Original Data Sources Research Articles

Making Better Use of Population Health Data for Community Health Needs Assessments.

Research Objective:Non-profit hospitals are required to work with community organizations to prepare a Community Health Needs Assessment (CHNA) and implementation strategy (IS). In concert with the health care delivery system’s transformation from volume to value and efforts to enhance multi-sector collaboration, such community health improvement (CHI) processes have the potential to bridge efforts of the health care delivery sector, public health agencies, and community organizations to improve population health. Having a shared measurement system is critical to achieving collective impact, yet despite the availability of community-level data from a variety of sources, many CHI processes lack clear, measurable objectives and evaluation plans. Through an in-depth analysis of ten exemplary CHI processes, we sought to identify best practices for population health measurement with a focus on measures for needs assessments and priority setting.Study Design:Based on a review of the scientific literature, professional publications and presentations, and nominations from a national advisory panel, we identified 10 exemplary CHI processes. Criteria of choice were whether (1) the CHIs articulate a clear definition of intended outcomes; (2) clear, focused, measurable objectives and expected outcomes, including health equity; (3) expected outcomes are realistic and addressed with specific action plans; and (4) whether the plans and their associated performance measures become fully integrated into agencies and become a way of being for the agencies. We then conducted an in-depth analysis of CHNA, IS, and related documents created by health departments and leading hospitals in each process.Population Studied:U.S. hospitals.Principal Findings:Census, American Community Survey, and similar data are available for smaller areas are used to describe the populations covered, and, to a lesser extent, to identify health issues where there are disparities and inequities.Common data sources for population health profiles, including risk factors and population health outcomes, are vital statistics, survey data including BRFSS, infectious disease surveillance data, hospital & ED data, and registries. These data are typically available only at the county level, and only occasionally are broken down by race, ethnicity, age, poverty.There is more variability in format and content of ISs than CHNAs; the most developed models include population-level goals/objectives and strategies with clear accountability and metrics. Other hospital IS’s are less developed.Conclusions:The county is the unit of choice because most population health profile data are not available for sub-county areas, but when a hospital serves a population more broadly or narrowly defined, appropriate data are not available to set priorities or monitor progress.Measure definitions are taken from the original data sources, so comparisons across measures is difficult. Thus, although CHNAs cover many of the same topics, the measures used vary markedly. Using the same community health profile, e.g. County Health Rankings, would simplify benchmarking and trend analysis.Implications for Policy or Practice: It is important to develop population health data that can be disaggregated to the appropriate geographical level and to groups defined by race and ethnicity, socioeconomic status, and other factors associated with health outcomes.

A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets

Huge amounts of data generated on social media during emergency situations is regarded as a trove of critical information. The use of supervised machine learning techniques in the early stages of a crisis is challenged by the lack of labeled data for that event. Furthermore, supervised models trained on labeled data from a prior crisis may not produce accurate results, due to inherent crisis variations. To address these challenges, the authors propose a hybrid feature-instance-parameter adaptation approach based on matrix factorization, k-nearest neighbors, and self-training. The proposed feature-instance adaptation selects a subset of the source crisis data that is representative for the target crisis data. The selected labeled source data, together with unlabeled target data, are used to learn self-training domain adaptation classifiers for the target crisis. Experimental results have shown that overall the hybrid domain adaptation classifiers perform better than the supervised classifiers learned from the original source data.

SEINet: A Centralized Specimen Resource Managed by a Distributed Network of Researchers

