliable synthetic dataset to simulate four realistic types of GOA inconsistency in biological databases. Three automatic approaches are proposed. They provide reasonable performance on the task of distinguishing the four types of inconsistency and are directly applicable to detect inconsistencies in real-world GOA database records. Major challenges resulting from such inconsistencies in the context of several specific application settings are reported. This is the first study to introduce automatic approaches that are designed to address the challenges in current GOA quality assurance workflows. The data underlying this article are available in Github at https://github.com/jiyuc/AutoGOAConsistency.

A Method for Expressing Integrity Constraints in Database Conceptual Modeling

Traditional methods lack the necessary or appropriate means for expressing the integrity constraints during the database conceptual modeling stage. At most, integrity constraints are informally documented and then, coded in the application. This leads to late error detection and database inconsistencies due to the incapacity of the domain expert to validate the program code. Thus, it is necessary to express such constraints in a natural and formal way in order to close the gap between modelers and domain experts, and to support the transformation to other languages and models. As a result, we propose a controlled natural language based on Semantics of Business Vocabulary and Business Rules (SBVR) to help modelers and domain experts in the process of writing and validating the constraints that cannot be represented in an Entity-Relationship schema; and the Alloy language to allow a formal specification. Inaddition, all the correspondences between the models and languages are described in order to consistently express the constraints and to lay the foundations of the automatic transformation. Finally, a case study and a usability survey show that the proposal is feasible, without abandoning a traditional and popular approach such as the Entity-Relationship model.

A Method for Expressing Integrity Constraints in Database Conceptual Modeling

Traditional methods lack the necessary or appropriate means for expressing the integrity constraints during the database conceptual modeling stage. At most, integrity constraints are informally documented and then, coded in the application. This leads to late error detection and database inconsistencies due to the incapacity of the domain expert to validate the program code. Thus, it is necessary to express such constraints in a natural and formal way in order to close the gap between modelers and domain experts, and to support the transformation to other languages and models. As a result, we propose a controlled natural language based on Semantics of Business Vocabulary and Business Rules (SBVR) to help modelers and domain experts in the process of writing and validating the constraints that cannot be represented in an Entity-Relationship schema; and the Alloy language to allow a formal specification. Inaddition, all the correspondences between the models and languages are described in order to consistently express the constraints and to lay the foundations of the automatic transformation. Finally, a case study and a usability survey show that the proposal is feasible, without abandoning a traditional and popular approach such as the Entity-Relationship model.

Westiellopsis ramosa sp. nov., intensely branched species of Westiellopsis (cyanobacteria) from a freshwater habitat of Jabalpur, Madhya Pradesh, India

An axenic culture of a cyanobacterium (strain HPS) was raised from a field specimen of greenish planktonic floccose mass from a local lake, Ganga Sagar. Intense morphological examination and comparative morphological assessment with the genera Fischerella and Hapalosiphon and all the known strains of the genus Westiellopsis indicated that the strain HPS differed in morphology with the closely related strains in trichome arrangement, size of vegetative cells, heterocytes, monocytes, and number of rows of main filaments. There were also differences in the habitat preference, being aquatic rather than terrestrial or subaerial. Intense ecological characterization of the habitat was performed through measurements of important physicochemical characteristics. 16S rRNA gene-based identification and phylogenetic placement indicated conclusively that the strain HPS was different from the most closely related strain Westiellopsis prolifica SAG 16.93. Phylogenetic inferences drawn in between all the branched heterocytous forms and subsequent 16S-23S ITS analyses and folding of the secondary structures revealed an entirely new form that is unknown till now. Subsequent nifD and rbcL gene-based phylogenetic assessments indicated that strain HPS is phylogenetically different from all the other previously known species of true branching cyanobacteria, along with also pointing toward the huge database inconsistencies in case of true branched cyanobacteria. Assessment of morphological and ecological differences along with comprehensive phylogenetic evaluation indicated that the strain HPS is a new species of the genus Westiellopsis and the name being proposed is Westiellopsis ramosa sp. nov.

Crash data quality for road safety research: Current state and future directions

Crash databases are one of the primary data sources for road safety research. Therefore, their quality is fundamental for the accuracy of crash analyses and, consequently the design of effective countermeasures. Although crash data often suffer from correctness and completeness issues, these are rarely discussed or addressed in crash analyses. Crash reports aim to answer the five “W” questions (i.e. When?, Where?, What?, Who? and Why?) of each crash by including a range of attributes. This paper reviews current literature on the state of crash data quality for each of these questions separately. The most serious data quality issues appear to be: inaccuracies in crash location and time, difficulties in data linkage (e.g. with traffic data) due to inconsistencies in databases, severity misclassification, inaccuracies and incompleteness of involved users’ demographics and inaccurate identification of crash contributory factors. It is shown that the extent and the severity of data quality issues are not equal between attributes and the level of impact in road safety analyses is not yet entirely known. This paper highlights areas that require further research and provides some suggestions for the development of intelligent crash reporting systems.

Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case

Abstract. The scientific initiative Prediction in Ungauged Basins (PUB) (2003–2012 by the IAHS) put considerable effort into improving the reliability of hydrological models to predict flow response in ungauged rivers. PUB's collective experience advanced hydrologic science and defined guidelines to make predictions in catchments without observed runoff data. At present, there is a raised interest in applying catchment models to large domains and large data samples in a multi-basin manner, to explore emerging spatial patterns or learn from comparative hydrology. However, such modelling involves additional sources of uncertainties caused by the inconsistency between input data sets, i.e. particularly regional and global databases. This may lead to inaccurate model parameterisation and erroneous process understanding. In order to bridge the gap between the best practices for flow predictions in single catchments and multi-basins at the large scale, we present a further developed and slightly modified version of the recommended best practices for PUB by Takeuchi et al. (2013). By using examples from a recent HYPE (Hydrological Predictions for the Environment) hydrological model set-up across 6000 subbasins for the Indian subcontinent, named India-HYPE v1.0, we explore the PUB recommendations, identify challenges and recommend ways to overcome them. We describe the work process related to (a) errors and inconsistencies in global databases, unknown human impacts, and poor data quality; (b) robust approaches to identify model parameters using a stepwise calibration approach, remote sensing data, expert knowledge, and catchment similarities; and (c) evaluation based on flow signatures and performance metrics, using both multiple criteria and multiple variables, and independent gauges for "blind tests". The results show that despite the strong physiographical gradient over the subcontinent, a single model can describe the spatial variability in dominant hydrological processes at the catchment scale. In addition, spatial model deficiencies are used to identify potential improvements of the model concept. Eventually, through simultaneous calibration using numerous gauges, the median Kling–Gupta efficiency for river flow increased from 0.14 to 0.64. We finally demonstrate the potential of multi-basin modelling for comparative hydrology using PUB, by grouping the 6000 subbasins based on similarities in flow signatures to gain insights into the spatial patterns of flow generating processes at the large scale.

QOCO

As key decisions are often made based on information contained in a database, it is important for the database to be as complete and correct as possible. For this reason, many data cleaning tools have been developed to automatically resolve inconsistencies in databases. However, data cleaning tools provide only best-effort results and usually cannot eradicate all errors that may exist in a database. Even more importantly, existing data cleaning tools do not typically address the problem of determining what information is missing from a database. To tackle these problems, we present QOCO, a novel query oriented cleaning system that leverages materialized views that are defined by user queries as a trigger for identifying the remaining incorrect/missing information. Given a user query, QOCO interacts with domain experts (which we model as oracle crowds) to identify potentially wrong or missing answers in the result of the user query, as well as determine and correct the wrong data that is the cause for the error(s). We will demonstrate QOCO over a World Cup Games database, and illustrate the interaction between QOCO and the oracles. Our demo audience will play the role of oracles, and we show how QOCO's underlying operations and optimization mechanisms can effectively prune the search space and minimize the number of questions that need to be posed to accelerate the cleaning process.

On the Data Complexity of Consistent Query Answering

The framework of database repairs is a principled approach to managing inconsistency in databases. In particular, the consistent answers of a query on an inconsistent database provide sound semantics and the guarantee that the values obtained are those returned by the query on every repair of the given inconsistent database. In this paper, we carry out a systematic investigation of the data complexity of the consistent answers of conjunctive queries for set-based repairs and with respect to classes of constraints that, in recent years, have been extensively studied in the context of data exchange and data integration. Our results, which range from polynomial-time computability to undecidability, complement or improve on earlier work, and provide a fairly comprehensive picture of the data complexity of consistent query answering. We also address the problem of finding a "representative" or "useful" repair of an inconsistent database. To this effect, we introduce the notion of a universal repair, as well as relaxations of it, and then apply it to the investigation of the data complexity of consistent query answering.

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models.

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

Database inconsistency errors correction, on example of LPIS databases in Poland

Topological correctness of objects is a problem that needs solving in every project related to the development and updating of spatial databases. This problem can be solved using contemporary GIS software. However, the GIS systems, while working on large volumes of data, tend to be inefficient in editing separated elementary areas (parcels, non-eligible areas) which are presented in the form of objects. An alternative to GIS systems can be the development of a CAD software based technology. The article presents the technology of detection and automatic elimination of topological errors related to a lack of consistency in the official Land Parcel Identification System databases. The developed methodology operates on virtual objects in a CAD software environment. It can be freely used on outdated databases, which contain very large numbers of objects. This technology was used on a test object, made available by a Government Agency. The developed methodology allowed for an automatic correction of errors that result from the inconsistency of the two databases, a non-eligible areas database containing information on the land use, and the reference database (cadastral database). This methodology is a part of the technology for improving the quality and performance of LPIS database systems control.

Genomic and proteomic data integration for comprehensive biodata search

Genomic and proteomic data integration for comprehensive biodata search