dataesr/scientific-paragraphs-categorization huggingface dataset:
• Define generic monitoring data insertion workflows (GMDIW). The second profile is called CODVerif-Specialist (bottom part of Figure  4 ). Users having this profile aim to perform the following activities: 1. Select the object, the attribute, or the reference to be monitored from the target data model. 2. Associate the selected attribute, reference, or object in the data model with one or multiple GMDIW scenarios to enforce data coherency rules or the imposed naming conventions. 3. Generate the individuals in the MDIWO ontology. 4. Determine whether generating the negations and the composition of the selected scenarios is necessary. 5. Generate MDIW instances and deploy them. The third profile of CODVerif is users populating data stores that are guided by CODVerif MDIW instances. CODVerif-Admin profile uses a portlet that accesses Web-Protégé  (Tudorache et al.
For example if we assume that we have 9 accounts in our CRM and each account could have at most 16 contacts playing different roles for that account, then a contact whose name ends with the suffix 105 will bring the two following pieces of information: • ID of the role of the contact in the account = ( 105 1 16 + ( ) mod = 10 • ID of the account to which the contact belongs = Int 1105 1000 16 1 -( ) ( ) + / = 7 Thus, a complex rule is that contacts shall end with a number capturing the two pieces of information above. The hypothesis that is evaluated is that it is beneficial for CRM users and users who populate general data to be guided to uphold the naming convention rules and produce high-quality data. For this reason, we need some criteria that will reflect this issue. The criteria that will be used for evaluating CODVerif should examine whether we address the aforementioned challenges.
Evaluation Case Our test sample comprises three groups of university master students who have joined a CRM course and who are to accomplish different exercises on various CRM features. The exercises required populating data in the CRM. To avoid introducing any bias when evaluating CODVerif, we have put no focus on following the naming convention. The subject was presented normally. The CRM exercises assigned to the students have avoided batch data insertion. We believe it is easier for users to follow the naming convention rules when they populate data in batch mode. The three groups, which do not know each other, were split into: • The first group populated CRM data while they were asked to follow the naming convention in the exercise. However, CODVerfi workflow instances were not deployed.
Paniagua et al. (2019)  also addressed the naming of services in an SOA but not the naming of inputs and outputs of services in an SOA. Harding and Bayliss (2022)  linked drug databases and naming convention policies to enforce interoperability between vendors, physicians, and pharmacists. Baijens et al. (2020)  showed the importance of naming conventions for analyzing clinical trial data, which are managed in big data platforms. They noticed that equivalent concepts were named differently, which complexified the clinical trial data analysis. First, this shows the importance of upholding the naming convention rules when populating data stores. Second, this also shows the limits of the a-posteriori data verification, which led to complexified data analysis models. Examples of naming conventions we can cite are  Dhavle and Rupp (205)  and  Dumas et al.
We have also illustrated the importance of having a data store with data upholding the standard's rules or the naming convention in  Alshreef et al. (2017). In this paper, the authors have defined access rights based on the name given to the data. They called their framework NC-RBAC (Naming Convention RBAC). The study developed in  Alshreef et al. (2017)  illustrates the importance of having data that uphold the naming convention and the standard rules. Besides the ambiguity and the wrong usage that can be made with data named wrongly, the access control based on the naming convention better motivates the CODVerif approach to impose the naming convention. In the same cyber security domain, we can cite the vulnerability databases (CNNVD. org. cn and NVD. nist. gov), which contain a variety of vulnerabilities, including different attributes such as the name and vulnerability priority.
The data in these databases follow a naming standard, making vulnerabilities from completely different vulnerability databases available in the same standard. This standard facilitates sharing of vulnerability information  (Jia et al. , 2018). O'Donovan et al. (  2019 ) has defined basic naming conventions to differentiate between cyberphysical interfaces in a fog and cloud architecture. Similarly, García-Holgado and García-Peñalvo (2019) developed a model-driven approach to model technological ecosystems. First, they proposed a meta-model specific to technological ecosystems, and then they defined how it can be used through different phases to generate Platform-Specific Model (PSM) for developing technological learning ecosystems based on Open Source software. The interesting aspect of their work is how they raised the importance of the naming convention when building the meta-model for their domain.
