dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunophenotypic data acquired at different MRD follow-up time points from four AML patients with different genetic abnormalities who were in cytological remission were used, including one MLL-r AML case, one t(8;21) AML case, one t(15;17) AML case, and one inv(16) AML case. Compass database-guided analysis was compared against quantitative reverse transcriptionpolymerase chain reaction (qRT-PCR). The phenotypes of leukemic blasts obtained from new patients were compared against the phenotypes of AML blasts from four different cytogenetic groups and the most similar my-HPCs using the three databases. AML blasts were selected according to CD45 +low expression, CD117, HLA-DR positivity, and side scatter (SSC) characteristics, as previously described  (7). Several exclusion gates were applied to avoid the inclusion of undesirable events, such as CD11b+ hypogranular neutrophils and basophils (tube 1), CD14 +low granulocytes and CD14 + IREM-2 + monocytes (tube 2), and CD10 + hematogones (tube 1) (Figure  3 ).
The AML blast events identified in each individual AML case were compared with each well-classified AML group and normal my-HPC populations using balanced APS plots. The similarity between any two populations was scored based on the position of the median for the new AML blast events from A B C FIGURE 2 | The analysis strategy for the identification of normal myeloid-committed HPCs using Tubes 1-3 of the EF AML/MDS panel. The discrimination of normal my-HPCs was performed by Infinicyt 2. 0 software, based primarily on the backbone markers CD117, human leukocyte antigen-DR (HLA-DR), and CD45. Several exclusion gates were used to avoid the inclusion of undesirable events that may fall into the blast gate, such as CD11b + hypogranular neutrophils and basophils (Tube 1), CD14 +low granulocytes and CD14 + CD300e (IREM-2) + monocytes (Tube 2), and CD10 + hematogones (Tube 1).
Bivariate dot plot histograms illustrating the HPCs committed toward a neutrophil lineage (CD45 +low/int CD117 + CD34 +/-HLA-DR +int CD13 + CD14 -IREM2 -CD33 + CD36 -; orange dots), a monocyte lineage (CD45 +low/int CD117 + CD34 +/-HLA-DR +high CD64 + CD14 -CD300e/IREM-2 -CD13 + CD33 + ; green dots), or an erythroid lineage (CD45 +low/-CD117 + CD34 +/-HLA-DR +low/int CD36 + CD105 + CD33 -CD35 +low ; dark-red dots). The other bone marrow cells are displayed in gray. each case relative to the 1 and 2 standard deviation (SD) curves for the AML groups and normal my-HPCs included in the database: falling within 1 SD was scored as 1 point (Figure  4A ); falling within 1-2 SDs was scored as 0. 5 point (Figure  4B ); and falling outside of 2 SDs was scored as 0 points (Figures  4C, D ). After comparison with the AML phenotypes in the databases, the new AML cases were classified as follows: Typical, clearly belonging to an AML group was used to described AML blast events that fell within 1 or within 1-2-SDs of a single AML group and outside of 2 SDs for all other groups based on the results of at least two of the three tubes from the EF AML/MDS panel (Figure  S1A ).
AML blasts (red dots), other singlet events (gray dots). AML, acute myeloid leukemia; AML-MRC, AML with myelodysplasia-related changes; APL, acute promyelocytic leukemia; AML NOS, AML, not otherwise specified. a Additional details can be found in Table  S1. highest score obtained after combining the scores obtained for each of the three tubes of the EF AML/MDS panel (Figure  S1B ). Other was used to describe AML blast events that fell outside of 2 SDs for all AML groups or within 1-2 SDs of any AML group based on the results of only one of the three tubes from the EF AML/MDS panel (Figure  S1C ). The Compass database-guided results were compared with the final diagnoses for all suspected AML cases, which were established using a combination of morphological aspects, karyotypes, FISH analysis, and molecular biology data, according to WHO recommendations  (1).
