Software Data Research Articles

I Know Something You Don’t Know: The annotation saga continues…

Over the past 20 years, the biodiversity informatics community has pursued components of the digital annotation landscape with varying degrees of success. We will provide an historical overview of the theory, the advancements made through a few key projects, and will identify some of the ongoing challenges and opportunities. The fundamental principles remain unchanged since annotations were first proposed. Someone (or something): (1) has an enhancement to make elsewhere from the source where original data or information are generated or transcribed; (2) wishes to broadcast these statements to the originator and to others who may benefit; and (3) expects persistence, discoverability, and attribution for their contributions alongside the source. The Filtered Push project (Morris et al. 2013) considered several use cases and pioneered development of services based on the technology of the day. The exchange of data between parties in a universally consistent way necessitated the development of a novel draft standard for data annotations via an extension of the World Wide Web Consortium’s Web Annotation Working Group standard (Sanderson et al. 2013) to be sufficiently informative for a data curator to confidently make a decision. Figure 2 from Morris et al. (2013), reproduced here as Fig. 1, outlines the composition of an annotation data package for a taxonomic identification. The package contains the data object(s) associated with an occurrence, an expression of the motivation(s) for updating, some evidence for an assertion, and a stated expectation for how the receiving entity should take action. The Filtered Push and Annosys (Tschöpe et al. 2013) projects also considered implementation strategies involving collection management systems (e.g., Symbiota) and portals (e.g., European Distributed Institute of Taxonomy, EDIT). However, there remain technological barriers for these systems to operate at scale, the least of which is the absence of globally unique, persistent, resolvable identifiers for shared objects and concepts. Major aggregation infrastructures like the Global Biodiversity Information Facility (GBIF) and the Distributed System of Scientific Collections (DiSSCo) rely on data enhancement to improve the quality of their resources and have annotation services in their work plans. More recently, the Digital Extended Specimen (DES) concept (Hardisty et al. 2022) will rely on annotation services as key components of the proposed infrastructure. Recent work on annotation services more generally has considered various new forms of packaging and delivery such as Frictionless Data (Fowler et al. 2018), Journal Article Tag Suite XML (Agosti et al. 2022), or nanopublications (Kuhn et al. 2018). There is risk in fragmentation of this landscape and disenfranchisement of both biological collections and the wider research community if we fail to align the purpose, content, and structure of these packages or if these fail to remain aligned with FAIR principles. Institutional collection management systems currently represent the canonical data store that provides data to researchers and data aggregators. It is critical that information and/or feedback about the data they release be round-tripped back to them for consideration. However, the sheer volume of annotations that could be generated by both human and machine curation processes will overwhelm local data curators and the systems supporting them. One solution to this is to create a central annotation store with write and discovery services that best support the needs of all stewards of data. This will require an international consortium of parties with a governance and technical model to assure its sustainability.

Svalbox Digital Model Database: A geoscientific window into the High Arctic

Abstract Digital outcrop models (DOMs) have revolutionized the way twenty-first century geoscientists work. DOMs are georeferenced three-dimensional (3-D) digital representations of outcrops that facilitate quantitative work on outcrops at various scales. Outcrop digitalization has been traditionally conducted using laser scanners, but in the past decade, it has seen an exponential growth because of efficient and consumer-friendly structure-from-motion (SfM) algorithms concurrent with the rapid development of cost-effective aerial drones with high-resolution onboard cameras. While DOMs are routinely used in geoscientific research, education, and industry, enhanced DOM usage is restricted because raw data (e.g., photographs) and metadata are often incomplete and/or unavailable. In this contribution, we present the Svalbox Digital Model Database (Svalbox DMDb), a database of metadata and openly available data packages for individual DOMs. The Svalbox DMDb is a regional DOM database geographically constrained to the Norwegian High Arctic archipelago of Svalbard at 74°N–81°N and 10°E–35°E. Svalbard offers exceptional-quality, vegetation-free outcrops with a wide range of lithologies and tectono-magmatic styles, including extension, compression, and magmatism. Data and metadata of the systematically digitalized outcrops across Svalbard are shared according to FAIR principles through the Svalbox DMDb. Fully open-access and downloadable DOMs include not just the DOMs themselves, but also the input data, processing reports and projects, and other data products such as footprints and orthomosaics. Rich metadata for each DOM include both the technical and geological parameters (metadata), enabling visualization and integration with regional geoscientific data available through the Norwegian Polar Institute and the Svalbox online portal. The current release of Svalbox DMDb, documented in this contribution, covers 135 DOMs cumulatively covering 114 km of Proterozoic to Cenozoic stratigraphy.