Overall, HPC has been widely used, with demonstrated benefits. Though our intuitive understanding justifies why HPC works, it in no way implies that it is the best transformation that can be done. Are there transformations that can be applied in the same pre-processing way as HPC that would achieve better alignment sensitivity? In this work, we define a more general notion which we call mapping-friendly sequence reductions. In order to efficiently explore the performance of all reductions, we identify two heuristics to reduce the search space of reductions. We then identify a number of mapping-friendly sequence reductions which are likely to yield better mapping performance than HPC. We evaluate them using two mappers (minimap2 and winnowmap2) on three simulated datasets (whole human genome, human centromere, and whole Drosophila genome).
Restricting the space of streaming sequence reductions To discover SSRs that improve mapping performance, our strategy is to put them all to the test by evaluating the results of an actual mapping software over a simulated test dataset reduced by each SSR. However, even with only 16 inputs and 5 outputs, the number of possible g mappings for order-2 SSRs is 5 16 z 1:5,10 11 , which is prohibitive to enumerate. In this section, we describe two ideas for reducing the space of SSRs that we will test. In subsection reverse complement-core-insensitive streaming sequence reductions, we show how the restriction to RC-insensitive mappings can be used to reduce the search space. In subsection equivalence classes of SSRs, we exploit the natural symmetry that arises due to Watson-Crick complements to further restrict the search space.
A more detailed result table can be found in Table  S1 , and a graph of MSR performance on the whole Drosophila genome in Figure  S7. As Figure  S7  shows, we also evaluated these MSRs on a whole Escherichia coli (GenBank: U00096. 2 (Blattner et al. , 1997) ) genome, where we observed similar results, albeit the best MSRs do not seem to be one of our three candidates. This might mean that specific MSRs are more suited to particular types of genomes.
DISCUSSION We have introduced the concept of mapping-friendly sequence reduction and shown that it improves the accuracy of the popular mapping tool minimap2 on simulated Oxford Nanopore long reads. We focused on reads with high mapping quality (50-60), as it is a common practice to disregard reads with low mapping quality  (Prodanov and Bansal, 2020; Li, 2021; Li et al. , 2018). However, across all mapped reads (mapq R 0), HPC and our MSRs have lower mapping accuracies than raw reads, consistent with the recommendation made in minimap2 to not apply HPC to ONT data. Despite this, we newly show the benefit of using HPC (and our MSRs) with minimap2 on ONT data when focusing on high mapping quality reads. Furthermore, MSRs provide a higher fraction of high-mapq reads compared to both raw and HPC, as shown on the human and Drosophila genomes.
ACKNOWLEDGMENTS The authors thank  Kristoffer Sahlin  for feedback on the manuscript. R. C. was supported by  ANR Transipedia, SeqDigger, Inception , and  PRAIRIE  grants ( ANR-18-CE45-0020 ,  ANR-19-CE45-0008 ,  PIA/ANR16-CONV-0005 ,  ANR-19-P3IA-0001 ). This method is part of projects that have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreements No  956229  and  872539. L. B. was also supported by the  ANR   PRAIRIE grant  ( ANR-19-P3IA-0001 ). This material is based upon work supported by the  National Science Foundation  under Grant No. 1453527  and  1931531.
ESA's PLATO mission aims the detection and characterization of terrestrial planets around solar-type stars as well as the study of host star properties. The noise-to-signal ratio (NSR) is the main performance parameter of the PLATO instrument, which consists of 24 Normal Cameras and 2 Fast Cameras. In order to justify, verify and breakdown NSR-relevant requirements the software simulator PINE was developed. PINE models the signal pathway from a target star to the digital output of a camera based on physical models and considers the major noise contributors. In this paper, the simulator's coarse mode is introduced which allows fast performance analyses on instrument level. The added value of PINE is illustrated by exemplary applications.
