Infinitylists: A Shiny application and R package for rapid generation of place‐based species checklists

BackgroundAllergic diseases are systemic chronic inflammatory diseases associated with multiorgan damage and complex pathogenesis. Several studies have revealed the association of gene expression abnormalities with the development of allergic diseases, but the biomedical field still lacks a public platform for comprehensive analysis and visualization of transcriptomic data of allergic diseases.ObjectiveThe aim of the study is to provide a comprehensive web tool for multiple analysis in allergic diseases.MethodsWe retrieved and downloaded human and mouse gene expression profile data associated with allergic diseases from the Gene Expression Omnibus (GEO) database and standardized the data uniformly. We used gene sets obtained from the MSigDB database for pathway enrichment analysis and multiple immune infiltration algorithms for the estimation of immune cell proportion. The basic construction of the web pages was based on the Shiny framework. Additionally, more convenient features were added to the server to improve the efficiency of the web pages, such as jQuery plugins and a comment box to collect user feedback.ResultsWe developed CTPAD, an interactive R Shiny application that integrates public databases and multiple algorithms to explore allergic disease-related datasets and implement rich transcriptomic visualization capabilities, including gene expression analysis, pathway enrichment analysis, immune infiltration analysis, correlation analysis, and single-cell RNA sequencing analysis. All functional modules offer customization options and can be downloaded in PDF format with high-resolution images.ConclusionsCTPAD largely facilitates the work of researchers without bioinformatics background to enable them to better explore the transcriptomic features associated with allergic diseases. CTPAD is available at https://smuonco.shinyapps.io/CTPAD/.

Opening and processing gene expression data files in Excel runs into the inadvertent risk of converting gene names to dates. As pathway analysis tools rely on gene symbols to query against pathway databases, the genes that are converted to dates will not be recognized, potentially causing voids in pathway analysis. Molecular pathways related to cell division, exocytosis, cilium assembly, protein ubiquitination and nitric oxide biosynthesis were found to be most affected by Excel auto-conversion. A plausible solution is hence to update these genes and dates to the newly approved gene names as recommended by the HUGO Gene Nomenclature Committee (HGNC), which are resilient to Excel auto-conversion. Herein, we developed a web tool with Streamlit that can convert old gene names and dates back into the new gene names recommended by HGNC. The web app is named Gene Updater, which is open source and can be either hosted locally or at https://share.streamlit.io/kuanrongchan/date-to-gene-converter/main/date_gene_tool.py. Additionally, as Mar-01 and Mar-02 can each be potentially mapped to 2 different gene names, users can assign the date terms to the appropriate gene names within the Gene Updater web tool. This user-friendly web tool ensures that the accuracy and integrity of gene expression data is preserved by minimizing errors in labelling gene names due to Excel auto-conversions.

Open OnDemand is an open source project to provide web based access to HPC resources (https://openondemand.org). OnDemand's Interactive Apps support launching web applications like Jupyter and RStudio on cluster compute nodes, proxying HTTP requests to those apps, and secure user separation of both app and data access enforced at the OS level [5]. This paper describes leveraging OnDemand to enable the deployment of R Shiny applications that can be launched in OnDemand's interactive HPC environment by multiple analysts at the same time to visualize different datasets. Dataset and app authorization is managed using network file system (NFS) file access lists (ACLs). A custom OnDemand app was built to enable admins to manage who has access to specific apps and what datasets can be loaded by those apps. By using OnDemand to facilitate the use of an HPC batch scheduler as the process manager and OnDemand as the proxy we can support multiple analysts launching Shiny apps to access their data in a scalable and secure manner without the need for managing a separate dedicated installation of RStudio or Shiny Server.

