dataesr/scientific-paragraphs-categorization huggingface dataset:
As of  [32] , these key requirements lead to the three specific design characteristics IT-driven value co-creation, closed-loop design and context-awareness. Cong et al. [32]  attempt to map existing design methods to these three characteristics, with the net result that a fundamental design methodology for SPSS is still missing. In particular, existing design frameworks do not cover design-level adaptations according to the specific context. Based on their analysis of the hybrid concerns of value co-creation by design, Zheng et al. [243]  propose a generic system architecture for Smart PSS that follows the DIKW (data-information-knowledgewisdom) model  [8]. While this architecture reflects the needs of hybrid design  [63]  on a macro-level, it helps point out the needs for a re-thinking of existing design methods on a micro-level  [243].
To address these challenges in an incremental rather than disruptive approach, several works have focussed on specific value propositions that can be leveraged through the adoption of a SPSS design mind set rather than a purely product-focused one. Examples are reconfigurability and upgradeability, e. g. [20, 74, 152]. Others such as  [5, 26, 66 ] take a more user-centric approach to focus and prioritize SPSS design efforts. Key is the systematic analysis of service value through the integration of user experience evaluation in the design process, as well as the feedback of in-use data to foster learning for future design iterations. The required co-design of products and services, as well as the increasing integration of digital design elements, leads to a more complex process of requirements elicitation  [229] , specification, interaction  [194] , evolution, analysis  [57, 58, 106]  and validation  [46] , as well as management throughout the design process  [241].
IPS 2 and data analytics and visualization There are significant opportunities in the perspective of enhancing value generated from data. Huang et al. [65]  evaluate the role of product tracking and tracing with triggering and delivering service. They highlight potentials with blockchain and offer an initial application in the context of IPS 2. As a benefit, they indicate that blockchain inherently creates a synchronized database of all transaction at each node while smart contracts allow for responsive action. Rondini et al. [166]  focus on the early design phases and propose a method that combines two existing approaches: The Engineering Value Assessment and Provider Value Evaluation methods. This is composed of two steps, the first pursuing the identification of valuable concepts from the customer and provider perspectives; the second pursuing an individual analysis of the components available for use in the concept selected.
As summarized from a recent exhaustive literature review in  [170] , few things have been published about how such IIoT-enabled ecosystems can be created successfully. Instead, the potentials of value creation through the exploitation of data collected have widely been analysed, in particular in the context of asset monitoring and control applications. Another review points out the lack of research addressing feedback of data acquired via IIoT to IPS 2 design, which could be realized by PLM software  [178]. In the logistics domain, providing smart logistics services based on the IoT gradually leads to the implementation of the Physical Internet (PI). The PI is a concrete, pragmatic paradigm of a collaborative and coopetitive logistics system. The term was coined in 2010 to emphasise the essence of full interconnectivity, intelligence, and interoperability for resource sharing based on modularisation and standardisation of tools, facilities, and business processes  [147].
RESULTS The common ancestor of Colletotrichum parasitized eudicots and specific lineages jumped independently to monocots. In this study, we present a comparative genomic analysis of 30    1 ). While a large variation at the genus level was already reported  23  (more than 50% in our dataset) these results highlight an unexpected variation of more than 30 Mb (39%) between closely related species such as C. cuscutae and C. paranaense. These two species belong to the Acutatum species complex and have been recognized as separate taxa only recently. As a general trend, species with bigger genomes are characterized by a lower GC content. This evidence coupled with the lack of correlation between genome size and number of predicted genes suggest that the genome size in Colletotrichum is mainly correlated with the amount of non-coding DNA (Figures  1  and 2 ).
A previously described laccase (lac2), which is involved in melanisation in appressorial cells of C. orbiculare  25  , is categorized as a member of family AA1 without a subfamily division, whereas a C. orbiculare lac1 which does not have a role in melanin biosynthesis or pathogenicity  25  , is catalogued to AA1_3. For three of the species, C. acutatum, C. higginsianum and C. graminicola, growth profiles on plant biomass related substrates are available in the FUNG-GROWTH database  26. Comparison of the CAZome of these three species (Suppl7_CAZYmes_PBD. xlsx) to their growth profiles did not provide clear correlations. Growth on xylan, galactomannan (guar gum) and inulin is relatively poor for C. higginsianum compared to the other two species, but no strong reduction in xylanolytic, mannanolytic or inulinolytic genes can be found in its genome.
Results highlighted a strong specific response by the four species, as the majority of the DEGs are not shared between the four genomes but are specific for each organism. Comparative analysis of enrichment profiles highlighted five terms enriched among overexpressed genes in eudicot pathogens on ECW (condition 7), all of which (GO:0000981, GO:0006355, IPR001138, IPR036864, PF00172) are associated with Zn(2)-Cys(  6 ) fungal-type DNA-binding domain and transcription regulation. As no other enrichment was identified we performed a manual annotation of genes identified in Figure  3D. As a result, more than one third of all genes identified (32/112) were assigned to three major groups, i. e. , transporters, CAZymes and transcription factors. We identified ten orthologous genes overexpressed in the presence of glucose compared to plant cell walls (condition 0).
