dataesr/scientific-paragraphs-categorization huggingface dataset:
Semio-Participatory Interaction Design Interaction design models digital things for people's use ?. Therefore, interaction design is also related to the first two stages of the generic communication message mentioned above. From this perspective,  Rosa and Matos [2016]  consider that from the communicative perspective presented by SemEng for (human-computer) interaction, interaction design is the construction/manipulation of communication messages. Rosa and Matos [2016]  corroborate that  Baranauskas [2013]  considers interaction design as a social process that designers should not only carry out but also include the users' participation. In this sense,  Rosa and Matos [2016]  suggested that software conception and development are not restricted to designers and engineers. However, they also include the user in the role of design partner, turning this semiotic process into semio-participatory.
SPIDe was conceived as part of the  Rosa [2017]  master's project. The objective was to understand how the interaction design process of digital educational technology can consider the children's cultural aspects of a school environment. Therefore, according to  Rosa [2017] , the initial choice of con-textual inquiry, brainstorm, braindraw, and think-aloud techniques was due to their uses with children. Later,  Pita et al. [2017]  conducted a study to improve SPIDe, considering the participation of visually impaired people in the interaction design of software for urban mobility. In carrying out their research, the authors understood that conducting the contextual inquiry technique left participants feeling embarrassed and feeling that the designers were evaluating them. In this sense,  Pita et al.
Researches around SPIDe Researches carried out by  Rosa and Matos [2016] ,  Pita et al. [2017]  present applications and modifications of SPIDe under (multi)cultural aspects of schoolchildren and the participation of visually impaired people in the process of interaction design, respectively. The expansion of the scope of SPIDe has conjectured the hypothesis about how SPIDe can elicit software requirements. Therefore,  Rosa et al. [2017]  presents a systematic literature review to identify whether there are studies that deal with the techniques that make up SPIDe for eliciting requirements. The researchers found works that apply brainstorm and storytelling to elicit software requirements. Therefore, these results indicate the existence of gaps in contextual inquiry, braindraw, and thinkaloud to elicit requirements.
Planning Given that this article presents an investigation into the feasibility of SPIDe, the following question guided the research: is SPIDe effective for integrated requirements elicitation and interaction design? Effectiveness is understood as doing the right things  [Bourque and Fairley, 2014] , so it is investigated whether SPIDe meets the proposed goal  [Shull et al. , 2001]  using the logbook  [McAlpine et al. , 2017]  naire  [Davis, 1989] , semi-structured interviews  [Lazar et al. , 2017] , and thematic analysis  [Braun and Clarke, 2006]. The case study of this research was a software project for a gospel music band from a Christian church in Salvador/Brazil. The band leader asked the researchers to create software to help the band's musicians carry out their search and storage activities for songs and chords already played by the band or rehearsal.
In this sense, an interview was performed individually with each of the users. The interview consisted of questions about (i) difficulties of participation; (ii) collaboration between participants; (iii) the use of subjective aspects such as culture, values, and previous knowledge; (iv) authorship and co-authorship; (v) wants and needs; and (vi) the perception about SPIDe. During the interviews, the audio was recorded for later transcription and analysis. Qualitative data analysis was performed using the thematic analysis  [Braun and Clarke, 2006]. It is possible to organize and describe a dataset coded and categorized into themes through thematic analysis. For this, the analysis is divided into six steps. Figure  6  presents the thematic analysis process. We analyzed the data collected through open questions from the TAM questionnaire, logbooks, and interviews through thematic analysis.
Discussion, Limitations, and Threats to Validity This investigation's findings explain that developers and users agreed with the effectiveness of the semio-participatory design process. The results show that SPIDe effectively elicits requirements while executing the interaction design process. Hence, it is inferred that its execution can optimize software development, as the process occurs in an integrated way. However, the requirements engineer did not indicate that SPIDe contributed to increased efficiency like others. The emphasis on stating that SPIDe can optimize software development lies in the confluence of the objective of interaction design (in its initial phases) and the requirements elicitation, which is to identify/raise data for software design with the highest adequacy in quality aspects, whether in use or interaction.