Conservation research and policies tend to be significantly restricted wherever relevant data on biodiversity is sparse, scattered or non-curated. Thus, the usefulness of occurrence data, for the study of biodiversity, depends not only on the availability but also on data quality. Notwithstanding the increase in the global availability of primary biodiversity data, they have numerous shortfalls, from incomplete or partially erroneous documentation to spatial and temporal biases (Hortal et al. 2015, Aubry et al. 2017). Also, many non-digitized specimen collections, scientific publications and grey literature are locked as printed or digital publications. We integrated existing knowledge, from dispersed sources of biodiversity data, namely Global Biodiversity Information Facility (GBIF), natural history collections, wildlife survey reports, species checklist and other scientific literature. This procedure allowed an update of Mozambique’s checklist of terrestrial mammals (Neves et al. 2018). Despite the potential from digital data to overcome gaps of knowledge, a relevant constraint on creating or updating species checklist is the dificulty to access spatially-disperse collections and examine every specimens upon which occurrences are based. To partly overcome this impediment, we developed a species selection process for specimen data from GBIF and museums (Fig. 1). The aim was to categorise the species detected in more than one data source as species with the well-supported occurrence. In addition to the number of collectors, we also accounted for the number of records collected and presented in Smithers and Tello (1976), the last checklist produced for Mozambique’ mammals. A species-occurrence record was considered well-supported and included into the species checklist when was: independently recorded by different collectors or recorded by a single collector but listed in Smithers and Tello (1976). independently recorded by different collectors or recorded by a single collector but listed in Smithers and Tello (1976). An additional list was produced which contained species with questionable occurrence in the country. Species entered this "questionable occurrence" list when they were: not listed in Smithers and Tello (1976), and a single record supported its presence in the country; not listed in Smithers and Tello (1976) and multiple records exist, but were all cited by a single author; or registered with a single record in Smithers and Tello (1976). not listed in Smithers and Tello (1976), and a single record supported its presence in the country; not listed in Smithers and Tello (1976) and multiple records exist, but were all cited by a single author; or registered with a single record in Smithers and Tello (1976). We compiled more than 17000 records, resulting in a total of 217 species (14 orders, 39 families and 133 genera) with supported occurrence in Mozambique and 23 species with questionable reported occurrence (Table 1). The proposed approach for species selection can be adapted and function as a powerful tool to update species checklists of countries facing similar lack of knowledge regarding their biodiversity. The capacity to pinpointing species and specimens in need of occurrence and taxonomic re-evaluation is of great value to optimise collection’s study and to boost collaboration between curators and researchers. Lastly, considering that most records integrated are from European and North American institutions, this work would significantly improve with the integration of data from African institutions. Therefore, an effort should be made to make these essential collections accessible online.

Can students learn a foreign language at school meeting their real communicative needs? Is it possible to exploit the potentialities of the 2.0 Web tools and the advantages of the Open Source software to guide students towards effective linguistic competence and autonomy? The chapter describes an experience of using simple Web 2.0 and teaching support online tools for learning English in an Italian secondary school, pointing out the achievements and the drawbacks of the integration of e-learning 2.0 with classroom teaching.

The purpose of this paper is to present a technical path in order to formulate the creation of small customised web applications that handle big data. Such web tools could be easily integrated in knowledge-based or data driven decision support systems and provide added value. The paper studies the various ways in which relevant application can be developed and the findings resulted in a specific technical proposal. This is a combination of techniques and technologies that can be pursued to achieve more efficient and costless data analysis. To this direction, a novel demo web app was developed with open source code in which a user can login, upload data and analyse them in real-time with the multivariate exploratory method of multiple correspondence analysis (MCA). The application was created under R programming language and underlines the use of shiny library. The paper contributes to the scientific facilitation of researchers and professionals with a minimum technical background in order to perform online complex data analytics tasks. Eventually, this architecture enables extensibility and customisation that suggests how decision support modules should be delivered in intelligent data systems.