In our dataset at least three different events of host jumps and specialization to monocots were detected, the first when species belonging to the Graminicola complex diverged from those belonging to the Spaethianum complex around 25. 23 mya (42. 71 -14. 91), the second when C. orchidophilum diverged from the common ancestor of species belonging to the Acutatum complex around 14. 58 mya (23. 89 -8. 89) and the third event when C. phormii diverged from the closely related species C. salicis around 3. 45 mya (6. 55 -1. 82). All members of the Graminicola complex are pathogenic to species belonging to the Poaceae. However, while most of the species can infect plants belonging to the Panicoideae subfamily (PACMAD clade), C. zoysiae is pathogenic to Zoysia tenuifolia which belongs to the Chloridoideae subfamily (PACMAD clade) and C.
Among these genes, 14 encode for redox enzymes with the highest enrichments from AA2 (heme-containing peroxidases) and AA5 (copper radical oxidases, CROs). Under cellophane infectious hyphae, high expression of GH7, AA9, PL1 and CBM1 family members was also detected. As in our study only a single time point was analyzed, this could explain the absence of the induction of some of these genes in our results. Previous studies have reported gene duplications within this gene family in species characterized by a broad host range  21, 22. Interestingly, different members of the CAZy family GH43 have been identified in three different conditions. Both of these results suggest that the GH43 may be an important family for plant substrate interaction and/or degradation in Colletotrichum species. The expression of the transcription factors (TFs) and other regulatory genes of C.
Phylogeny and divergence date estimation The proteomes were clustered with OrthoFinder v0. 4 66 and the clusters were analyzed with Mirlo (https://github. com/mthon/mirlo) to identify the 500 most phylogenetically informative single copy gene families. The families were aligned with MAFFT 7 67 and then concatenated. A substitution model and its parameter values were selected using ProtTest 3. 4  68. A phylogenetic tree was reconstructed using Bayesian MCMC analysis from the concatenated alignment of the 500 genes identified with Mirlo under the WAG + I evolutionary model and the gamma distribution calculated using four rate categories and homogeneous rates across the tree. The posterior probability threshold was 50%. A selection of 126 genomes covering the Pezizomycotina plus the genome of Saccharomyces cerevisiae as an outgroup were selected from the MycoCosm database (Supplementary 1) and analyzed.
The calibrated tree was inferred by applying the RelTime method  69, 70  to the supplied phylogenetic tree whose branch lengths were calculated using the Ordinary Least Squares method using MEGA X v10. 1. 7  71. The timetree was computed using 5 calibration point (3 fossil records and 2 estimated constraints): 1: Paleopyrenomycites on the crown group of Pezizomycotina, thus assuming the common ancestor of all filamentous, sporocarp-producing Ascomycota (Pezizomycotina) to be 400 mya  [72] [73] [74]   The Tao method was used to set minimum and maximum time boundaries on nodes for which calibration densities were provided  79. The evolutionary distances were computed using the Poisson correction method  80  and are in the units of the number of amino acid substitutions per site. This analysis involved 127 amino acid sequences and total of 124023 positions in the final dataset.
Annotation and evolution of specific gene categories Proteins that are transported out of the cell and into the extracellular space were identified with SignalP-4. 1  81. Protein domains were annotated using Pfam 82 and InterPro 83 and mapped to Gene Ontology terms GO terms 84 , CAZymes were annotated using CAZy pipeline  85. Peptidases were annotated with the MEROPS database, a hierarchical, structure based classification for peptidases, organized into families and clans (https://www. ebi. ac. uk/merops/)  86. BLASTp  87  and RunIprScan (http://michaelrthon. com/runiprscan/) results were used to manually identify genes encoding enzymes that are signatures of backbone secondary metabolite (SM) genes in the Ascomycota 88 : nonribosomal peptide synthetases (NRPS; IPR010071, IPR006163, IPR001242), polyketide synthases (PKS; IPR013968), DMATS-family aromatic prenyltransferases (IPR017795, Pfam PF11991), and terpene synthases/cyclases (IPR008949).
Transcription factors were identified using BLASTp against NCBI non-redundant protein sequences (nr) database and the Aspergillus Genome Database (AspGD)  89. P value of 1e-10 was used as cutoff in both cases. NCBI conserved Domains Database(CCD) and EMBL Simple Modular Architecture Research Tool (SMART) (https://smart. embl. de)  90 were used to manually assign putative function(s) to uncharacterized transcription factors. Cys6Zn2 and Cys2His2 regulators were also analyzed by phylogenetic analyses (NJ) using orthologs of all kingdoms of known regulators involved in plant biomass degradation 2. ortholog gene(s) of each species in each specific comparison. The variable expression means more than one gene were included in the ortholog group of a specific species and the expression of these genes show different expression trend in comparison.