Harmonising, Harvesting, and Searching Metadata Across a Repository Federation

The collection of metadata for research data is an important aspect in the FAIR principles. The schema.org and Bioschemas initiatives created a vocabulary to embed markup for many different types, including BioChemEntity, ChemicalSubstance, Gene, MolecularEntity, Protein, and others relevant in the Natural and Life Sciences with immediate benefits for findability of data packages. To bridge the gap between the worlds of semantic-web-driven JSON+LD metadata on the one hand, and established but separately developed interface services in libraries, we have designed an architecture for harmonising, federating and harvesting metadata from several resources. Our approach is to serve JSON+LD embedded in an XML container through a central OAI-Provider. Several resources in NFDI4Chem provide such domain-specific metadata. The CKAN-based NFDI4Chem search service can harvest this metadata using an OAI-PMH harvester extension that can extract the XML-encapsulated JSON+LD metadata, and has search capabilities relevant in the chemistry domain. We invite the community to collaborate and reach a critical mass of providers and consumers in the NFDI.

An Australian Model of Cooperative Data Publishing to OBIS and GBIF

The Australian Commonwealth Science and Industrial Research Organisation (CSIRO) hosts both the Australian Ocean Biodiversity Information System (OBIS) and Global Biodiversity Information Facility (GBIF) nodes within the National Collections and Marine Infrastructure (NCMI) business unit. OBIS-AU is led by the NCMI Information and Data Centre and publishes marine biodiversity data in the Darwin Core (DwC) standard via an Integrated Publishing Toolkit (IPT), with over 450 marine datasets at present. The Australian GBIF node is hosted by a separate team at the Atlas of Living Australia (ALA), a national-scale biodiversity analytical and knowledge delivery portal. The ALA aggregates and publishes over 800 terrestrial and marine datasets from a wide variety of research institutes, museums and collections, governments and citizen science agencies, including OBIS-AU. Many OBIS-AU published datasets are harvested and republished by ALA and vice-versa. OBIS-AU identifies, performs Quality Control and formats marine biodiversity and observation data, then publishes directly to the OBIS international data repository and portal, using GBIF IPT technology. The ALA data processing pipeline harvests, aggregates and enhances datasets from many sources with authoritative taxonomic and spatial reference data before passing the data on to GBIF. OBIS-AU and ALA are working together to ensure that the publication pathways for any datasets managed by both (with potential for duplication of records and incomplete metadata harvests) are rationalised and that a single collaborative workflow across both units is followed for publication to GBIF. Recently, the data management groups have established an agreement to cooperatively publish marine data and eDNA data. OBIS-AU have commenced publishing datasets directly to GBIF with ALA endorsement. We present the convergent evolution of OBIS and GBIF data publishing in Australia, adaptive data workflows to maintain data and metadata integrity, challenges encountered, how domain expertise ensures data quality and the benefits of sharing data skills and code, especially in publishing eDNA data types in DwC (using the DNA-derived data extension) and exploring the new CamTrap Data Package using Frictionless data. We also present the work that both data groups are doing toward adopting the GBIF new Unified Data model for publishing data. This Australian case study demonstrates the strengths of collaborative data publishing and offers a model that minimises replication of data in global aggregators through the development of regional integrated data publishing pipelines.

RETRACTED ARTICLE: Identification and assessment of differentially expressed necroptosis long non-coding RNAs associated with periodontitis in human