The SEINet Portal Network has a complex social and development history spanning nearly two decades. Initially established as a basic online search engine for a select handful of biological collections curated within the southwestern United States, SEINet has since matured into a biodiversity data network incorporating more than 330 institutions and 1,900 individual data contributors. Participating institutions manage and publish over 14 million specimen records, 215,000 observations, and 8 million images. Approximately 70% of the collections make use of the data portal as their primary "live" specimen management platform. The SEINet interface now supports 13 regional data portals distributed across the United States and northern Mexico (http://symbiota.org/docs/seinet/). Through many collaborative efforts, it has matured into a tool for biodiversity data exploration, which includes species inventories, interactive identification keys, specimen and field images, taxonomic information, species distribution maps, and taxonomic descriptions. SEINet’s initial developmental goals were to construct a read-only interface that integrated specimen records harvested from a handful of distributed natural history databases. Intermittent network conductivity and inconsistent data exchange protocols frequently restricted data persistence. National funding opportunities supported a complete redesign towards the development of a centralized data cache model with periodic "snapshot" updates from original data sources. A service-based management infrastructure was integrated into the interface to mobilize small- to medium-sized collections (<1 million specimen records) that commonly lack consistent infrastructure and technical expertise to maintain a standard compliant specimen database. These developments were the precursors to the Symbiota software project (Gries et al. 2014). Through further development of Symbiota, SEINet transformed into a robust specimen management system specifically geared toward specimen digitization with features including data entry from label images, harvesting data from specimen duplicates, batch georeferencing, data validation and cleaning, generating progress reports, and additional tools to improve the efficiency of the digitization process. The central developmental paradigm focused on data mobilization through the production of: a versatile import module capable of ingesting a diverse range of data structures, a robust toolkit to assist in digitizing and managing specimen data and images, and a Darwin Core Archive (DwC-A) compliant data publishing and export toolkit to facilitate data distribution to global aggregators such as Global Biodiversity Information Facility (GBIF) and iDigBio. a versatile import module capable of ingesting a diverse range of data structures, a robust toolkit to assist in digitizing and managing specimen data and images, and a Darwin Core Archive (DwC-A) compliant data publishing and export toolkit to facilitate data distribution to global aggregators such as Global Biodiversity Information Facility (GBIF) and iDigBio. User interfaces consist of a decentralized network of regional data portals, all connecting to a centralized shared data source. Each of the 13 data portals are configured to present a regional perspective specifically tailored to represent the needs of the local research community. This infrastructure has supported the formation of regional consortia, who provide network support to aid local institutions in digitizing and publishing their collections within the network. The community-based infrastructure creates a sense of ownership – perhaps even good-natured competition – by the data providers and provides extra incentive to improve data quality and expand the network. Certain areas of development remain challenging in spite of the project's overall success. For instance, data managers continuously struggle to maintain a current local taxonomic thesaurus used for name validation, data cleaning, and to resolve taxonomic discrepancies commonly encountered when integrating collection datasets. We will discuss the successes and challenges associated with the long-term sustainability model and explore potential future paths for SEINet that support the long-term goal of maintaining a data provider that is in full compliance with the FAIR use principles of making the datasets findable, accessible, interoperable, and reusable (Wilkinson et al. 2016).

Data Migration from One Database to Another: Nervous breakdown of a database manager!