Background: The Mycobacterium tuberculosis complex (MTBC) bacteria include the slowly growing, host-associated bacteria Mycobacterium tuberculosis, Mycobacterium Bovis, Mycobacterium microti, Mycobacterium africanum, Mycobacterium pinnipedii. Aim: Comparative Functional Genomics Studies for understanding the Hypothetical Proteins in Mycobacterium tuberculosis variant microti 12. Objective: A computational genomics study was performed to understand the 247 hypothetical protein genes. Functional annotation of virtual proteins was performed on different servers to maximize confidence level. Methods: Sequence Retrieval. The whole genome sequences for the Mycobacterium tuberculosis micro variant 12 were retrieved from the KEGG database ( http://www.genome.jp/kegg/) and were used for screening 247 hypothetical proteins (Fig. 1 ). Functional Annotation and Sub-cellular localization. The Mycobacterium tuberculosis micro variant 12 hypothetical proteins were screened and sorted out from the genome and were individually analyzed for the presence of conserved functional domains by using computational biology tools like CDD-BLAST ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) ;Pfam ( http://pfam.xfam.org/ncbiseq/398365647); The subcellular localization of hypothetical proteins was determined by CELLO2GO ( http://cello.life.nctu.edu.tw). These web tools can search the defined conserved domains in the sequences available in the online servers or databases and assist in the classification of proteins in the appropriate families. Protein Structure Prediction. The in-silico structure predictions of the hypothetical protein sequences showing functional properties were carried out by using the PS2 Protein Structure Prediction Server ( http://www.ps2.life.nctu.edu.tw/). The online server helps to generate the 3D structures of the hypothetical proteins. The server accepts the sequences in FASTA format as a query to generate resultant proteins 3D structures. The structure determination is completely based on the conserved template regions detected during functional annotations. Protein-protein interaction through String database: The interaction of each hypothetical protein analyzed for functional characteristics was subjected to a protein-protein interaction server for the prediction of a possible functional role in interaction amongst the available known proteins ( https://string-db.org/). This information can help us to further validated the functional role of such hypothetical proteins and their possible role in the Mycobacterium Tuberculosis micro variant. Protein secondary structure prediction through JPred4: The secondary structure prediction of all the hypothetical proteins was determined through JPred4 ( http://www.compbio.dundee.ac.uk/jpred4/index.html) and served to identify the available secondary structures in the unknown hypothetical protein sequences. These further help us to understand the available templates in the uncharacterized protein sequences for the prediction of novel functions associated with these proteins. The predictions were further characterized by the Phyre2 server for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Protein modeling, prediction, and analysis through Phyre2. The hypothetical proteins which were identified to have functional properties were further characterized by the Phyre2 server ( http://www.sbg.bio.ic.ac.uk/phyre2) for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Results: A computational genomics study was performed to understand the 247 hypothetical protein genes Functional annotation of virtual proteins, and was performed on different servers to maximize confidence level. The functional prediction was performed by CDD-Blast and Pfam. The gene sequences of proteins have probably been successfully functionally annotated, characterized, and their subcellular localization and 3-D structural predictions have been predicted computationally. Online automated bioinformatics tools such as CDD-Blast, Pfam, CELLO2GO and PS2-Server were used for the structural and functional characterization of screened hypothetical proteins. The structure, function, and subcellular localization of a hypothetical protein from Mycobacterium tuberculosis variant microti 12 have been obtained and presented (Fig. 2 ). Also, the three-dimensional structure generated after using the template with the highest score was displayed as the template ID in the structure column of the respective hypothetical protein. However, as systems biology denies hypothetical protein functions, the structures of such proteins can be tested through biological processes and experiments, making them suitable for understanding their role in the life cycle, pathogenesis, and drug development. We can further explore these predictive possibilities in pharmaceuticals, and other clinically relevant studies. This study by HP helped find structure-function relationships in Mycobacterium tuberculosis variant microti 12 using a variety of bioinformatics tools. The string database made predictions about protein-protein interactions and the template helped us predict a hypothetical protein structure and even helped us find its 3D protein structure. Protein profiling can be performed on structures retrieved from these servers. This is useful for proteomics studies, including protein-protein interactions, protein expression of specific hypothetical proteins, and post-translational modifications of protein-coding genes. Further understanding of these hypothetical proteins can help us to know more about the Mycobacterium tuberculosis complex (MTBC) and may assist in Drugs and inhibitors against different pathogens within this complex. Conclusion: The all-inclusive bioinformatic study has helped to functionally elucidate 247 hypothetical proteins, which have resulted and made it easier to understand many functional proteins available in the Mycobacterium tuberculosis micro variant 12. The subcellular localization of the 247 sorted hypothetical proteins was also carried & which further helped us understand the localization of identified enzymes or proteins. We have successfully characterized the 247 unknown proteins of hypothetical protein sequences from Mycobacterium tuberculosis micro variant 12 to validate their structure and functions of the gene products. These predicted functions and three-dimensional structures may lead to establishing their role in the life cycle of the bacterium. This computationally generated data can also be further used for developing new protocols for new vaccines against Mycobacterium tuberculosis micro variant 12 that are essential for preventing infection, diseases, and transmission. This complete result of Hypothetical Protein is needed for further studies of the whole genomic of the Mycobacterium Tuberculosis micro variant 12 for their function interpretation which further help in the understanding of its functions as well as structure. Moreover, this interpretation would help us to study the evolution of Mycobacterium Tuberculosis micro variant 12 which further helps in the process of discovering the drugs to inhibit the causes of diseases.