BackgroundPeriodontitis is the most common oral disease and is closely related to immune infiltration in the periodontal microenvironment and its poor prognosis is related to the complex immune response. The progression of periodontitis is closely related to necroptosis, but there is still no systematic study of long non-coding RNA (lncRNA) associated with necroptosis for diagnosis and treatment of periodontitis.Material and methodsTranscriptome data and clinical data of periodontitis and healthy populations were obtained from the Gene Expression Omnibus (GEO) database, and necroptosis-related genes were obtained from previously published literature. FactoMineR package in R was used to perform principal component analysis (PCA) for obtaining the necroptosis-related lncRNAs. The core necroptosis-related lncRNAs were screened by the Linear Models for Microarray Data (limma) package in R, PCA principal component analysis and lasso algorithm. These lncRNAs were then used to construct a classifier for periodontitis with logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the model. The CIBERSORT method and ssGSEA algorithm were used to estimate the immune infiltration and immune pathway activation of periodontitis. Spearman’s correlation analysis was used to further verify the correlation between core genes and periodontitis immune microenvironment. The expression level of core genes in human periodontal ligament cells (hPDLCs) was detected by RT-qPCR.ResultsA total of 10 core necroptosis-related lncRNAs (10-lncRNAs) were identified, including EPB41L4A-AS1, FAM30A, LINC01004, MALAT1, MIAT, OSER1-DT, PCOLCE-AS1, RNF144A-AS1, CARMN, and LINC00582. The classifier for periodontitis was successfully constructed. The Area Under the Curve (AUC) was 0.952, which suggested that the model had good predictive performance. The correlation analysis of 10-lncRNAs and periodontitis immune microenvironment showed that 10-lncRNAs had an impact on the immune infiltration of periodontitis. Notably, the RT-qPCR results showed that the expression level of the 10-lncRNAs obtained was consistent with the chip analysis results.ConclusionsThe 10-lncRNAs identified from the GEO dataset had a significant impact on the immune infiltration of periodontitis and the classifier based on 10-lncRNAs had good detection efficiency for periodontitis, which provided a new target for diagnosis and treatment of periodontitis.

Building Software for Hierarchical Events in Biodiversity Informatics

In 2019, the Atlas of Living Australia (ALA) ran a national consultation, clarifying a long-held suspicion that while simple occurrence records provide invaluable discoverability and analysis for biodiversity data, the lack of contextual information on data collection methodology and protocols limits its usefulness for species abundance estimation and time-series analysis. The consultation recognised that the ALA has strong leadership in biodiversity standards and development, and that our 12-year history and investment in projects and engagement demonstrates a clear capacity to transition to a repository capable of capturing and aggregating the monitoring and survey data required for conservation efforts (Daly 2019). Around the same time, the larger data landscape was undergoing change in a similar direction, both internationally through the Global Biodiversity Information Facility’s (GBIF) Unified Model engagements, and nationally through the development of the Australian Biodiversity Information Standard (ABIS), an ontology for describing environmental data (Anonymous 2021). We embarked on a project to examine existing data standards and practices, extend our own occurrence model, and build software that could ingest event-based datasets and make them discoverable and interoperable. Initially we focused on well-structured surveys, both marine and terrestrial, to develop the system and user interface (UI). During the project, we restructured and modeled other exemplar datasets, collaborating with GBIF to develop event terms, vocabularies, and user interface components. Seeking interoperability with existing standards, we integrated concepts from both ABIS and the Ocean Biodiversity Information System’s (OBIS) ENV-DATA model (De Pooter et al. 2017) into a standardised yet flexible implementation of Event Core, navigable via a friendly user interface. The initial software release is comprised of an ingestion pipeline for events in parallel to occurrences, an index capable of handling nested data structures, and a user interface. The UI guides the user to explore and filter datasets; includes visualisations for data structures, taxonomic scope, repeat location surveys, extended measurements or facts; and links out to child occurrence records. Users can download filtered original and interpreted datasets with Digital Object Identifiers (DOI), in compressed files that comply simultaneously with Darwin Core Archive and Frictionless Data Package specifications. On release, we will present a range of datasets covering different event-based scenarios. The model has serendipitously provided the flexibility to encapsulate complex seed bank data. During the project, we developed a draft extension, which we used to service a new data portal for the Australian Seed Bank Partnership, a testament to the model’s serviceability for novel use cases. The ALA has taken innovative steps beyond simple collection of complex data types and worked with our local biodiversity informatics community to provide a navigable interface to this data. We intend to continue working with our own data providers and the international community, to realise the benefits of a more complex data model.