Migrating from one database to another is always accompanied by challenges. Recently Meise Botanic Garden has migrated its Living Collections data from LivCol, a bespoke database, to BG-BASE, a commercial product. Differences in database structure, degree of atomisation and field definition increase the complexity of such a transfer of data. The greater the number of fields used in the original data source, the greater the number of problems there are to resolve. Living collections are often centered around the 'accession information' of the living material, but the way one does this and the philosophy behind this may differ. The different approach to accessioning material in LivCol and BG-BASE affected the structure of the data model in each program. The LivCol approach was not as strictly defined as the BG-BASE approach e.g. new generations derived from existing accessions in LivCol retained the same accession number despite being not genetically identical (of seed origin), whereas in BG-BASE a new accession number would be generated with reference to the parent accession. In the data transfer LivCol accession number where grouped by accession number and garden location, and the inter-generation information combined in a single record in BG-BASE (this is not ideally in accordance with the BG-BASE concept, the alternative was to create 'artificial' new accession numbers but this would have complicated matters more both from a data and management point of view). The use of standards would greatly improve data transfer, and indeed many standards have been adopted by both the above mentioned databases. However, it soon became evident that there are multiple standards for a single topic e.g. for information concerning conservation status: NatureServe Global Conservation Status Ranks; Fish & Wildlife conservation category; International Union for Conservation of Nature (IUCN) - old and new codes (plus version); Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES); etc. Also, some values used are region specific and do not translate well to ‘global’ standards e.g. in Belgium there are three principal regions controlling conservation status (Brussels, Flanders, Wallonia) and they differ in their approach and definitions, all these need to be taken into account as there are legal implications - this was done by finding close matches in IUCN (New) codes and combining them with 'non-standard' World Geographical Scheme for Recording Plant Distributions (WGSRPD). The latter TDWG standard is out of date and in many circumstances not sufficiently atomised to be of practical use. There were also certain fields that would benefit from having standards, but are at present absent e.g. invasiveness - BG-BASE uses Cronk and Fuller (1995) whereas LivCol uses AlterIAS (http://www.alterias.be/), Belgian Forum on Invasive Species (http://ias.biodiversity.be) and Lambinon et al. (1992). The above problems will be discussed and their impact on mapping the data. Decisions had to be made with respect to ‘best fit’ solutions. The latter lead to the loss of information or a slight variation in its interpretation, examples will be given to highlight these aspects. As the structures of the databases differed, sometimes assumptions had to be made, this too will be illustrated. These changes were reasonable, but represent an interpretation of the original data and therefore not strictly the same. Databases are crucial to the management of Living Collections and the research done on them. This talk will look at the lessons learned during the data transfer and the problems associated with mapping (decisions, assumptions and standards). Databases are 'living' entities that need to grow, adapt, be maintained and regularly updated to new developments in technology. Databases are not seen as new or innovative by funding bodies and are often left to struggle along in suboptimal conditions. If we want data quality to improve and increase interoperability between systems, maybe we should start at the point where data is entered.

Mission: Implausible — Revealing rogue marine species in records across biodiversity data platforms

Online biodiversity platforms publish datasets with graphic tools to help with quality control of submitted records, but more could be done to make the data robust for ecological analyses. Attention has focused mostly on automating tools for obvious errors, including misspelled names and synonyms, dates, or coordinates. However, a manual review of species identifications and distributions may uncover improbable records, such as a species reported in an area far from its usual range, or a rare species found in an area that has many more records of a related species. Examples are shown by constructing checklists in the Northwest Atlantic, using information from the World Register of Marine Species (WoRMS, http://www.marinespecies.org) and the Ocean Biogeographic Information System (OBIS, https://obis.org). Reviewing rare species records revealed some misidentifications, but in other instances, the rare species was valid while it was the commonly reported species that needed correction. Confirmations were obtained by comparing records from different regions, but also across platforms, including photos from observers on iNaturalist Canada (https://inaturalist.ca), genetic analyses on Barcode of Life Data systems (BOLD, http://www.boldsystems.org), and literature in the Biodiversity Heritage Library (BHL, https://www.biodiversitylibrary.org). While this exercise succeeded in validating the marine taxa of a region, it is an obvious candidate for automation in three areas: 1) flagging records of improbable taxa in a region, 2) comparing records with different types of information (e.g., specimen photos, genetic groupings, or literature records), and 3) updating users and providers when records get flagged as unusual or are modified. The first approach could be explored using online graphics tools or R software packages (rOpenSci, https://ropensci.org). The second toolset, comparing records across platforms, is partially realized with some linkages already operating between WoRMS, OBIS, BOLD, BHL, iNaturalist, and the Global Biodiversity Information Facility (GBIF, https://www.gbif.org). The third target will be the most difficult to implement, requiring reliable platform cross-linkages and specimen record identifiers to send notifications of changed status of records to both users and the original data source. Ongoing work is discussed on communicating the need to review records across platforms, with the hope that toolsets will be developed to make this task easier.