Only all the orthologs of a species show the same highly or lowly expression in glucose were defined as differentially expressed gene. The no differentially expression means the gene expression show no significant difference between glucose and other two carbon sources. The absence means the ortholog gene were not detected for specific species. These categories were coded with -2, -1, 0, 1, 2 respectively, and were visualized with heatmap using R package "gplots", with the complete-linkage clustering method and Euclidean distance. For each CAZy family, we did similar analysis. Instead of summarizing the expression difference between glucose and other carbon sources to different categories, the numbers of genes of highly or lowly expressed in glucose condition were visualized in the heatmap.
Comparative transcriptomics A custom script orthoexpress. pl was developed to combine Proteinortho v5. 16b  92  and expression profiles in order to identify groups of genes showing specific expression profiles. Recent duplications were manually checked. In case of different behavior of paralogs both forms of the (co-)orthologous groups were analyzed independently. Seven logical conditions (Table  4 ) were established to identify genes differentially expressed in specific organisms/conditions.
2. 2 (https://github. com/PacificBiosciences/FALCON), improved with finisherSC version 2. 0 , and polished with Quiver version smrtanalysis_2. 3. 0. 140936. p5 (https://github. com/PacificBiosciences/GenomicConsensus). For the other seven strains (C. cereale, C. eremochloae, C. sublineola, C. falcatum, C. caudatum, C.
3. 2 60 (C. eremochloae, C. sublineola, C. falcatum, C. somersetensis, C. zoysiae) or Trinity ver. 2. 1. 1 61 (C. cereale, C. navitas, C. caudatum, C. godetiae, C. phormii, C. acutatum s. s. and C. lupini). C. abscissum, C. cuscutae, C. tamarilloi, C paranaense, C. costaricense, C. melonis were sequenced using Illumina HiSeq 2500 Rapid-PE 250bp sequencing technology by the McGill University and Genome, Quebec Innovation Centre (Canada). Paired reads were assembled using SPAdes v3. 8. 2 62. Scaffolds were filtered for errors and contaminations based on low coverage value. High coverage fragments were manually checked by blastn against the nr database and scaffolds belonging to the mitochondrial genome and to the ribosomal cluster were masked. Analysis of genome completeness BUSCO v3 63 (Benchmarking Universal Single-Copy Orthologs) was used to search the selected genomes for 3725 Sordariomycete orthologous genes (sordariomyceta_odb9 data set) to assess the completeness of the genome sequences.
ACKNOWLEDGMENTS This research was supported by funds from the  Ministerio de Ciencia Innovación y Universidades of Spain  ( AGL2015-66362-R ) and grants  RTI2018-093611-B-I00  and  PID2021-125349NB-100  from the  Ministerio de Ciencia e Innovación (MCIN) of Spain   AEI/10. 13039/501100011033  and the  European Regional Development Fund (ERDF). The work (proposals:  10. 46936/10. 25585/60000617and 10. 46936/10. 25585/60000725 )conducted by the  U. S. Department of Energy Joint Genome Institute  (https://ror. org/04xm1d337),a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  operated under Contract No. DE-AC02-05CH11231. EB was supported by a grant of the  Dutch Technology Foundation STW, Applied Science division of NWO , and the  Technology Program  of the Ministry of Economic Affairs  016.
volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume.
For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of postoperative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.
Related Study From the fast-growing development in the field of deep learning, convolutional neural networks have demonstrated impressive performance in various segmentation tasks and benchmark challenges, with the added-value of being fully automatic and deterministic  (16). Regarding brain tumor segmentation, performances have specifically been assessed on the Brain Tumor Segmentation Challenge (BraTS) dataset  (17, 18). Occurring every year since 2012, the challenge focuses on gliomas (i. e. , HGGs and LGGs) and has reached a notable cohort size with a total of 2,040 patients included in the 2021 edition, and multiple MR sequences included for each patient (i. e. , T1c, T1w, T2, FLAIR). Segmentation performance has been assessed using the Dice similarity coefficient and the 95th percentile Hausdorff distance (HD95) as metrics  (19).
The current stateof-the-art is an extension of the nnU-Net architecture  (20)  with an asymmetrical number of filters between the encoding and decoding paths, the substitution of all batch normalization layers by group normalization, and the addition of axial attention  (21). An average Dice score of 85% together with a 17. 70 mm HD95 were obtained for the enhancing tumor segmentation task in high-grade gliomas. The segmentation of other brain tumor types has been sparsely investigated in the literature in comparison, possibly due to a lack of open-access annotated data, as illustrated by recent reviews or studies investigating brain tumor segmentation in general  (22, 23). Grovik et al. used a multicentric and multi-sequence dataset of 165 metastatic patients to train a segmentation model with the DeepLabV3 architecture  (24, 25).