Building on the Functionalities of GBIF-COL ChecklistBank

ChecklistBank, co-developed by Catalogue of Life (COL) and the Global Biodiversity Information Facility (GBIF), provides a suite of functionalities for building taxonomic data solutions. In addition to serving as a publishing platform and open data repository for taxonomic and nomenclatural data, ChecklistBank includes tools for managing the assembly of new species lists from other datasets published to the platform. The ChecklistBank API provides access to all data sources, including the COL Checklist, all mediated through ChecklistBank. It also allows for (bulk) matching of names, and provides an overview of the name usage identifiers used by COL and the identifiers for the same taxon originating from other data sources. The ChecklistBank infrastructure is still in development, but is starting to allow for various kinds of usages and services, including quality assurance and quality control. Examples will be presented of possible functionalities and services that could be added to ChecklistBank in future, including: An option to push a ChecklistBank dataset to Github and then automate reimport from the same source (with appropriate validation). A Github representation, based upon the Catalogue of Life Data Package (ColDP) data model, could serve as a general resource against which developers can build new lightweight editing or batch update tools, including merge tools to bring in new names from ChecklistBank datasets, with Github providing versioning support and triggers for synchronisation with Github An option to establish similar bidirectional editing models with other environments such as TaxonWorks or Aphia, Notifications for checklist curators when previously unseen names and combinations for their group appear in new or updated ChecklistBank datasets Enhancements to the data model and user interface to support better quality/completeness indicators (traffic lights). An option to push a ChecklistBank dataset to Github and then automate reimport from the same source (with appropriate validation). A Github representation, based upon the Catalogue of Life Data Package (ColDP) data model, could serve as a general resource against which developers can build new lightweight editing or batch update tools, including merge tools to bring in new names from ChecklistBank datasets, with Github providing versioning support and triggers for synchronisation with Github An option to establish similar bidirectional editing models with other environments such as TaxonWorks or Aphia, Notifications for checklist curators when previously unseen names and combinations for their group appear in new or updated ChecklistBank datasets Enhancements to the data model and user interface to support better quality/completeness indicators (traffic lights).

Online learning: experiences of Namibian students relying on smartphones

ABSTRACT This study reports on the experiences of a group of university students relying on smartphones to access online learning during/post COVID-19. In understanding the effectiveness of smartphones Rogers Diffusion of Innovation theory was consulted. The study took place at a University in Namibia at the Faculty of Education. This study adhered to a mixed methodology approach favoured by the paradigm. One hundred students participated in the study recruited through convenience sampling method. The data was collected using a questionnaire containing questions for both qualitative and quantitative data. The study employed content analysis techniques to analyse the qualitative data and statistical software to analyse the quantitative data. The findings present the following factors: opportunities, restrictions and possible solutions for students relying on smartphones for online learning. The study made recommendations for effective online learning which include affordable data packages, provision of suitable smart devices and the need for stable internet connections to all students. The study reveals that smartphones in Namibia have become a necessity to many students to access education during/post COVID-19 era.

Management of Adverse Reactions for BCMA-Directed Therapy in Relapsed Multiple Myeloma: A Focused Review.

Anti-B-cell maturation antigen therapies consisting of bispecific antibodies, antibody-drug conjugates, and chimeric antigen receptor T cells have shown promising results in relapsed refractory multiple myeloma (RRMM). However, the severe side effects include cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, cytopenia(s), infections, hemophagocytic lymphohistiocytosis, and organ toxicity, which could sometimes be life-threatening. This review focuses on these most common complications post-BCMA therapy. We discussed the risk factors, pathogenesis, clinical features associated with these complications, and how to prevent and treat them. We included four original studies for this focused review. All four agents (idecabtagene vicleucel, ciltacabtagene autoleucel, teclistamab, belantamab mafodotin) have received FDA approval for adult RRMM patients. We went through the FDA access data packages of the approved agents to outline stepwise management of the complications for better patient outcomes.