The Global Caryophyllales Initiative: Towards an updated taxonomic backbone and a dynamic monograph of a major plant group

The ongoing paradigm shift in taxonomy from individual contributions to a truly collaborative and forward-looking endeavour results in a number of challenges related to distributed data management. Examination of physical specimens remains a key task, but searching for specimen data, literature, and name information is now mostly done online. In the past, these research steps involved many physical visits to collections and libraries. Although these infrastructures were and are still freely accessible and supportive for research carried out by individuals, the amount of characters, specimens, and the complexity of current analytical approaches limit what can be achieved by individual workers. Monographing is challenged because: larger genera remain unstudied and become fragmented in regional treatments; long-term availability of detailed (unpublished) primary research data is often not addressed; cross-disciplinary interoperability and open data principles are needed; and highly specialised techniques used in phylogenetic and genomic analyses require teamwork by specialists. larger genera remain unstudied and become fragmented in regional treatments; long-term availability of detailed (unpublished) primary research data is often not addressed; cross-disciplinary interoperability and open data principles are needed; and highly specialised techniques used in phylogenetic and genomic analyses require teamwork by specialists. The process towards generating truly community-based integrative dynamic taxonomic treatments is ongoing. In botany, specialist communities and networks have formed for certain plant groups, for example in the families Solanaceae, Brassicaceae, Fabaceae, Poaceae, Melastomataceae-Miconieae, Asteraceae-Cichorieae, and in the order Caryophyllales. Their common aim is to create sustainable information systems according to the FAIR principles, making the information Findable, Accessible, Interoperable and Reusable. At the same time, the information system is meant to support and document ongoing taxonomic research as an iterative process with tracking of changes, and backlinks to original data sources. This represents a big step forward with respect to the efficiency of the entire field of taxonomy. The Global Caryophyllales Initiative aims at creating a global synthesis of species diversity in this group (Borsch et al. 2015) encompassing about 12,500 species in more than 30 families or about 6% of flowering plants (Fig. 1). Caryophyllales include ecologically diverse, economically important, invasive, and threatened species. The Caryophyllales Network was initiated in 2011 and unites specialists from across the world (to date, 150 scientists from 37 countries). The network is recognized by the World Flora Online (WFO) Council as its Taxonomic Expert Network for Caryophyllales. Advances by the network are presented in the open-access Caryophyllales Portal (http://caryophyllales.org/), aiming to provide up-to-date phylogenetic and taxonomic information. The systematic treatment is powered by the EDIT Platform for Cybertaxonomy (https://cybertaxonomy.eu/), with the generic checklist (http://caryophyllales.org/Checklist) and some family treatments already publicly available (e.g. Cactaceae). The checklist is regularly updated in consultation with family editors. A species-level taxonomic backbone incorporating all names and pertinent nomenclatural acts and evaluations is being compiled. A compilation of the Nepenthaceae (Berendsohn et al. 2018) served as a case study for the accommodation of descriptive and other factual data, and for demonstrating the feasibility of the workflow contributing to the WFO initiative. The taxonomic treatment of Iresine (Borsch et al. 2018) is another example. These studies are fundamental in establishing a general workflow for collaborative online monographs. Several challenges remain, inter alia the genomics perspective in biodiversity informatics, proper attribution and unique identification of taxonomic concepts, review and impact assessment of individual contributions, possibilities to simultaneously display contrasting taxonomical concepts and classifications, and engaging both the wider taxonomic community and the public. The ongoing implementation shows that a dynamic online monograph requires rethinking of editorial workflows. Based on experiences with the Caryophyllales network, the taxonomic and biodiversity informatics communities are ready to meet this challenge.

This Time Is Different, but It Will End the Same Way: Unrecognized Secular Changes in the Bond Market since the 2008 Crisis That May Precipitate the Next Crisis

The US bond market had over $42.39 trillion of outstanding debt at the end of the third quarter of 2018, eclipsing the US stock market’s approximately $30 trillion in market capitalization. The sheer size of the bond market provides ample opportunities, as well as risks, for institutional investors. Some of these risks escape investors’ radar because of the nature of fixed-income securities: low transparency, illiquidity, and over-the-counter (OTC) trading. In this paper, we present our concerns regarding five secular changes brought up by the over-regulation of the marketplace after the financial crisis of 2008 and investors’ persistent thirst for yield. Further, while painful lessons were gleaned after the punishing 2008 financial crisis, we present empirical evidence that suggests that many sectors, such as the auto loans and collateralized loan obligations, that were largely unscathed by this crisis may be at risk in the next downturn. This paper is based on original data sources and academic research. The authors are in continuing dialogue with other experts that may further the research, and welcome interested parties to get in contact.