Therefore, SPIDe is a helpful tool for the requirements engineer and the developer (designer) to carry out their activities, including a multi-professional team or from the same working area. SPIDe can positively impact software development projects. For example, concerning the requirements elicitation, we indicate that one of the main gains of SPIDe is its non-hierarchical participatory character. It allowed the (intended) user to effectively assume the designer's role without being inhibited due to technical limitations to the software development process since their contributions are essential to identify the software and interaction requirements. SPIDe is a process that is constantly evolving since its conception (see subsection Researches around SPIDe), so in this execution, we identified aspects that need attention.
Through thematic analysis, we detected which characteristics, limitations, and recommendations should be incorporated or modified for/in SPIDe. The case study presented in this manuscript is characterized as a feasibility study and is therefore limited to providing sufficient evidence to justify more studies about SPIDe  [Shull et al. , 2001]. That the semio-participatory design process should evolve into a framework; thus, this study's goal is not to obtain a definitive answer to the research question but to allow the construction of an acceptable body of knowledge for project continuation. In this sense, the study also stands out as exploratory, seeking to recognize some phenomena and identify new research questions and hypotheses to improve SPIDe  [Lazar et al. , 2017; Wohlin et al. , 2012].
In addition to the questions that emerge as a research opportunity for/about SPIDe, through data analysis, we can reiterate that SPIDe is related to the six characteristics/concepts  [Luck, 2018]. SPIDe, as a semio-participatory methodological process, uses techniques that seek to give participants an active voice and resources fairly and democratically, with the realization of activities in their natural context, according to thematic analysis results, allowing the mutual construction of knowledge. With this, it is also possible to establish that SPIDe favors genuine participation as users assume the design partners' role. An essential aspect of SPIDe is that it is noticeable or engaged by the participants throughout its execution. The techniques employed in the contextual analysis and interface engineering phases connect everyone involved in a common objective, the resolution of the proposed problem, or software creation, for example.
The test set of the segmentation module comprised of 2948 glomeruli. For the Oxford classification, 6206 expert-annotated glomeruli from 308 PAS WSIs were labelled for M, E, S, C and split into a training set of 4298 glomeruli from 207 WSIs, and a test set of 1908 glomeruli. We chose the best-performing models to construct an end-to-end pipeline, which we named MESCnn (MESC classification by neural network), for the glomerular Oxford classification of WSIs.
Renal biopsies as whole slide images All biopsies (total of n=386 WSIs) were annotated for glomeruli in QuPath  [11]  with a Cintiq Pro 16-inch pen tablet (Wacom, Düsseldorf, Germany) by an expert nephropathologist (JUB). The biopsies were used to train and validate both segmentation and classification models. The dataset can be divided into biopsies with IgAN (n=102) and without (Other GN; n=284). The Other GN cohort, which reflects the broad range of human glomerulonephritis on PAS WSIs, covering all morphological aspects of IgAN, was used to develop the segmentation models. The IgAN cohort was then used to develop the classification models. A description of the composition of biopsies, WSIs, and glomeruli for each task is reported in Fig. 1. Annotations included sclerosed and non-sclerosed glomeruli but not empty Bowman's capsules.
Glomerular dataset for the classification model A summary of the three different IgAN cohorts for the classification model is reported in Table  4  at glomerular-level. These 102 biopsies, corresponding to 308 WSIs (with 6206 glomerular crops), were divided into a training set of 67 (with 207 WSIs and 4298 glomerular crops), and a test set of 35 biopsies (with 101 WSIs and 1908 glomerular crops) for the classifiers predicting Oxford M, E, S, C labels. Predicted Oxford labels were then translated into predicted biopsylevel Oxford scores following the logic detailed above for M0/M1, E0/ E1, S0/S1, C0/C1/C2. Independently, the expert biopsy-level Oxford scores were determined following the Oxford Classification by an expert nephropathologist (JUB) on the exact same set of WSIs per biopsy. Of note, we only had one biopsy with C2 (more than 25 % active crescents), in our test set, reflecting the low incidence of C2 in clinical practice.
