dataesr/scientific-paragraphs-categorization huggingface dataset:
Breaking barriers Another route to improve on metabolite annotation performance is to gain complementary information about the same samples from other sources such as genomics. In natural product discovery, genome mining tools such as antiSMASH  136  can mine genome and metagenome sequences for their biosynthesis potential and return predicted biosynthesis gene clusters that likely encode for the production of specialized metabolites. Tools like BiG-SCAPE  137  and BiG-SLICE 6 can, at a respectively smaller and larger scale, group those biosynthesis gene clusters in gene cluster families that are likely to produce structurally related metabolites. One can then envision that such gene cluster families could be linked to molecular families obtained from metabolomics data,  138  for example through pattern-based genome mining.
139 That would help to nd the likely producers of specialized metabolites in complex samples, as well as to gain complementary structural information such as about the stereochemistry of chiral centers. In recent years, the rst strain correlation and feature-based scores have been developed,  15  as well as novel scores, 140 i. e. as proposed by Soldatou et al. ,  141  to charter chemical diversity in polar bacteria. Such attempts are largery enabled by cataloguing (i) known and validated biosynthesis gene clusters (BGCs) encoding for the production of specialized metabolites in MIBiG,  142  (ii) known and curated metabolites with metadata on their origins in databases such as NP Atlas,  129  and reference spectra in open libraries such as those from MassBank 143 and GNPS,  21  and will be further driven by their integration and initiatives such as the paired omics data platform that brings together sequenced genomes and metagenomes with LC-MS/MS metabolomics proles that are available in the public domain.
144 For example, the NPLinker framework has already adopted a workow to directly read in data from the paired omics data platform to then perform various scores to rank and prioritize BGC-MS/MS spectral links for further study. 140 Looking into the future, substructure-based workows could further enhance such rankings by linking the substructure annotations of MS2LDA and MESSAR with those from the antiSMASH subclusterblast and by statistical recognition of genes that could encode for the production of a substructure. 145 Alternatively, the predicted antiSMASH compound classes (non-ribosomal peptides, polyketides, etc. ) could be used to prioritize MS or NMR spectra that are predicted to be of those compound classes through the use of MolNetEnhancer or CANOPUS or NMR-based alternatives. Most of the attention in this review was spent on the proling of non-volatile specialized metabolites.
However, nature produces many volatiles and recently an open platform for GC-MS data analysis and library matching was established. 54 nalogous to the above-described options to link MS/MS-based substructures with those observed in 2D-NMR, we could envision something similar for GC-MS to develop. The structural annotation of metabolites is usually only the start: it is typically the functional annotation that is most relevant to the biological or biochemical research question. Well curated and annotated datasets are crucial to make large-scale metabolomics analyses effective in functional analysis. In that respect, the launch of ReDU in 2020 symbolizes the beginning of repository-level analysis of MS/MS datasets. 146 ReDU allows the reanalysis of public MS/MS datasets with metadata using a controlled vocabulary, which enables researchers to project their data on all the available public data in ReDU and assess its chemical uniqueness.
The other way around, researchers can also select a specic subset of the LC-MS/MS data, for example all fungal datasets, when they want to study fungal chemical diversity. The next step is to do the same thing for MS/MS spectra: when metadata at the spectral level is consistently collected and stored, this will allow researchers to better track the origin and possible functions of the metabolites in their proles. Moreover, through network and substructure-based approaches, such spectral metadata can also be propagated to related metabolites in the same dataset. It is encouraging to observe that also on the NMR side there is an increased attention for sample metadata. 147 ith analyses happening at increasingly larger scales, indepth repository-scale analyses are becoming within reach. To visualize such amounts of data and efficiently extract the relevant information from it, novel algorithms are needed that are scalable, i.
e. , in clustering all MS/MS spectra, that speed up analysis time, and that can visualize the resulting data structures to enable analysis and biochemical interpretations. Such developments that lead toward repository-scale analyses will in turn increase the benets of sharing well-documented datasets in the public domain, thus changing data sharing from a perceived (time) burden into an actual benet. We do note that the growth in the number of available computational tools and the increasing density and size (number of data les) of typical metabolomics experiments can come with high computational costs. Whilst numerous tools can be run on a fairly normal laptop or desktop computer, there are several tools that benet from dedicated servers with sufficient memory and cores available. Furthermore, many tools have not been fully optimized for performance; we refer to the review by Chang et al.
Conclusions & final perspectives Computational metabolomics approaches have started to change the metabolomics eld by automating various aspects of typical metabolomics workows and thereby enabling largescale metabolomics analysis. Together with the current increase in publicly available datasets, this has also shied the focus of many studies towards unknown metabolites that are not fully described and catalogued in databases yet. In this paper, we show how substructure and network-based metabolomics approaches can cause a paradigm shi in the annotation level of these yet unknown metabolites in the forthcoming years by leveraging structural, chemical compound class, and substructural information from MS/MS and NMR spectral data. Once structural motif databases are sufficiently populated, they in turn will spark the development of new tools to accelerate the elucidation of entire structures and metabolic pathways based on the available spectral data in various conditions.