The best segmentation results were around 79% Dice score with 3. 6 false positive detections per patient on average. Other prior studies have focused on using variations of the DeepMedic architecture  (26) , using contrast-enhanced T1weighted MRI volumes as input, to train their segmentation models  (27, 28). Datasets were of a similar magnitude with around 200 patients. However, in both cases the test sets were limited to up to 20 patients, making it difficult to assess the generalization ability of the trained models in the absence of cross-validation studies. Obtained average Dice scores over the contrast-enhancing tumor were approximating 75%, with almost 8 false positive detections per patient. From a recent review on the use of machine learning applied to different meningioma-related tasks using MRI scans  (29) , more than 30 previous studies have investigated automatic diagnosis or grading but only a handful focused on the segmentation task.
In addition, the datasets' magnitude used for segmentation purposes has been consistently smaller than for the other tasks, with barely up to 126 patients in the reported studies. Laukamp et al. reported the best Dice scores using well-known 3D neural network architectures such as DeepMedic and BioMedIA, though at the expense of heavy preprocessing techniques the likes of atlas registration  (30, 31). In a previous study, we achieved equally promising performance using an attention-based U-Net architecture, reaching an average Dice score of up to 88% on contrast-enhanced T1-weighted MRI volumes  (32). In addition, the cross-validation studies performed over up to 600 patients with a wide range of tumor sizes, coming from the hospital and the outpatient clinic, exhibited a proper ability to generalize from the trained models.
To summarize, with the exception of the BraTS challenge, there is a dearth of high-quality MRI datasets for brain tumor segmentation. Furthermore, open-access pretrained models and inference codes are scarce and can be cumbersome to operate, hence hindering the generation of private datasets for brain tumor segmentation tasks. On the other hand, open-source tools are being developed to assist in image labeling and the generation of AI models for clinical evaluation, such as MONAI Label  (33)  or Biomedisa  (34). Yet, they do not integrate nor provide access to the latest and highest performing brain tumor segmentation models from the literature, or provide only semiautomatic methods hence requiring manual inputs from the user. From a validation standpoint, the focus has been on reporting Dice scores and often Hausdorff distances, while many other meaningful and possibly more relevant metrics exist and could be investigated to better highlight the strengths and weaknesses of the different segmentation methods  (35, 36).
Contributions While research is exceedingly ahead for glioma segmentation under the aegis of the BraTS challenge community, the segmentation of meningiomas and metastases is trailing behind. In addition, validation studies in the literature have too often been dominated by Dice score reporting and a broader inspection is essential to ensure clinical relevance. Finally, the outcome of this research is often not readily available, especially for the intended end-users who are clinicians without programming experience. As such, the contributions of our study are: (i) the training of robust segmentation models for glioblastomas, lower grade gliomas, meningiomas, and metastases assessed using a panel of more than 20 different metrics to better highlight performance, (ii) the development of two software solutions enabling easy and fully automatic use of the trained models and tumor features computation: Raidionics and Raidionics-Slicer, and (iii) open-access models and source code for the software and validation metrics computation.
DATA For this study, four different datasets have been assembled, one for each main tumor type considered: glioblastoma, lower grade glioma, meningioma, and metastasis. The tumor type was assessed at the time of surgery, when applicable, following the currently applicable guidelines (i. e. , either  (38)  or  (39) ). Tumors were manually segmented in 3D by trained raters using as support either a region growing algorithm  (40)  or a grow cut algorithm  (41, 42) , and subsequent manual editing. Trained raters were supervised by neuroradiologists and neurosurgeons. On contrast-enhanced T1-weighted scans, the tumor was defined as gadolinium-enhancing tissue including non-enhancing enclosed necrosis or cysts. On FLAIR scans, the tumor was defined as the hyperintense region. The four datasets are introduced in-depth in the subsequent sections.
Tumor Segmentation The architecture selected to train segmentation models for each brain tumor type is AGU-Net, which has shown to perform well on glioblastoma and meningioma segmentation  (32, 44). In the following, the different training blocks are presented with some inner variations specified by roman numbers inside brackets. A global overview is provided in Table  2  summarizing used variants. Architecture: Single-stage approach leveraging multi-scale input and deep supervision to preserve details, coupled with a single attention module. The loss function used was the class-averaged Dice loss, excluding the background. The final architecture was as described in the original article with 5 levels and  [16, 32, 128, 256, 256]  as convolution blocks. Preprocessing: The following preprocessing steps were used: 1.