Glomerular segmentation model development Several variants of Mask R-CNN were thoroughly compared to find the most suitable architecture for the glomerular segmentation task. The results were generated tile-wise, collected as such, and then projected onto WSIs. Finally, we integrated the glomerular segmentation module into QuPath as the "QuPath Interface for Glomeruli Segmentation" (QIGS). This module allows to automatically segment glomeruli on PAS WSIs, registering them as separate objects for visualization and editing in a QuPath project. The glomerular crops are readily available for downstream applications like our Oxford classifier or can be exported through the provided interface. The task of object detection involves predicting rectangular bounding boxes around all objects. We combined this task with instance segmentation, delineating each instance of an object with a precise mask, replicating the ground truth expert annotations.
As an improvement over Faster R-CNN and Mask R-CNN architectures, Cai and Vasconcelos proposed Cascade R-CNN  [15] , where a sequence of detectors is trained with increasing intersection over union (IoU) thresholds. Since previous works showed its suitability for glomerular segmentation tasks  [16] , we included also this architecture in our experiments. The models were trained with stochastic gradient descent and the Momentum (SGDM) Optimizer, as set by default in the Detectron2 environment, using a base learning rate of 3e-4, for a maximum number of iterations of 300,000, with 2 tiles composing each batch. To construct the training and validation dataset for Detectron2 models, WSIs were split into tiles of 1024 × 1024 pixels, extracted at × magnification. Overlap between tiles was set to 512 pixels for each axis, so that glomerular areas fitted well into at least one tile.
Inference was realized first on the tiles level. Overlap was considered between consecutive tiles to avoid missing glomeruli placed at the margin of the tiles. To avoid problems of duplicate detection, Non-Max-Area-Suppression (NMAS) algorithm  [17]  was applied to detections projected back in the WSI space. Finally, the results in the WSI space are stored in a QuPath project with the QIGS between Detectron2 and QuPath realised in Python by using the PAQUO library (https://paquo. readthedocs. io/, last accessed April 2, 2023).
Glomerular classification model development For image classification tasks, two types of deep learning (DL) models are widely used: CNNs and more recently vision transformers (ViTs). In the following subsections, the CNN and ViT architectures used in this work are briefly presented. To construct the training and validation datasets for classification, the glomerular images underwent the following pre-processing steps: (i) mask was applied to the glomerular crop to remove pixels outside the glomerular area as defined under expert glomerular segmentation guidelines above; (ii) zero padding was used to render the glomerular crop into a square shape; (iii) image was resized, with no stretch, to × 256 pixels. To introduce variability in the training images seen by the DL models and to reduce the risk of overfitting during the training phase, PyTorch AutoAugmentPolicy.
Convolutional neural networks SqueezeNet. Developed by Iandola et al. , SqueezeNet is a popular CNN architecture known for its small size which makes it faster to train and run while reducing the hardware load. Its architecture is very simple: it is composed of a convolutional layer, followed by several Fire modules, ending with another convolutional layer. Each Fire module is composed of one squeeze convolution layer (with a 1 × 1 filter) followed  Fig. 2. Pie charts representing the M, E, S, C lesion distribution in the dataset collected from the three cohorts. The number of samples for each lesion is reported in Table  4. by an expand layer composed by a mix of 1 × 1 and 3 × 3 convolution filters. SqueezeNet can achieve the same performance as AlexNet with 50 × less parameters  [19]. MobileNetV2.
End-to-End glomerular Oxford Classification pipeline Lastly, we coded an end-to-end pipeline with PAS WSIs as input and Oxford M, E, S, C labels for individual glomerular crops and the Oxford M, E, S, C classification as output on a biopsy level. The overall workflow employed for the development and execution of MESCnn is portrayed in Fig. 4. The full end-to-end pipeline of MESCnn, which generates a spreadsheet report as the output, and takes the WSIs as the input, can be found on GitHub (https://github. com/Nicolik/MESCnn). The shared pipeline also integrates the QIGS module (https://github. com/Nicolik/MESCnn/ tree/main/detection), which exploits the PAQUO library to integrate with the QuPath software. It allows visualization and editing of segmentation results, through a QuPath project with glomerular annotations, and their export for downstream classification stages.