We expect that the linking of MS/MS spectra to information obtained from genome mining positively contributes to the annotation power of metabolomics data. We foresee that the impact of machine learning-based approaches will further increase with the increased availability of metabolomics data that can serve as training and test data to improve the performance of spectral library matching and to create biochemically interpretable mass spectral networks. Together with well-curated and consistent reported metadata, this will open up new avenues to directly link taxonomic and functional annotations to spectral data. Altogether, we conclude that networking and substructurebased computational metabolomics analysis workows have already started to form an essential part of the future of metabolomics in which large-scale metabolomics datasets can be rapidly transformed into present and active metabolic pathways and metabolite groups with annotated functionsa necessity to efficiently apply wide-screen metabolomics.
This study leverages the status quo bias theory and adoption literature to conceptually replicate and enrich the theory in the context of a post implementation of human resource (HR) information systems in a European context. To undertake the study, we gathered data from a French company via survey and objective data of system usage. Our results support the proposed enriched user resistance model empirically. Behavioral intention to use the new HR tool investigated in the study was found to be an intermediate variable that explained user resistance and IT use. The results of the study offer both theoretical and practical implications to manage resistance and change amongst IT savvy employees more specifically. We provide the investigative context and theoretical background in the following two sections of the paper.
Perceived value is then shown to reduce or increase user resistance behaviors to a new IT tool. However, this seminal work was set in the context of pre-implementation of an IT tool. The study was conducted within a major IT service company that was deploying a new enterprise portal and knowledge management system. This tool studied was not a human resource management tool that we study in this paper rather it was a tool for enterprise-wide collaboration and knowledge management functions  (Kim & Kankanhalli, 2009). Data was gathered via a survey from general employees from various functional units of the company as opposed to IT savvy employees as we do in this paper. The study could not prove all the laid out hypothesis empirically, see Figure  1  of model proposed by  (Kim & Kankanhalli, 2009).
Objective inputs from the system were extracted for this variable. The raw data was categorized into a range from 'none' to 'more than 4 times' of HR tool usage in the last three months. Next, for instrumentation purposes, we tested the measurement model with IT use and IT user resistance as our dependent variables. Factor analysis showed that there is a strong correlation between each of the items and their corresponding construct. This demonstrates convergent validity. We tested convergent validity by examining the composite reliability (CR) and average variance extracted (AVE: the ratio of the construct variance to the total variance among indicators) for the indicators  (Hair et al. , 1998). We note that 0. 70 is the suggested CR threshold for reliable measurement  (Chin, 1998). As seen in Table  2 , the CR values ranged from.
16 n intermediate position is to develop a plugin or extension for an existing platform. Extensibility has been key to the success of software projects such as ImageJ,  17  Fiji,  18  Icy  19  and CellProfiler,  20  with the result that many novel algorithms are developed as plugins for these platforms. One example is the CLIJ library, which brings manifold implementations of accelerated image processing algorithms as plugins to platforms such as ImageJ and Icy. 11 This strategy not only reduces effort for the developer, who can focus primarily on developing new functionality beyond that already available, but also reduces the effort required by users to become familiar with entirely new softwares. For this reason, we advocate developing plugins or adapting existing solutions wherever possible.
[25] [26] [27] [28]  When launching a new software project, our experience is to carefully consider the boundaries of the audience to be served. As maintenance is an ongoing tax against future software developments (not to mention research progress, which is often necessary for funding the software), it is important to not be overly ambitious by attempting to create software that addresses too many distinct problems simultaneously. For example, in the CellProfiler project, we aimed to serve the community needing modular, automated pipelines, and left interactive manual analysis and image visualization tools to ImageJ, which was well-developed in these features. We also left a gap for very large-format images such as for pathology, which would have been difficult to support well given its underlying infrastructure; QuPath was later developed specifically to meet this need from the ground up.
23 Likewise, Single-Molecule Localization microscopy was not well served by generic bioimage analysis platforms, because it produces point clouds rather than conventional images; the Tesseler-suite  25, 29  and its successor PoCA  30  were created to meet this need. In another example, ilastik  31  was developed with the aim of providing interactive machine learning tools, intentionally leaving pre-and post-processing steps to Fiji and CellProfiler. OMERO has always remained largely focused on image storage and organization. 32 efining the scope in this way prevents a project from being spread too thin and serving no audience very well. Furthermore, interoperability between software using open standards makes it possible to gain the benefits of combining applications. Another choice to consider is whether to serve point-and-click users versus those who are comfortable writing code themselves; some software serves both ends of this spectrum well, but again defining the audience proactively ensures that developer time is not spread too thin meeting those needs.