Agro-Climatic Data by County: A Spatially and Temporally Consistent U.S. Dataset for Agricultural Yields, Weather and Soils

Agro-climatic data by county (ACDC) is designed to provide the major agro-climatic variables from publicly available spatial data sources to diverse end-users. ACDC provides USDA NASS annual (1981–2015) crop yields for corn, soybeans, upland cotton and winter wheat by county. Customizable growing degree days for 1 °C intervals between −60 °C and +60 °C, and total precipitation for two different crop growing seasons from the PRISM weather data are included. Soil characteristic data from USDA-NRCS gSSURGO are also provided for each county in the 48 contiguous US states. All weather and soil data are processed to include only data for land being used for non-forestry agricultural uses based on the USGS NLCD land cover/land use data. This paper explains the numerical and geo-computational methods and data generating processes employed to create ACDC from the original data sources. Essential considerations for data management and use are discussed, including the use of the agricultural mask, spatial aggregation and disaggregation, and the computational requirements for working with the raw data sources.

Targeting key angiogenic pathways with a bispecific CrossMAb optimized for neovascular eye diseases.

Targeting key angiogenic pathways with a bispecific CrossMAb optimized for neovascular eye diseases.

The Effect of Evidence Transfer on Latent Feature Relevance for Clustering

Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer’s application on clustering is designed to be robust when introduced with a low quality of evidence, while increasing the effectiveness of the clustering accuracy during relevant corresponding evidence. We interpret the effects of evidence transfer on the latent representation of an autoencoder by comparing our method to the information bottleneck method. Information bottleneck is an optimisation problem of finding the best tradeoff between maximising the mutual information of data representations and a task outcome while at the same time being effective in compressing the original data source. We posit that the evidence transfer method has essentially the same objective regarding the latent representations produced by an autoencoder. We verify our hypothesis using information theoretic metrics from feature selection in order to perform an empirical analysis over the information that is carried through the bottleneck of the latent space. We use the relevance metric to compare the overall mutual information between the latent representations and the ground truth labels before and after their incremental manipulation, as well as, to study the effects of evidence transfer regarding the significance of each latent feature.

Fusion of Multi-Sensor-Derived Heights and OSM-Derived Building Footprints for Urban 3D Reconstruction

So-called prismatic 3D building models, following the level-of-detail (LOD) 1 of the OGC City Geography Markup Language (CityGML) standard, are usually generated automatically by combining building footprints with height values. Typically, high-resolution digital elevation models (DEMs) or dense LiDAR point clouds are used to generate these building models. However, high-resolution LiDAR data are usually not available with extensive coverage, whereas globally available DEM data are often not detailed and accurate enough to provide sufficient input to the modeling of individual buildings. Therefore, this paper investigates the possibility of generating LOD1 building models from both volunteered geographic information (VGI) in the form of OpenStreetMap data and remote sensing-derived geodata improved by multi-sensor and multi-modal DEM fusion techniques or produced by synthetic aperture radar (SAR)-optical stereogrammetry. The results of this study show several things: First, it can be seen that the height information resulting from data fusion is of higher quality than the original data sources. Secondly, the study confirms that simple, prismatic building models can be reconstructed by combining OpenStreetMap building footprints and easily accessible, remote sensing-derived geodata, indicating the potential of application on extensive areas. The building models were created under the assumption of flat terrain at a constant height, which is valid in the selected study area.

Quantum Rate-Distortion Coding of Relevant Information

Rate-distortion theory provides bounds for compressing data produced by an information source to a specified encoding rate that is strictly less than the source's entropy. This necessarily entails some loss, or distortion, between the original source data and the best approximation after decompression. The so-called Information Bottleneck Method is designed to compress only `relevant' information. Which information is relevant is determined by the correlation between the data being compressed and a variable of interest, so-called side information. In this paper, an Information Bottleneck Method is introduced for the compression of quantum data. The channel communication picture is used for compression and decompression. The rate of compression is derived using an entanglement assisted protocol with classical communication, and under an unproved conjecture that the rate function is convex in the distortion parameter. The optimum channel achieving this rate for a given input state is characterised. The conceptual difficulties arising due to differences in the mathematical formalism between quantum and classical probability theory are discussed and solutions are presented.