The results of comparisons between database-guided classifications and the final clinical diagnoses were classified as follows: true positives, when the AML group indicated by the Compass database-guided approach coincided with the final diagnosis; false positives, when the AML group indicated by the Compass database-guided approach was not confirmed by other diagnostic tests; false negative, when a final diagnosis was made in favor of an AML group featuring a genetic abnormality that was not identified by the Compass database-guided approach; and true negative, when the Compass database-guided approach did not identify an immunophenotype corresponding to any AML groups with recurrent genetic abnormalities, and no FISH or PCR tests detected any of the genetic abnormalities assessed in the study.
Database-Guided DfN We sought to evaluate whether the database-guided analysis could be used to identify DfN immunophenotypes during AML follow-up. We evaluated immunophenotypic data acquired at different MRD follow-up moments from four AML patients with different genetic abnormalities, including five time points for one with MLL-r AML, eight time points for one with t(8;21) AML, four time points for one with t(15;17) AML, and six time points for one with inv(  16 ) AML. Nondebris, singlet, CD117 + HLA-DR + my-HPCs were selected from each of the fcs files analysed for each patient, as described above (Figure  2 ). Events were evaluated for inclusion into one of the following groups, based on comparison with the developed Compass databases: normal my-HPCs, AML t(8;21), AML inv(  16 ), AML t(15;17), and MLL-r AML (Figures  S2-S5 ).
Prospective Validation Study The resulting AML databases and the database-guided analysis tool were validated on 125 consecutive AML-suspected cases. The results were compared with the final diagnosis according to WHO 2016 guidelines that were established by each center. Overall, the distribution of the 125 cases across the various AML groups was as follows: 15 (12%) t(15;17) AML; 7 (5. 6%) t(8;21) AML; 6 (4. 8%) inv(16)/t(16;16) AML; 8 (6. 4%) KMT2A(MLL) AML; 88 (70. 4%) other AML; and 1 (0. 8%) non-AML. The distribution across the database-guided result categories (typical, atypical, or other), based on the scoring algorithm described in the Materials and Methods section, is shown in Table  2. When combining the Compass database-guided results obtained from the first three tubes of the EF AML/MDS panel, a true positive result was obtained in 33 (26.
4%) cases, a true negative result was obtained in 67 (53. 6%) cases, a false-positive result as obtained in 24 (19. 2%) cases, and a false-negative result was obtained in one (0. 8%) case. The Compass database-guided diagnosis allowed for correct classification with an AUC of 0. 83 (95% confidence interval (CI): 0. 75-0. 89; p < 0. 001; Figure  5 ). Overall, this method was able to exclude AML associated with t(8;21), t(15;17), inv(16)/t(16;16), and KMT2A(MLL) translocation with 92% sensitivity (95% CI: 78. 6%-98. 3%), a 98. 5% negative predictive value (95% CI: 90. 6%-99. 8%), and a likelihood ratio for a negative test of 0. 04 (95% CI: 0. 01-0. 28). The negative predictive value was 100% for t(15;17) and t(8;21) AMLs; and 99. 1% (95% CI: 95. 2%-99. 9%) for inv(16)/t(16;16) and KMT2A(MLL) AMLs (95% CI: 94.
The morphological examination revealed the presence of undifferentiated blasts, and the immunophenotypic evaluation ruled out the t(15;17) AML phenotype. The Compass database-guided analysis guided the diagnosis toward the MLL-r AML group. Genetic and molecular tests confirmed the absence of t(15;17)(q24;q21) or PML/RARA rearrangement; however, the karyotype and FISH analyses did not reveal the presence of an MLL rearrangement, and an arraycomparative genomic hybridization (CGH) analysis was performed. A PICALM/MLLT10 (CALM-AF10) fusion gene was identified. Cases were the median of AML blast events fall within the 1 SD or 1-2 SDs of two or more AML groups; the assignment in a group being realized on the highest score obtained by summarizing the individual scores obtained in each of the three tubes.