This noise-to-signal ratio (NSR) is one of PLATO's key performance parameters. PLATO will employ an array of 24 Normal Cameras and two Fast Cameras (hereafter, 'camera' means the telescope optics and the focal plane assembly, including all the ancillary devices like baffling, electronics, etc. ). Each camera is a dioptric telescope with 12𝑐𝑚 entrance pupil feeding four CCDs in their focal plane, each one with 4510 × 4510 18𝜇𝑚 pixels. The 24 Normal Cameras will provide image data for the primary scientific goals. Their CCDs are Te2v CCD 270 full frame type. Each Normal Camera has a 1037 𝑑𝑒𝑔 2 effective field-of-view (FoV). The Normal Cameras are optimized to observe stars fainter than 𝑚 𝑉 = 8 with a cadence of 25𝑠. The 24 Normal Cameras are arranged in four groups of six cameras. All six cameras of each group have exactly the same FoV and pointing.
This results in a 619 𝑑𝑒𝑔 2 FoV. The two Fast Cameras will be equipped with a red and a blue filter, respectively, to image stars bi-spectral in the magnitude range from 4 to 8. These cameras work with a cadence of 2. 5𝑠. The images will be used to answer dedicated scientific questions (e. g. Grenfell 2020) and to provide input for high-precision attitude control of the spacecraft. The main performance driver for the design of PLATO is to achieve a certain NSR value for a large number of stars. The software simulator PINE (PLATO Instrument Noise Estimator) was developed to predict NSR values of stars in the field of view of PLATO. PINE is used to derive, break down and justify instrument requirements, to perform sensitivity studies, optimizations and trade-offs, to support the generation of PLATO input catalogues (PIC, Montalto 2021) which will be the basic input for PLATO's field selection  (Nascimbeni 2022)  and to support verification and validation activities during instrument development, characterization/ calibration and in-orbit commissioning.
PINE PINE is a software simulator written in IDL  (IDL 2020). It considers all relevant instrument parameters and characterizes the signal's pathway from a source (here photons from a star) to a digital measurement value through all camera components, e. g. filter, lenses, CCD, electronics. PINE considers all important environmental conditions, e. g. temperature, stray light, radiation and spacecraft jitter. This paper focusses on performance investigations on Normal Cameras in two standard use cases beginning of life (BOL) and end of life (EOL) with required parameters (the standard instrument parameter sets defined for these use cases). The software can also be applied for different parameter sets (e. g. for typical parameters), for changed configurations (e. g. considering the loss of cameras) and, in independent runs, for Fast Camera simulations.
The required value is marked bold as reference. The plot also allows to estimate the NSR for those stars which are covered just by 6, 12 or 18 cameras simultaneously (see green areas in Figure  1 ). Cadence and exposure time will be the only major camera parameters to be changeable in space. Although increasing the exposure time would in principle improve the NSR, it would affect the sampling frequency of the PLATO light curves and would also increase the number and degree of saturated stars within the FoV. The latter effect will have an impact on the data product quality of other objects too due to blooming effects. Moreover, the approach of increasing the exposure time assumes that spacecraft jitter noise will behave better than required for longer times frames. This option might nevertheless be required in case of a possible loss of some additional cameras.
Acknowledgements Gathering all the know-how, information and data needed for this paper and to keep them consistent was a tough job. It was a big and long-lasting system engineering task and is a masterpiece of collaborative work. Sincere thanks go to all colleagues and supporters from all the institutions and companies even if not mentioned by name. This work presents results from the  European Space Agency (ESA)  space mission PLATO. The PLATO payload, the  PLATO Ground Segment  and PLATO data processing are joint developments of  ESA  and the  PLATO Mission Consortium (PMC). Funding for the PMC is provided at national levels, in particular by countries participating in the PLATO Multilateral Agreement (Austria,  Belgium ,  Czech Republic ,  Denmark ,  France ,  Germany ,  Italy ,  Netherlands ,  Portugal ,  Spain ,  Sweden ,  Switzerland ,  Norway , and  United Kingdom)  and institutions from  Brazil.