In contrast, Java applications are portable and can be executed on all major platforms, without the need for recompilation. Java applications can also readily use the image processing ecosystem around ImageJ  17  and Bio-Formats for image file reading/writing; 33 together these advantages help explain why so many bioimage analysis platforms have been developed in Java. Finally, recent years have seen an important increase in the number of workflows and tools developed in Python. Python has become the language of choice for data science and can be self-learned by non-computer scientists. Combined with its numerous available scientific libraries, Python represents a solution that is well-suited for developing new analysis methods using both image processing and machine learning techniques. Each language has its pros and cons, and bridges exist to allow communication between tools developed in different languages.
For the user, the best scenario is when all dependencies are directly included in the installer or are automatically downloaded. If they are not included, the user will have to install them separately, a potential source of issues and a support burden for the developer. Interpreted languages such as Python have slightly shifted this common process as scripts/apps can be bundled as packages and easily installed using packagemanagement systems such as pip. In practice, dependencies or version issues are not uncommon, and installing Python modules may still require some level of expertise. Python scripts can also be bundled in web-based interactive computational environments (such as Jupyter Notebooks). To ensure simplicity, we advocate providing 1-click installers for bioimage analysis tools where feasible, supporting all three popular operating systems.
Version control and quality assurance The development process of scientific software includes two more important elements: Firstly, version control is good scientific practice, allowing developers to maintain their code in a modern fashion while tracing changes and contributions. The git platform (https://git-scm. com/) is most commonly used for version control. Furthermore, releases should be tagged and archived to allow reproducing scientific results produced with a given release of a scientific software later on. Secondly, automatic and manual quality assurance procedures should be established in scientific software projects to minimize potential issues introduced when code changes. Software is nowadays highly complex and developers cannot foresee if a change may cause issues in code that depends on the modified function.
Documentation Software projects aiming to bring services and solutions to a broad audience require special emphasis on documentation: beyond a publication that mostly serves as an announcement of major software features, there are four kinds of modern software documentation:  35  1) learning-oriented tutorials targeting beginners and introducing them to basic principles of a given software or software library; 2) goal oriented guides documenting typical use cases of a software, in which users can learn a new tool; 3) understanding-oriented discussions benefiting developers who might, especially in open-source projects, become contributors to extend and maintain a given software project; and 4) information-oriented reference manuals serving as a glossary of functionality, for example as a searchable database of individual functions to explore features and read their concise description.
Documentation should not be seen as the final step before launching a new software. It is rather a continuous process, particularly when new features are added. Furthermore, user feedback about the documentation can ultimately also lead to updates in the software. Software features that are intuitive to the developers might be complicated to explain in the user guide while writing the corresponding documentation, triggering a rethinking of how the feature is offered to the user and changing the GUI or the underlying algorithm. This will improve user experience and the documentation. Finally, newly released bioimage analysis tools should always be provided with example datasets, for example included in the installer or uploaded on online platforms, such as Zenodo (https://zenodo. org), chosen to illustrate some specific features.
Much effort has been devoted by the bioimage community to promote external interoperability. For instance, Bio-Formats 33 is a software library for reading proprietary microscopy formats and converting to open standardized formats. These efforts are also resulting in community efforts towards new open formats. 36, 37  a higher level, internal interoperability can be achieved by plugin mechanisms to ensure that within a given software application, modules from various origins can communicate with each other. Software such as ImageJ 17 /Fiji,  18  CellProfiler, 20 KNIME,  38  or Icy  19  are known in the image data science field as platforms that are extensible using plugin architectures. Such interoperability can be beneficial to all software tools involved and drive innovation. A case in point is KNIME, which wanted to add image processing functionalities to its features set and decided against developing their own from scratch.
ROHs are long stretches of DNA homozygous at each polymorphic position. The proportion of genome covered by ROHs and their length are indicators of the level and origin of inbreeding. Frequent common ROHs within the same population define ROH islands and indicate hotspots of selection. In this work, we investigated ROHs in a total of 1131 pigs from 20 European local pig breeds and in three cosmopolitan breeds, genotyped with the GGP Porcine HD Genomic Profiler. PLINK software was used to identify ROHs. Size classes and genomic inbreeding parameters were evaluated. ROH islands were defined by evaluating different thresholds of homozygous SNP frequency. A functional overview of breed-specific ROH islands was obtained via overrepresentation analyses of GO biological processes. Mora Romagnola and Turopolje breeds had the largest proportions of genome covered with ROH (~1003 and ~955 Mb respectively), whereas Nero Siciliano and Sarda breeds had the lowest proportions (~207 and 247 Mb respectively).