How We Developed a Data Exchange Format: Lessons Learned from Camera Trap Data Package (Camtrap DP)

Camera trapping has revolutionized wildlife ecology and conservation by enabling automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide (Steenweg et al. 2016, Kays et al. 2020, Delisle et al. 2021). Although management and processing of camera trap-derived big data are becoming increasingly solvable with the help of scalable infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. We therefore developed a new data exchange format, Camera Trap Data Package (Camtrap DP), to facilitate the exchange, harmonization and archiving of camera trap data at local and global scales (Camtrap DP Development Team 2023). Camtrap DP was developed with two guiding principles. It should: allow easy and interoperable data exchange and be developed openly and collaboratively. allow easy and interoperable data exchange and be developed openly and collaboratively. Camtrap DP structures the data in a simple model consisting of three tables (Deployments, Media, and Observations), which supports a wide range of camera deployment designs, classification techniques and analytical use cases. To describe these tables and the accompanying metadata, we adopted the Frictionless Standards, a collection of open specifications developed by the Frictionless Data project (Fowler et al. 2018), which offer a standardized way to describe datasets, data files and tabular data. Doing so, we did not have to reinvent how to express generic properties such as licenses, contributors, file formats, field names, data types, required values, controlled values, and relationships between tables. We expanded upon those to describe the necessary properties related to camera trapping, relying on existing standards such as Darwin Core (Wieczorek et al. 2012), Audiovisual Core (Audiovisual Core Maintenance Group 2023) and Data Cite Metadata Schema (DataCite Metadata Working Group 2021) where possible. This approach is not only efficient, but also facilitates interoperability: since a Camtrap DP is in essence a Frictionless Data Package, existing software tools can be used to read and validate data. We developed Camtrap DP openly, collaboratively, and with version control from the start. It is licensed under the permissive MIT license, allowing anyone to use it. Suggestions for change were and continue to be discussed in a public issue tracker on GitHub. These are incorporated only after review and automated testing. Once a number of changes have been adopted, a new version of the standard is released using semantic versioning. This allows Camtrap DP to evolve over time, while making sure that software and datasets referring to older versions of the standard are still valid. Equally important to the success of a data exchange format is community-wide adoption, which requires trust and implementation by existing systems. From the start, we have involved researchers as well as maintainers of software tools and management platforms from the camera trapping community. Using an iterative approach, they tested and provided feedback on Camtrap DP to make sure it met their requirements. To aid their understanding of the format, we provided a website, an example dataset that is versioned with the format, and an R package to read, explore and visualize Camtrap DP datasets (Oldoni et al. 2023). Through open development and outreach, we also managed to get the support from trusted and well-recognized organizations such as Biodiversity Information Standards (TDWG) and the Global Biodiversity Information Facility (GBIF). As a result, Camtrap DP can now be used as a data publication format in the Integrated Publishing Toolkit (Robertson et al. 2014).

The Catalogue of Life Data Package (CoLDP) and the Taxon Concept Standard (TCS): A Comparison

The Catalogue of Life Data Package (CoLDP; Catalogue of Life 2022) is the preferred mechanism for getting data in and out of the ChecklistBank. A CoLDP is a ZIP file containing files with delimiter-separated values, similar to the Darwin Core Archive (DwC-A; Global Biodiversity Information Facility 2023), but does not enforce the DwC-A star schema requiring a single Core file and zero or more Extension files. This makes it preferable for exchanging taxonomic data in tabular format as taxonomic data contains at least three different types of objects—taxa, names and references—that are all interlinked. TCS (currently still Taxon Concept Schema) is the TDWG (Biodiversity Information Standards) standard for dealing with taxonomic data specifically. An effort is currently underway (TCS 2 Task Group 2023) to free TCS from its XML schema and turn it into a TDWG vocabulary standard that is maintainable under the TDWG Vocabulary Maintenance Specification (VMS; Vocabulary Maintenance Specification Task Group 2017). For a domain standard like TCS, it is essential that it supports an important warehouse of taxonomic information like ChecklistBank and an exchange format like CoLDP. Therefore, as part of the Implementation Experience Report that has to accompany a major update to an existing TDWG standard, a comparison has been undertaken between TCS and the data model behind CoLDP. While some of the terms CoLDP uses are different from the ones used by TCS, the two specifications are completely compatible. This presentation will highlight some of the key similarities and differences. It is heartening to see that different groups of people independently chose to make the same departure from the existing specification. As TCS will be maintainable—and, in fact, still has to undergo public review—and CoLDP presumably can evolve as well, there are opportunities for TCS and CoLDP to come even closer together than they already are.

Identification of a miRNA-mRNA regulatory network for post-stroke depression: a machine-learning approach.