Statistical methods for reproducibility Cohen's kappa was calculated with the scikit-learn (v. 1. 0. 1) Python package's sklearn. metrics. cohen_kappa_score function. Intraclass correlation coefficient (ICC) was calculated with pingouin (v. 0. 5. 3) Python package's pingouin. intraclass_corr function. Specifically, we used ICC  (3, 1) , considering the expert pathologist (JUB) and the respective DL model as two fixed raters.
Discussion In this work, we designed a complete pipeline, which we call MESCnn, for a computer-aided nephropathology system that can reliably segment glomeruli and score M, E, S, C according to the Oxford   Classification for kidney biopsies with IgAN. The first step of this pipeline was achieved with a variant of the successful Mask R-CNN architecture, which delivered satisfactory segmentation results trained on our large and heterogenous dataset, covering three institutions and 11 classes of glomerulonephritis. The segmentation accuracy on IgAN of our best model compared favourably to those of other recently published models  [16, 32, 33]. To achieve this aim, several flavours of models grounded on the powerful Mask R-CNN architecture were trained as reported in Tables  5  and 6. Qualitative results, as the examples reported in Fig.
5 , also demonstrated the viability of the Mask R-CNN-based architectures for the task of identifying the glomerular regions. The second step in the pipeline is the classification step for M, E, S, C lesions on a granular and also on the entire biopsy level, as scores. As we have outlined in the introduction, this work is the second to strictly follow the Oxford Classification, using only PAS sections, only IgAN biopsies and training for all four glomerular scores. Uchino et al. [6]  trained for the same lesions as we did. Nevertheless, their performance was considerably lower than ours on all scores, despite a larger dataset. We have not tried the Inception-V3 architecture they employed, but it is conceivable from benchmarking results, that EfficientNet and ResNet are better suited also for glomerular image tasks.
Conclusion The Oxford Classification for IgAN is the most successful example of an evidence-based nephropathology classification system. The aim of the proposed deep learning approach was to develop a model for the automatic analysis of large biopsy cohorts with perfect reproducibility and minimal human effort. In this study, we showed that features extracted from modern deep network architectures, pretrained on ImageNet, deliver promising results for the replication of expert labels for the glomerular components of the Oxford Classification. The availability of increasingly larger datasets for glomerular lesion classification will help to improve.
Acknowledgments The study was partially funded under the  National Recovery and Resilience Plan (NRRP) , project " BRIEF-Biorobotics Research and Innovation Engineering Facilities ",  Mission 4 : " Istruzione e Ricerca ",  Component 2:  " Dalla ricerca all'impresa ", Investment 3. 1: " Fondo per la realizzazione di un sistema integrato di infrastrutture di ricerca e innovazione ", CUP:  J13C22000400007 , funded by  European Union--NextGenerationEU  and under the  National Plan for Complementary Investments  to the  NRRP , project " D34H-Digital Driven Diagnostics, prognostics and therapeutics for sustainable Health care " (project code:  PNC0000001 ),  Spoke 2 : " Multilayer platform to support the generation of the Patients' Digital Twin ", CUP:  B53C22006170001 , funded by the  Italian Ministry of University and Research.
[8] Weis et al. 2022 M, S Local dataset PAS Training/Validation: first step, 12,253 images (expert CNN + CAM (XAI) [5] nephropathologist); second step, 11,142 images (consensus from three experts); test: 180 consensus images for evaluating CNN Sato et al. 2021 (unsupervised) 68 IgAN patients H&E 2144 glomeruli (1319 images for clustering and training of CNN) CNN (NAS-Net) + [44] Clustering + Score- CAM, Grad-CAM Jaugey 2023 M, E, S, T, C 196 IgAN cases (42 MT Overall, roughly 3546 glomeruli; training: roughly 798; test: CNN et al. training, 66 test, 88 roughly 726; application: roughly 1672 [7] application) Uchino 2020 M, E, S, C 283 cases (218 train/ PAS, PAM Overall, 15,888 glomerular images; model construction with Fine-tuning of et al. validation, 65 test), 4489 PAS and 4744 PAM; performance evaluation with 1704 InceptionV3 [6] including 35 IgAN cases PAS and 1777 PAM.