Contribution of Markers to the DfN Identification The discriminatory power to distinguish between the different AML groups and normal my-HPCs included in the databases was assessed for the performance of DfN analysis using the Compass database-guided analysis. Tested antibody combinations were evaluated using balanced APS plots, using all eight markers and the FSC and SSC parameters for each tube of the EF AML/MDS panel (Figure  8 ). Antibody combinations from the three tubes only provided a clear distinction between the blasts from different AML groups was for the t(15;17) AML group. Interestingly, a good separation was also observed between t(15;17) AML blasts and normal neutrophil-committed CD13 + HPCs (tube 1 of the EF AML/ MDS panel), normal monocyte-committed CD64 + HPCs (tube 2 of the EF AML/MDS panel), and normal erythroid-committed CD36 + CD71 + CD33 -HPCs (tube 3 of the EF AML/MDS panel).
Compass Database-Guided DfN-MRD Evaluation in AML With Recurrent Genetic Abnormalities In our study, the aberrant expression of lymphocytic markers on AML blasts was observed in a small number of cases: CD56 expression was observed in 13% of t(15;17) AML cases; the coexpression of CD19 and CD56 was observed in 28% of t(8;21) AML cases; the partial and low expression of CD19 was observed in 42% of t(8;21) AML cases; CD4 expression was observed in 7% of inv(16)/t(16;16) AML cases and in 18% of MLL-r AML cases; and CD7 expression was observed in 9% of all AML cases. Thus, performing MRD follow-up using these phenotypic aberrancies would only be applicable to a limited number of AML cases. Therefore, we evaluated the utility of applying the Compass database-guided analysis based on the first three tubes of the EF AML/MDS panel to the detection of residual blasts in four AML cases harboring the most frequently detected genetic abnormalities.
Compass Database-Guided Analysis for the Detection of APL Residual Blasts by MFC Versus qRT-PCR Evaluation Although the qRT-PCR evaluation of the PML-RARA fusion transcript of 10 -4 was more sensitive, and a difference of 1 log 10 was noticed between the qRT-PCR and those obtained for the MRD evaluation by MFC (Figure  S2 ), the clinical relevance of these results was the same according to ELN MRD Working Party consensus, which recommended a threshold of 0. 1% to distinguish "MRD-positive" from "MRD-negative" patients by MFC (4).
Compass Database-Guided Analysis for the Detection of inv(16) Residual Blasts by MFC Versus qRT-PCR Evaluation The interpretation of DfN MFC data was most difficult for the inv(  16 ) AML case because the isolation of events corresponding to residual blasts required the evaluation of multiple clusters on t-distributed stochastic neighbor embedding (t-SNE) graphs. In addition, in this case, we noticed discordances between the results obtained using the different EF AML/MDS tubes at the same time points (Figure  S5 ). Despite these complications, overall, a good correlation was observed between the results obtained from the qRT-PCR and detection by MFC for identifying CBFB-MYH11 and MRD.
However, the discriminatory potential for this panel to differentiate between AML blasts and normal my-HPCs, in addition to the utility of the panel for AML blast classification across different cytogenetic groups, remains poorly explored. Novel software for MFC data analysis has been developed by the EF group, which includes several analysis tools to facilitate phenotypic comparisons between pathological cells derived from different groups of diseases, with the ultimate aim of facilitating the performance of fast, objective, and reproducible diagnostic assessments. A database-guided analysis of MFC outcomes is capable of objectively analyzing all single leukemic events and classifying each event individually, providing a global image of the composition of the leukemic bulk, which can frequently be heterogeneous in AML  (16).
This method performs accurate, simultaneous measurements of the mean fluorescence values from eight different markers, and two scatter parameters (FSC and SSC) represent a more objective approach than expert-based interpretations, which considers arbitrary categorical classifications of negative versus positive and low versus bright patterns of marker expression for the dominant leukemic population  (17). The Compass database-guided analysis, which is based on a PCA algorithm, has been demonstrated to be effective in various studies, such as for acute leukemia orientation (  16 ), B-cell chronic lymphoproliferative disorder classification  (18) , multiple myeloma diagnosis and monitoring  (19) , B-cell acute lymphoblastic leukemia follow-up  (20) , and MRD assessment in older patients with AML  (21).