The study aimed to explore the miRNA and mRNA biomarkers in post-stroke depression (PSD) and to develop a miRNA-mRNA regulatory network to reveal its potential pathogenesis. The transcriptomic expression profile was obtained from the GEO database using the accession numbers GSE117064 (miRNAs, stroke vs. control) and GSE76826 [mRNAs, late-onset major depressive disorder (MDD) vs. control]. Differentially expressed miRNAs (DE-miRNAs) were identified in blood samples collected from stroke patients vs. control using the Linear Models for Microarray Data (LIMMA) package, while the weighted correlation network analysis (WGCNA) revealed co-expressed gene modules correlated with the subject group. The intersection between DE-miRNAs and miRNAs identified by WGCNA was defined as stroke-related miRNAs, whose target mRNAs were stroke-related genes with the prediction based on three databases (miRDB, miRTarBase, and TargetScan). Using the GSE76826 dataset, the differentially expressed genes (DEGs) were identified. Overlapped DEGs between stroke-related genes and DEGs in late-onset MDD were retrieved, and these were potential mRNA biomarkers in PSD. With the overlapped DEGs, three machine-learning methods were employed to identify gene signatures for PSD, which were established with the intersection of gene sets identified by each algorithm. Based on the gene signatures, the upstream miRNAs were predicted, and a miRNA-mRNA network was constructed. Using the GSE117064 dataset, we retrieved a total of 667 DE-miRNAs, which included 420 upregulated and 247 downregulated ones. Meanwhile, WGCNA identified two modules (blue and brown) that were significantly correlated with the subject group. A total of 117 stroke-related miRNAs were identified with the intersection of DE-miRNAs and WGCNA-related ones. Based on the miRNA-mRNA databases, we identified a list of 2,387 stroke-related genes, among which 99 DEGs in MDD were also embedded. Based on the 99 overlapped DEGs, we identified three gene signatures (SPATA2, ZNF208, and YTHDC1) using three machine-learning classifiers. Predictions of the three mRNAs highlight four miRNAs as follows: miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p. Subsequently, a miRNA-mRNA network was developed. The study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.

Adaptive event‐triggering mechanism‐based N‐step predictive load frequency control for power systems with cyber attack

AbstractAlthough there have already been many works on the model predictive control (MPC) for load frequency control (LFC) of modern power systems, limited works can be found in the literature related to the output feedback MPC. This article studies an N‐step synthesis approach of output feedback MPC for LFC systems considering the problems of communication efficiency and network security. First, to improve the communication efficiency, an adaptive event‐triggering (AET) scheme involving two adaptive laws is designed to reduce the number of transmitted data packages which offers more flexible compared with existing event‐triggering schemes; Second, to handle the network security, a new model of LFC power system combining the AET scheme and random deception attack under an unified framework is established; Moreover, a synthesis approach of output feedback MPC with N‐step strategy is addressed for LFC of power system by parameterizing the infinite control moves into a series of output feedback laws. Compared with the existing predictive load frequency control methods, the present technique is shown as an useful way to improve the control performance since more degrees freedom is introduced by the N‐step strategy. Finally, the simulation experiment is carried out to verify our technique.

Novel deep learning approach for DDoS attack using elephant heard optimization algorithm along with a fuzzy classifier for rules learning

The research aims to provide network security so that it can be protected from several attacks, especially DoS (Denial-of-Service) or DDoS (Distributed Denial-of-Service) attacks that could at some point render the server inoperable. Security is one of the main obstacles. There are a lot of network risks and attacks available today. One of the most common and disruptive attacks is a DDoS attack. In this study, upgraded deep learning Elephant Herd Optimization with random forest classifier is employed for early DDos attack detection. The DDoS dataset’s number of characteristics is decreased by the proposed IDN-EHO method for classifying data learning that works with a lot of data. In the feature extraction stage, deep neural networks (DNN) approach is used, and the classified data packages are compared to return the DDoS attack traffic characteristics with a significant percentage. In the classification stage, the proposed deep learning Elephant Herd Optimization with random forest classifier used to classify the data learning which deal with a huge amount of data and minimise the number of features of the DDoS dataset. During the detection step, when the extracted features are used as input features, the attack detection model is trained using the improved deep learning Elephant Herd Optimization. The proposed framework has the potential to be a promising method for identifying unidentified DDoS attacks, according to experiments. 99% recall, precision, and accuracy can be attained using the suggested strategy, according on the findings of the experiments.