Background. In metabarcoding analyses, the taxonomic assignment is crucial to place sequencing data in biological and ecological contexts. This fundamental step depends on a reference database, which should have a good taxonomic coverage to avoid unassigned sequences. However, this goal is rarely achieved in many geographic regions and for several taxonomic groups. On the other hand, more is not necessarily better, as sequences in reference databases belonging to taxonomic groups out of the studied region/environment context might lead to false assignments. Methods. We investigated the effect of using several subsets of a cytochrome c oxidase subunit I (COI) reference database on taxonomic assignment. Published metabarcoding sequences from the Mediterranean Sea were assigned to taxa using COInr, which is a comprehensive, non-redundant and recent database of COI sequences obtained both from BOLD and NCBI, and two of its subsets: (i) all sequences except insects (COInr-WO-Insecta), which represent the overwhelming majority of COInr database, but are irrelevant for marine samples, and (ii) all sequences from taxonomic families present in the Mediterranean Sea (COInr-Med).
Four different algorithms for taxonomic assignment were employed in parallel to evaluate differences in their output and data consistency. Results. The reduction of the database to more specific custom subsets increased the number of unassigned sequences. Nevertheless, since most of them were incorrectly assigned by the less specific databases, this is a positive outcome. Moreover, the taxonomic resolution (the lowest taxonomic level to which a sequence is attributed) of several sequences tended to increase when using customized databases. These findings clearly indicated the need for customized databases adapted to each study. However, the very high proportion of unassigned sequences points to the need to enrich the local database with new barcodes specifically obtained from the studied region and/or taxonomic group.
Including novel local barcodes to the COI database proved to be very profitable: by adding only 116 new barcodes sequenced in our laboratory, thus How to cite this article Mugnai F, Costantini F, Chenuil A, Leduc M, Gutiérrez Ortega JM, Meglécz E. 2022. Be positive: customized reference databases and new, local barcodes balance false taxonomic assignments in metabarcoding studies.
INTRODUCTION Metabarcoding became a well-established technique applicable to a wide range of studies  (Taberlet et al. , 2018; Slatko, Gardner & Ausubel, 2018; Ruppert, Kline & Rahman, 2019). It can be applied in different fields such as diet analyses or interaction networks, but its primary field of application is the biodiversity assessment  (Compson et al. , 2020). One of the challenges of metabarcoding is the production of robust datasets, that needs to be addressed both by appropriate experimental design, such as the use of replicates, mock communities and negative controls, the use of different markers and careful selection of primers  (Cristescu & Hebert, 2018; Alberdi et al. , 2019; Zinger et al. , 2019; van der Loos & Nijland, 2021) , coupled with appropriate bioinformatics pipelines adapted to the study design  (Zinger et al.
, 2019). This step leads to a set of validated MOTUs (Molecular Operational Taxonomic Units) or ASVs (Amplicon Sequence Variants) in each sample, ideally free from artefacts like PCR or sequencing errors, chimeras, mis-tagging, or pseudogenes. The second challenge is the description and comparison of the biodiversity based on the outcome of the validated MOTUs or ASVs. This can be achieved either by assigning each ASV to a taxon, or the use of a blind approach where taxon richness and biodiversity are estimated from the number of MOTUs without the need of assigning them to taxa  (Nugent & Adamowicz, 2020; Marques et al. , 2020). This second option can be particularly appropriate for studies targeting geographical regions, or taxa where reference databases are highly incomplete  (Marques et al.
, 2020) , such as the marine environment  (Mugnai et al. , 2021)  or for markers with low unspecific amplifications  (Collins et al. , 2019). However, knowing the taxonomic origin of the MOTUs or ASVs can be valuable if the aim is detecting specific groups of organisms or linking the community to ecosystem functioning, where this approach is often necessary for decisions on conservation measures. That said, in this study we will focus on the taxonomic assignment of ASVs, and the effect of the reference database used for the taxonomic assignment. Metabarcoding can be based on one or several markers or even a complete mitochondrial or chloroplast genomes obtained by genome skimming  (Coissac et al. , 2016). The selection of the molecular markers for each study depends on several factors, including the variability of each marker in the target taxa, and the availability of the reference database(s), if the ASVs are needed to be assigned to taxa.