The use of molecular tools in the study of parasite taxonomy and systematics have become a substantial and crucial component of parasitology. Having genetic characterisation at the disposal of researchers has produced mostly useful, and arguably more objective conclusions. However, there are several groups for which limited genetic information is available and, coupled with the lack of standardised protocols, renders molecular study of these groups challenging. The Diplozoidae are fascinating and unique monogeneans parasitizing mainly freshwater cyprinid fishes in Europe, Asia and Africa. This group was studied from a molecular aspect since the turn of the century and as such, limitations and variability concerning the use of these techniques have not been clearly defined. In this review, all literature and molecular information, primarily from online databases such as GenBank, were compiled and scrupulously analysed for the Diplozoidae. This was done to review the information, detect possible pitfalls, and provide a “checkpoint” for future molecular studies of the family. Hindrances detected are the availability of sequence data for only a limited number of species, frequently limited to a single sequence per species, and the heavy reliance on one non-coding ribosomal marker (ITS2 rDNA) which is difficult to align objectively and displays massive divergences between taxa. Challenging species identification and limited understanding of diplozoid species diversity and plasticity are also likely restricting factors, all of which hamper the accurate taxonomic and phylogenetic study of this group. Thus, a more integrated taxonomic approach through the inclusion of additional markers, application of more rigorous morphological assessment, more structured barcoding techniques, alongside thorough capturing of species descriptions including genetypes, genophore vouchers and reference collections in open sources are encouraged. The pitfalls highlighted are not singular to the Diplozoidae, and the study of other groups may benefit from the points raised here as well.

In the era of omics with the huge available data sets in field of genomics, transcriptomics, proteomics, metabolomics and phenomics need the quick access and efficiently use of the information to validate the functions for the particlular gene family or the gene of interest in the plant cell. Therefore, user-friendly bioinformatics tools are utmost important for the students, researchers, and scientists to carry out successful functional genomics experiments. Here we describe the free available online databases and tools that can be utilized without prior deep knowledge of bioinformatics. The access to genomics, transcriptomics, metabolomics, and phenomics tools will be beneficial for designing of intelligent experiments.

Background: The most comprehensive synopsis of the mammal fauna of Mozambique was published in 1976, listing 190 species of terrestrial mammals. Up-to-date knowledge of the country’s biodiversity is crucial to establish the baseline information needed for conservation and management actions.Objectives: The aim of this article was to present a list of terrestrial mammal species reported from Mozambique, based on primary occurrence data.Method: We integrated existing knowledge, from dispersed sources of biodiversity data: the Global Biodiversity Information Facility portal, natural history collections, survey reports and literature. Data were updated and manually curated. However, none of the specimens upon which occurrences are based was directly observed. To partly overcome this impediment, we developed a species selection process for specimen data. This process produced the country’s species checklist and an additional list of species with questionable occurrence in the country.Results: From the digital and non-digital sources, we compiled more than 17 000 records. The data integrated resulted in a total of 217 mammal species (representing 14 orders, 39 families and 133 genera) with supported occurrence in Mozambique and 23 species with questionable reported occurrence in the country.Conclusion: The diversity of species accounted for is considerable as more than 70% of species present in the southern African subregion are found in Mozambique. We consider that the current number of mammal species in Mozambique is still underestimated. The methodological approach for species selection for specimen data can be adapted to update species checklists of crucial importance to countries facing similar lack of knowledge regarding their biodiversity.