Method We use the interview as scoping means to solicit data about how design researchers, sound designers, engineers, expert users conceptualise and use sound in design. First, we derive a model of the topics discussed during the conversations with the four categories of professionals, by making use of Leximancer, 4 software tool for automated content analysis. Then, we interpret the resulting concept map in order to find answer to our research questions, and namely tracing sound embodiments in the verbal accounts of the four categories of interviewees, and stressing their shared and distinctive meaning attribution to sound in relation to their professional activity. We briefly discuss the rationale of our methodological approach, before reporting the study and discussing our findings. Semantic models of sound-driven design.
Automated content analysis Computer-aided Qualitative Data Analysis Software (CAQDAS) have become increasingly popular in social sciences and design  (Woods et al. , 2016). They are used to support the researcher in the understanding of complex phenomena, typically by providing semi-automated coding of video, transcripts, and document data, and by assisting their interpretation by means of interactive visual analytics. Whereas the discussion of existing CAQDAS environments is beyond the scope of this paper, we point out that the choice of the tool implies ontological and epistemological considerations  (Wilk et al. , 2019). Computer-assisted environments for qualitative data analysis can be broadly split in two main categories, that is tools that emphasise and require the researcher's initialisation of the data analytics process (e.
Terms are weighted so that the presence of each word in a sentence provides an appropriate contribution to the accumulated evidence for the presence of a concept. Finally, highly connected concepts are clustered in higher-level groups  (Smith & Humphreys, 2006). The automated analysis does not replace in any way the role of the researcher, instead the aim is to uncover networks and patterns that may have not been visible otherwise. In this respect, we use the theoretical framework discussed in Section 1 as conceptual lens to make sense of the semantic map generated in Leximancer. Similar approaches based on natural language processing have been recently used to characterise ambiguous and vague verbal expressions in design conversations, as linguistic markers of the design space  (Ungureanu & Hartmann, 2021) , and conversely to study design talk by using machine learning to generate text in the style of a design conversation  (Lloyd et al.
Disentangling the Sound and Work clusters The inspection of the data associated to the Work cluster revealed strongly contextualised reflections triggered by Q1, about the impact of sound in the participants' working routine and daily tasks. Two main lines of thought were stressed, on a) sound as socio-technological space  (Dourish, 2006) , and on b) sound as working means: This two-sided connotation is a recurrent refrain in the professionals' narratives. In this respect, the work concept acts as bridge between the Sound and Work clusters. Therefore, to make sense of these two clusters, we looked at the co-occurrence of the sound concept with the other most ranked concepts in the two groups (i. e. , sound-people, sound-use, etc. ), and inspected the corresponding, associated blocks of textual data.
Figures  3a  and 3b  show the co-occurrence of the sound concept with the other most ranked concepts in the Sound and Work clusters, respectively. These are the concepts that mostly travel together when sound is discussed in the data (co-occurrence), and that at the same time are very likely to include sound when they are individually considered (likelihood of the co-occurrence). show a stronger correlation with a reference category. A prominence score > 1:0 indicates that the cluster and the category are not independent Semantic models of sound-driven design.
"I can operate with a much clearer auditory vision, when I'm surrounded by a more peaceful and lower volume environment" (SndDes), "I try to be very mindful of which apps are giving me which sound notifications, [. ] but also to have the right level of notification" (DesRes). "I would like to have a simultaneous understanding of how people interpret and listen to sounds for real. There are some things in the visual world that are universally accepted, whether true or not, we have a language, we have an understanding of what is yellow, what is purple, what is black, etc. I feel that it does really not exist with sound and this drives me nuts. " (SndDes). "For some people abstraction is easy, for others is not. " "I always come back with the experience prototyping idea that you should, as soon as possible let people hear and interact with it, in order to interpret their reaction and statements.
[As a design intention,] "we use listening to build languages, systems of sounds that help to build narratives or can guide the users" (DesRes). [Process-wise, listening is used] "to probe the people who will be using and hearing those sounds", [with the intention of] "reducing misunderstandings and enforce directions during a project" (SndDes). "We use sound measurements, psychoacoustic descriptors and visual means, including spectrograms, to objectively qualify a given problem at a system level" (Eng).