Организация информационной структуры цифровой системы оперативного управления работой станции

The article is devoted to the study of control systems for the operation of railway station, driven by the trend in the industry towards the development and implementation of digital control systems that increase the functionality of the technological means used. The analysis of automated control systems used on railways and in the metro is carried out, their advantages and disadvantages are described. It is shown that it is necessary to build a digital system of operational control of the station, which provides automation of such intellectual functions as planning operations, forming tasks and monitoring their implementation by responsible departments. In the presented work, the synthesis of the information structure of such a system is carried out, providing the receipt, transmission and processing of data, the source of which is currently operated automated railway transport systems for various purposes. The gates of the structure (databases, information arrays) are defined, their description is given, connections between them are built, implemented functions are described. The concept of a model unit as a data package describing the information stored in the system for each element of the technological process of the station is formulated. Examples of algorithms for the functioning of the described structure in terms of movement control and the formation of model units of rolling stock located in the control loop of the system are given. Possible directions for further research are formulated.

Introducción al machine learning en Senología

Machine Learning or Statistical Learning is a concept belonging to the field of Computer Science that refers to the ability of machines to build mathematical models with high predictive efficiency from large data packages through a series of tools based on Statistics, Algorithmics and recursion, and to improve them as new information is incorporated.Although in the field of breast disorders there are already various projects mainly oriented to the interpretation of images in both Radiology and Pathology, the systematic use of this technology as a source of acquiring new knowledge is still exceptional, particularly with regard to clinical-therapeutic decision-making. All the specialists involved in the field of breast disorders are obliged to familiarize ourselves with this methodology, in order to properly direct it away from its use for profit, especially when the successes achieved in other social spheres allow us to intuit that its implementation in Medicine can not only be useful but unavoidable.

Evaluation of THEO-260 as a novel oncolytic virus therapy for treating stromal rich tumours.

e14573 Background: Stromal rich tumours pose a significant challenge to the effectiveness of systemic anticancer therapies, including immunotherapies and oncolytic viruses. This is due to the abundance of cancer associated fibroblasts (CAFs) that suppress immune cells and limit drug penetration. THEO-260 is a potent, mechanistically novel oncolytic virus therapy intrinsically capable of lysing both cancer cells and CAFs, and is currently in development for late stage relapsed refractory ovarian cancer. Methods: The efficacy and safety of THEO-260 was assessed in multicellular spheroids, in patient-derived ex vivo fresh ovarian samples and in mouse models of human stromal tumours. Results: Unpassaged ascites and tumour samples collected from cytoreductive surgeries for advanced ovarian cancer (n=24 independent patients) were treated with a clinically-relevant dose of THEO-260. THEO-260 effectively killed the cancer cell (EpCAM+ EGFR+PDL1+ CA125+) and CAF (FAP+) populations, being efficacious in treatment-naive and platinum-resistant patient samples. Importantly, the T cell population in these patients, in addition to primary normal fibroblasts (including intestinal, pulmonary, dermal, cardiac, uterine fibroblasts) from healthy donors, were not damaged. Ex vivo studies show THEO-260 stimulates effector T cell responses upon tumour lysis as indicated by IFNγ release. Demonstrating suitability as an IV delivered agent, THEO-260 was active in pooled human serum and whole human blood (n = 10 donors). THEO-260 excels in low nutrient, low proliferative environments, typical of human solid tumours and has been developed with a view to persist in the tumour microenvironment without the need for repeated cycles of delivery. THEO-260 is well tolerated in vivo following a clinically relevant intravenous dosing regimen. In PARP inhibitor and platinum resistant ovarian cancer murine models containing human CAFs and cancer cells, THEO-260 shows antitumor efficacy, with a complete reduction in tumour volume in subcutaneous and intraperitoneal systems, and superiority to SOC. Virus activity is not restricted to ovarian cancer, with cell line data demonstrating killing in additional solid tumour types, including pancreatic, colorectal, lung, and breast cancer. THEO-260 exhibits genetic and temperature based stability, and GMP manufacturing to high yields is underway. Conclusions: Taken together, we present a preclinical data package for THEO-260 supporting progression to Phase I clinical trials.