Barnes et al., (2024). regional-mom6: A Python package for automatic generation of regional configurations for the Modular Ocean Model 6. Journal of Open Source Software, 9(100), 6857, https://doi.org/10.21105/joss.06857

The ability to display and inspect powder diffraction data quickly and efficiently is a central part of the data analysis process. Whilst many computer programs are capable of displaying powder data, their focus is typically on advanced operations such as structure solution or Rietveld refinement. This article describes a lightweight software package,Jpowder, whose focus is fast and convenient visualization and comparison of powder data sets in a variety of formats from computers with network access.Jpowderis written in Java and uses its associated Web Start technology to allow `single-click deployment' from a web page, http://www.jpowder.org.Jpowderis open source, free and available for use by anyone.

시뮬레이션 가시화 기술은 시뮬레이터 사용자가 직접적으로 접하게 되는 기술이며, 사용자들은 보다 빠르고, 보다 현실적이며, 보다 직관적인 시뮬레이션 영상을 요구하고 있다. 컴퓨팅 기술 및 영상장비 기술 등의 하드웨어는 우수한 성능으로 발전된 반면, 현재 일반적으로 선박 운항 시뮬레이터에 사용되고 있는 그래픽 렌더링 엔진은 그 한계를 나타내고 있다. 또한 이러한 배경에서 본 논문에서는 선박 운항 시뮬레이션 시스템의 가시화를 위해 필요한 요구사항을 도출하였다. 가시화 요구사항들을 반영할 수 있는 상업용 및 공개용 그래픽 렌더링 엔진에 대해 조사/검토하며, 시스템 요구사항을 만족시키기 위한 그래픽 렌더링 엔진의 장단점을 분석하였다. 이를 통해 선박 운항 시뮬레이션 가시화에 대한 그래픽 렌더링 엔진으로서 OGRE3D의 활용가능성을 평가하였으며, 예부선 운항 시뮬레이션 가시화를 대상으로 OGRE3D 엔진의 타당성을 검토하였다. Simulation visualization technology is an important constituent through which users directly interact with simulators. Simulator users have a needs for more fast, realistic and intuitive visualization. Though hardware-related performances such as computing power and visual equipment have been grown, the limits have existed in graphics rendering engines generally used in marine simulator up to now. This paper has focused on the review of graphic rendering engines available for simulation visualization. We had deduced system requirements for visualization of ship handling simulation, had surveyed graphic rendering engines as commercial and open source, and analyzed strengths and weakness of them. The feasibility study for simulation visualization of tug-barge transportation with an open source graphics rendering engine(OGRE3D) has been demonstrated.

The wild bee fauna (Hymenoptera: Apoidea: Anthophila) of the Canary Islands has received considerable attention from researchers and naturalists over the years. However, since the last comprehensive report published in the 1990´s, critical knowledge gaps regarding taxonomy, distribution and origins remain. Here, we present an updated checklist of the bee species of the Canary Islands, incorporating recent taxonomic revisions, newly described species, and relevant distribution data by combining information from both literature and citizen science contributions. More than 8000 records from citizen science platforms, along with additional collections records, have been examined to update the spatial distribution coverage and address several biological knowledge gaps. Our results suggest a composition ruled by a high proportion of endemic and threatened species compared to adjacent continent areas. The present checklist encompasses 146 species and 46 subspecies for the archipelago, 60 of which are endemic species and 42 endemic subspecies. In total 63 new local records are provided and three species Lasioglossum medinai (Vachal, 1895), Seladonia gemmea (Dours, 1872) and Sphecodes rubripes Spinola, 1838 are reported for the first time. Our biogeographical approach reveals differences in faunistic composition among islands, identifying three clusters corresponding to species predominantly associated with eastern, central, and western islands. Overall, Canarian endemic and Palaearctic elements are consistently represented across the archipelago, with endemics notably dominant in the western islands and introduced global species more frequent in the central islands. In contrast, Saharo-Arabian and Mediterranean elements are more prominent in the eastern islands, likely reflecting their geographical proximity to North Africa.

The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects – often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.