A sustainable process and toolbox for geographical linked data generation and publication: a case study with BTN100

We describe the process and tools that we have used to generate and publish the BTN100 Linked Dataset, based on the original data from the Spanish Topographic Base (1:100.000 scale) from the Spanish Instituto Geográfico Nacional. We have taken into account the limitations and lessons learned from our initial experience on the generation and publication of Linked Data from a range of geographical sources in Spain, in 2010, and we have now refined the process in order to facilitate: declarative mappings for the transformations from existing open data (shapefiles), automation of transformations whenever there are changes in the original data sources, version control, and alignment with INSPIRE URIs. As a result of this transformation and publication process we have also updated the reference ontology for geographical features and aligned with general ontologies such as GeoSPARQL.

Fusion of Visible and Infrared Images Based on IHS Transformation and Regional Variance Matching Degree

It is usually necessary to identify and extract specific characteristics by deriving an informative fused image from multiple images. An effective fusion algorithm was proposed by combining the intensity-hue-saturation (IHS) transformation and the regional variance matching degree (RVMD) in our study. Visible and thermal infrared images of wheat were used as the original data sources. After finishing the IHS transformation, a fusion rule was designed to produce the new component I. More specifically, the high frequency fusion rule was generated by the RVMD with a threshold of 0.5 and a 3 × 3 moving window, and the weighted average was used as the low frequency fusion rule. Experimental results show that the proposed algorithm can avoid producing color distortion in comparison with the IHS transformation, and additionally, it can also enhance the edge contrast and produce more obvious texture resolution. In addition, three quantitative indicators including entropy, standard deviation and average gradient were used to validate the proposed algorithm. The analysis results show that the values of three indicators are respectively 7.82, 63.93, 10.06, which are better than the results derived from the IHS transformation and regional variance fusion.

Analgesic efficacy and impact of caudal block on surgical complications of hypospadias repair: a systematic review and meta-analysis

Background and objectivesis commonly used for children undergoing hypospadias repair. However, the safety of caudal block for hypospadias repair in children is controversial in terms of surgical complications such as...

APID database: redefining protein-protein interaction experimental evidences and binary interactomes.

The collection and integration of all the known protein–protein physical interactions within a proteome framework are critical to allow proper exploration of the protein interaction networks that drive biological processes in cells at molecular level. APID Interactomes is a public resource of biological data (http://apid.dep.usal.es) that provides a comprehensive and curated collection of `protein interactomes’ for more than 1100 organisms, including 30 species with more than 500 interactions, derived from the integration of experimentally detected protein-to-protein physical interactions (PPIs). We have performed an update of APID database including a redefinition of several key properties of the PPIs to provide a more precise data integration and to avoid false duplicated records. This includes the unification of all the PPIs from five primary databases of molecular interactions (BioGRID, DIP, HPRD, IntAct and MINT), plus the information from two original systematic sources of human data and from experimentally resolved 3D structures (i.e. PDBs, Protein Data Bank files, where more than two distinct proteins have been identified). Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions (following the `ontology and controlled vocabulary’: www.ebi.ac.uk/ols/ontologies/mi; developed by `HUPO PSI-MI’). Within this data mining framework, all interaction detection methods have been grouped into two main types: (i) `binary’ physical direct detection methods and (ii) `indirect’ methods. As a result of these redefinitions, APID provides unified protein interactomes including the specific `experimental evidences’ that support each PPI, indicating whether the interactions can be considered `binary’ (i.e. supported by at least one binary detection method) or not.

The Analysis of the Influencing Factors on the Problems of Bike-Sharing System in China

Bike-sharing has experienced rapid growth in China since 2016. Notwithstanding the fast expansion, or possibly because it grew too fast, some bike-sharing companies have experienced setbacks and failure in 2018. This paper aims to close the gap due to the insufficient analysis on the influencing factors of the bike-sharing system problems in China. The study first used text mining of bikes-sharing related Chinese news reports and social web discussion boards. Subsequently, the study used association rule mining to explore the relationship between keywords generated by text mining of the original data sources. The results of news reports mined keywords show that problems with shared bikes deposits were closely related to the complaints of customers. In the context of social web discussion boards, the keywords relations implies that the users concern about the possible collapse of the bike-sharing companies and related management and economy issues. The information existed before the bike-sharing companies financial failure news took place. Our results also show that the association rule mining relationship of major keywords in news reports and social media can be an early warning sign of the financial failure of sharing bikes companies.