[If I had a magic wand,]" I would create a universal language for us to describe the experience with sound", "I wish there is a way to create a sense of trust with the public" (SndDes). "I would block the really loud sounds in an environment for a second to empower people, sometimes sounds can be so badly designed that they don't just realise it" (DesRes). "I would have my own audiobox with all the technological resources, CPU and algorithms, all the clearance and authorisations to place it in the car systems of top executives and some clients to sketch in context and scale prototypes efficiently" (Eng). Design Studies Vol 83 No. C Month 2022 their verbal accounts stress its complexity and multifaceted nature. Listeningedoing is the active and intentional behaviour by which the professionals access action-sound couplings, form the experience, perceive, sense and associate events, create purposes and attribute socio-culturally situated meanings.
.  Plasma samples Blood samples from healthy donors were collected by the French blood establishment (agreement #16/EFS/031) under French and European ethical practices. Blood samples from women with estrogen receptor-positive (ER+) HER2-negative (HER2-) metastatic breast cancer (MBC) were collected, after written informed consent, in the frame of the ALCINA study (NCT02866149). Eligibility criteria were: patients aged ≥18 years with ER+, HER2-MBC, treated at Institut Curie (Paris and Saint Cloud, France) who progressed under endocrine therapy and for which a treatment with palbociclib and fulvestrant was being initiated. Tumour response to therapy was assessed at least every 3 months and classified, per RECIST v1. 1 criteria, as complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD).
.  Western blot and NTA Western blots (WB) and Nanoparticle tracking analyses (NTA) were performed on ultracentrifugation (UC) pellets (30′ at 100,000 g, TLA-45 rotor) of each SEC elution fraction obtained from 2 ml of healthy plasma, isolated as described above. Pellets were resuspended in 15 µl of PBS, 13 µl were used for WB, and 2 µl for NTA. NTA was performed using ParticleMetrix ZetaView PMX-20 with software version 8. 04. 02. The instrument settings were sensitivity of 75 and shutter of 75. Measurements were done at 11 different positions (five cycles per position) and a frame rate of 30 frames per second. WB were performed using Laemmli sample buffer without reducing agent (BioRad). After boiling 5 min at 95 • C, samples were loaded on a 4-15% Mini-protean TGX-stain free gels (BioRad).
0 fluorometer (Thermo Fisher). Amplifiable cellular cfDNA (cellDNA) was quantified using a quantitative PCR (qPCR) assay targeting LINE1 elements. PCR was performed in a 25 µl total volume with the following components: 20 µM of forward primer (5′AGGGACATGGATGAAATTGG3′), 20 µM of reverse primer (5′ TGAGAATATGCGGTGTTTGG3′), 12. 5 µl of 2X Power SYBR Green PCR Master Mix (Thermofisher), 10 µl of molecular biology grade water and 2 µl of DNA input. A serial dilution of normal human peripheral blood mononuclear cells (PBMC) DNA was incorporated in each plate as a standard. All real-time PCR assays were carried out in a 7900 Fast real-time PCR system (Thermofisher). The reaction was monitored with the following cycling conditions: One cycle at 95 • C for 5 min, followed by two cycles of (95 ). The baseline and the cycle threshold value (Ct) were set using the Thermofisher software.
Amount of mitochondrial DNA (mtDNA) was determined by qPCR for the human ND5 gene using primers from Sansone and colleagues  (Sansone et al. , 2017). To prepare a 25 µl PCR reaction, we used 2X of Power SYBR Green PCR Master Mix (Thermofisher), 20 µM of forward primer (5′TTACCACCCTCGTTAACCCTAACAAA3′) and 20 µM of reverse primer (5′TGGGTTGTTTGGGTTGTGGCT3′), 10 µl of molecular biology grade water and 2 µl of DNA input. A serial dilution of total genomic DNA (including cellDNA and mtDNA), isolated from the MCF7 breast cancer cell line was incorporated in each plate as a standard. DNA was amplified with a Quantstudio 5 Real-time PCR system (Thermofisher) with the following program: One cycle at 95 • C for 10 min, 35 cycles at 95 • C for 15 s, 47 • C for 15 s and 72 • C for 30 s. Results were analysed with the QuantStudio 3 and 5 Real-Time PCR System Software (Thermofisher).