Boosting Adversarial Transferability with Shallow-Feature Attack on SAR Images

Adversarial example generation on Synthetic Aperture Radar (SAR) images is an important research area that could have significant impacts on security and environmental monitoring. However, most current adversarial attack methods on SAR images are designed for white-box situations by end-to-end means, which are often difficult to achieve in real-world situations. This article proposes a novel black-box targeted attack method, called Shallow-Feature Attack (SFA). Specifically, SFA assumes that the shallow features of the model are more capable of reflecting spatial and semantic information such as target contours and textures in the image. The proposed SFA generates ghost data packages for input images and generates critical features by extracting gradients and feature maps at shallow layers of the model. The feature-level loss is then constructed using the critical features from both clean images and target images, which is combined with the end-to-end loss to form a hybrid loss function. By fitting the critical features of the input image at specific shallow layers of the neural network to the target critical features, our attack method generates more powerful and transferable adversarial examples. Experimental results show that the adversarial examples generated by the SFA attack method improved the success rate of single-model attack under a black-box scenario by an average of 3.73%, and 4.61% after combining them with ensemble-model attack without victim models.

Identification of immune-related genes in acute myocardial infarction based on integrated bioinformatical methods and experimental verification.

Acute myocardial infarction (AMI) is one of the leading causes of death worldwide. The etiology of AMI is complex and has not been fully defined. In recent years, the role of immune response in the development, progression and prognosis of AMI has received increasing attention. The aim of this study was to identify key genes associated with the immune response in AMI and to analyze their immune infiltration. The study included a total of two GEO databases, containing 83 patients with AMI and 54 healthy individuals. We used the linear model of microarray data (limma) package to find the differentially expressed genes associated with AMI, performing weighted gene co-expression analysis (WGCNA) to further identify the genes associated with inflammatory response to AMI. We found the final hub genes through the protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) regression model. To verify the above conclusions, we constructed mice AMI model, extracting myocardial tissue to perform qRT-PCR. Furthermore, the CIBERSORT tool for immune cells infiltration analysis was also carried out. A total of 5,425 significant up-regulated and 2,126 down-regulated genes were found in GSE66360 and GSE24519. A total of 116 immune-related genes in close association with AMI were screened by WGCNA analysis. These genes were mostly clustered in the immune response on the basis of GO and KEGG enrichment. With construction of PPI network and LASSO regression analysis, this research found three hub genes (SOCS2, FFAR2, MYO10) among these differentially expressed genes. The immune cell infiltration results revealed that significant differences could be found on T cells CD4 memory activated, Tregs (regulatory T cells), macrophages M2, neutrophils, T cells CD8, T cells CD4 naive, eosinophils between controls and AMI patients.

Best practice considerations for nonclinical in vivo cardiovascular telemetry studies in non-rodent species: Delivering high quality QTc data to support ICH E14/S7B Q&As

The ICH E14/S7B Questions and Answers (Q&As) guideline introduces the concept of a “double negative” nonclinical scenario (negative hERG assay and negative in vivo QTc study) to demonstrate that a drug does not produce a clinically relevant QT prolongation (i.e., no QT liability). This nonclinical “double negative” data package, along with negative Phase 1 clinical QTc data, may be sufficient to substitute for a clinical Thorough QT (TQT) study in some specific cases. While standalone GLP in vivo cardiovascular studies in non-rodent species are standard practice during nonclinical drug development for small molecule programs, a variety of approaches to the design, conduct, analysis and interpretation are utilized across pharmaceutical companies and contract research organizations (CROs) that may, in some cases, negatively impact the stringent sensitivity needed to fulfill the new Q&As. Subject matter experts from both Pharma and CROs have collaborated to recommend best practices for more robust nonclinical cardiovascular telemetry studies in non-rodent species, with input from clinical and regulatory experts. The aim was to increase consistency and harmonization across the industry and to ensure delivery of high quality nonclinical QTc data to meet the proposed sensitivities defined within the revised ICH E14/S7B Q&As guideline (Q&As 5.1 and 6.1). The detailed best practice recommendations presented here cover the design and execution of the safety pharmacology cardiovascular study, including optimal methods for acquiring, analyzing, reporting, and interpreting the resulting QTc and pharmacokinetic data to allow for direct comparison to clinical exposures and assessment of safety margin for QTc prolongation.