In this study, we analyzed the same 20 European autochthonous pig breeds from nine different countries (Croatia, France, Germany, Italy, Lithuania, Portugal, Serbia, Slovenia and Spain) and three other cosmopolitanderived breeds to obtain genomic inbreeding information from whole genotyping datasets using ROHs and other genomic approaches, which were based on the variance of additive genetic values, on the correlation between uniting gametes and on SNP homozygosity. We also estimated the effective population size (N e ) of these breeds and compared this information with the ROH patterns. We then evaluated the distribution of ROH in the genome of these breeds and identified putative selection hotspot regions that might be originated by the different selection histories and structures of these pig genetic resources.
Genotyping and quality control All pigs (39-55 for each breed; Table  S2 ) were genotyped with the GeneSeek ® GGP PORCINE HD GENOMIC PROFILER version 1 (Illumina Inc), which includes 68 516 SNPs evenly distributed with a median of 25 kb gap spacing. The average genotyping call rate was 0. 94. SNPs were mapped on the SSCROFA11. 1 genome version, following the procedure already described by  Fontanesi et al. (2012 Fontanesi et al. ( , 2014)). Only autosomal SNPs located in unique positions were considered. Genotyping data were then filtered using PLINK software version 1. 9  (Chang et al. 2015). A call rate of 0. 90 and HWE P of 0. 001 were set as thresholds to keep SNPs. Although filtering for MAF is necessary as best practice in most SNP chip analyses, this approach excludes the SNPs that are homozygous for the whole breed; therefore it could cause an underestimation of the coverage in ROHs  (Meyermans et al.
Multidimentional-plot analysis of pig breeds and effective population size The first three dimensions for a MDS plot were obtained using PLINK software version 1. 9 and plotted with the R package 'Scatterplot3d'  (Ligges & Mächler 2003)  to graphically visualize the genetic distances between the 23 pig breeds. Effective population size (N e ) at recent and remote generations was computed using SNP data with the software SNEP  (Barbato et al. 2015). SNEP allows estimation of the historic effective population size by considering the linkage level (in terms of r 2 ) in bins of different widths and the recombination rate; the computation is based on the basic formula: E r 2 À Á ¼ ð1 À 4N e cÞ À1 where the r 2 estimate E(r 2 ) depends on the distance between SNPs in windows and c is the recombination rate, kept at 1 × 10 -8 as default.
2008); and (v)  the difference between the observed and expected numbers of homozygous genotypes (F HOM ). The F hat1 , F hat2 , F hat3 and F GRM GRM coefficients were calculated using PLINK1. 9 with the ported functions of GCTA software version 1. 92  (Yang et al. 2011 ). Among the latter methods, F GRM and F hat1 are influenced by the frequency of alleles in the population and F hat3 takes into consideration the correlation between uniting gametes, which could come from the same ancestor in case of inbreeding. F hat2 and F HOM are influenced by the excess of homozygosity, but do not consider the position of SNPs along the genome. F HOM was computed with PLINK software version 1. 9  (Chang et al. 2015). Pearson correlation coefficients (r) between all evaluated inbreeding coefficients were calculated.
This method always identifies ROH islands as the threshold is defined on a percentile within the breed dataset and does not consider the structure of the population or its level of inbreeding. Considering the problems that these two methods could have, we developed a third method where the identification of the threshold was chosen considering the average S ROH level of the breed, which approximates the genomic inbreeding level of a population (hereinafter called S ROHbased threshold). This method consisted of predicting the threshold after fitting a simple linear model in which the percentile threshold was a function of the average S ROH. The basic model was: y i ¼ β 0 þ β 1 s i þ ɛ i , where y i is the threshold value (minimum number of animals positive for an ROH) obtained using the percentilebased threshold for the i th breed, s i is the S ROH value for the i th breed, β 0 is the intercept term whereas β 1 is the corresponding regression coefficients and e i is the error term.
This matrix was used to compute a similarity matrix D (n × n), whose generic entry d is the Euclidean distance between pairs of breeds with values scaled in the range 0-1. A final dissimilarity matrix (1 -D) was obtained and used to produce a heatmap in R (package corrplot;  Wei & Simko 2017)  showing similarity among breeds. Genes annotated in the SSCROFA11. 1 pig genome version that mapped in the identified ROH islands were retrieved using the ENSEMBL BIOMART tool (http://www. ensembl. org/ biomart/martview/) and from the NCBI SSCROFA11. 1 GFF file. Functional enrichment analysis was carried out with ENRICHR (Chen et al. 2013) via Fisher's exact test. Analyses were carried on the Biological Process branch of the GO  (Ashburner et al. 2000) , by interrogating a total of 5103 functional terms that were covering 14 433 human genes.