The Global Naturalized Alien Flora (GloNAF) database.

This dataset provides the Global Naturalized Alien Flora (GloNAF) database, version 1.2. GloNAF represents a data compendium on the occurrence and identity of naturalized alien vascular plant taxa across geographic regions (e.g. countries, states, provinces, districts, islands) around the globe. The dataset includes 13,939 taxa and covers 1,029 regions (including 381 islands). The dataset is based on 210 data sources. For each taxon-by-region combination, we provide information on whether the taxon is considered to be naturalized in the specific region (i.e. has established self-sustaining populations in the wild). Non-native taxa are marked as "alien", when it is not clear whether they are naturalized. To facilitate alignment with other plant databases, we provide for each taxon the name as given in the original data source and the standardized taxon and family names used by The Plant List Version 1.1 (http://www.theplantlist.org/). We provide an ESRI shapefile including polygons for each region and information on whether it is an island or a mainland region, the country and the Taxonomic Databases Working Group (TDWG) regions it is part of (TDWG levels 1-4). We also provide several variables that can be used to filter the data according to quality and completeness of alien taxon lists, which vary among the combinations of regions and data sources. A previous version of the GloNAF dataset (version 1.1) has already been used in several studies on, for example, historical spatial flows of taxa between continents and geographical patterns and determinants of naturalization across different taxonomic groups. We intend the updated and expanded GloNAF version presented here to be a global resource useful for studying plant invasions and changes in biodiversity from regional to global scales. We release these data into the public domain under a Creative Commons Zero license waiver (https://creativecommons.org/share-your-work/public-domain/cc0/). When you use the data in your publication, we request that you cite this data paper. If GloNAF is a major part of the data analyzed in your study, you should consider inviting the GloNAF core team (see Metadata S1: Originators in the Overall project description) as collaborators. If you plan to use the GloNAF dataset, we encourage you to contact the GloNAF core team to check whether there have been recent updates of the dataset, and whether similar analyses are already ongoing.

Corrigendum] Netrin‑1 induces the proliferation of gastric cancer cells via the ERK/MAPK signaling pathway and FAK activation.

During the preparation of the figures in the above article, the authors inadvertently selected images for the shCTL experiments portrayed in Fig.5I andJ (for the MGC803 and SGC7901cell lines, respectively) that were generated from the same original data source. A corrected version of Fig.5 is shown opposite, showing the correct data for the shCTL experiment performed in SGC7901cells (Fig.5J). This error did not affect the major conclusions reported in the paper. All the authors have agreed to this Corrigendum. The authors regret this error, and apologize for any confusion that it may have caused. [the original article was published in Oncol Rep40: 2325‑2333, 2018; DOI: 10.3892/or.2018.6614].

Generalization and multiscale structure of subsurface structural maps

Subsurface structural maps are stored as spatially referenced numeric grids. The spatial sampling density of these grids is a critical parameter in the mapping process because the sampling and aliasing that occurs when transforming from original data sources during the gridding process controls the information content and aesthetics of the final map. New results from upscaling experiments and sampling theory indicate that it is possible to specify gridding parameters that remove noise while retaining key geologic structure — an optimized generalization procedure. Furthermore, geologic structure may exist at multiple scales. Sampling theory can again be applied, in a multiscale curvature analysis, to yield structure at a range of scales via decomposition of a gridded surface. These products can be analyzed further for indications of short-wavelength, high-curvature features that may correspond to fault or fracture zones, and long-wavelength, prospect, and field-scale structure. These results combine to inform a discussion on sampling, smoothing, and geologic information, as well as provide a quantitative alternative to rules of thumb for grid sampling that balance signal and noise in standard mapping schemes.