In this study, we sought to evaluate whether this method and the antibody combinations provided by the first three tubes of the EF AML/MDS panel could be used to identify the most frequently identified recurrent genetic abnormalities in AML to rapidly orient cytogenetic and molecular tests and highlight the DfN characteristics of AML blast immunophenotypes. Using the algorithm described here, the Compass databaseguided diagnosis enabled the correct classification of AML types with an AUC of 0. 83 (95% CI: 0. 75-0. 89; p < 0. 001). The major advantage of this method was the ability to exclude certain recurrent genetic abnormalities from new AML cases, with 92% sensitivity and a 98. 5% negative predictive value, which can prevent the performance of unnecessary, expensive, and time-consuming tests.
This method also allowed for the identification of NPM1mutant AML cases with an APL-like immunophenotype, which might benefit from personalized therapy. In addition, using this method, we observed a significant phenotypic heterogeneity among the various MLL-r AML cases, which is consistent with the prognostic variability associated with this AML type. This method could be useful for identifying immunophenotypic patterns associated with different translocations involving the MLL gene. The application of the PCA-based algorithm to the four typical AML groups with recurrent genetic abnormalities that were included in the reference database highlighted the relative contributions of each marker included in the staining panel. The increased contributions of the SSC and FSC parameters highlighted the importance of rigorously standardizing these parameters over time and across instruments when performing multicenter studies.
Our preliminary data show that the Compass databaseguided analysis represents a helpful tool for the identification of DfN, which may be useful for advancing MRD evaluations in AML. According to our data, a similar approach has been applied for the detection of DfN patterns during MRD assessments in older AML patients. This method allowed the detection of DfN patterns and complete remission by MFC-MRD before morphological complete remission, serving as an independent prognostic factor in older AML patients. Therefore, this method can improve the sensitivity of MRD detection after semiintensive therapy or hypomethylating agents  (21). The identification of phenotypic imprints for AML groups with recurrent genetic abnormalities may allow for the detection of leukemic blasts, even in the absence of phenotypic identification at diagnosis.
The antibody combination from the first three tubes of the EF AML/MDS panel has differential efficiencies for assessing MRD across different types of AML. Therefore, the use of multiple tubes could increase the efficiency of MRD evaluation using MFC. The detection of AML MRD using the Compass databaseguided analysis was superior to WT1 qRT-PCR evaluation for predicting the risk of disease relapse in MLL-r AML. In addition, previously published data show that qRT-PCR tests failed to predict disease progression in t(8;21) AML cases with low levels of  MRD (22). In line with this study (22), we observed that the MFC method showed a constant low level of MRD positivity before relapse in the t(8;21) AML case, unlike the qRT-PCR method that indicated MRD levels below 10 -4 at three different time points.
A good correlation was also observed between MRD evaluation by qRT-PCR and MFC in t(15;17) AML and inv (16) AML cases. These preliminary results show that MRD evaluation in AML using MFC combined with Compass database-guided analysis should be considered to complement molecular biology for MRD detection. The limited number of antibodies that can be combined into a single tube for routine MFC diagnostic purposes is a disadvantage of this method, as a relatively high number of unclassified events remains in the CD45 +low gate, limiting the specificity and sensitivity of this method. However, the findings of this study require large-scale validation in a multicenter study to gather sufficient quantities of sample data to establish a robust analytical model that can contribute to the development of supervised machine learning techniques capable of performing automated MFC interpretation for the objective detection of MRD in AML.
In conclusion, the first three tubes of the EF AML/MDS panel, combined with the Compass database-guided analysis: • allows for the exclusion of frequent recurrent genetic abnormalities in new AML cases, with 92% sensitivity and a 98. 5% negative predictive value, which can contribute to preventing the performance of unnecessary, expensive, and time-consuming tests; • allows for the correct classification of t(15;17), t(8;21), inv (16)/t(16;16), and MLL-r AML groups with an AUC of 0. 83 (95% CI: 0. 75-0. 89; p < 0. 00); • provides valuable clues and can direct the extensive genetic or molecular exploration of difficult cases, such as MLL-r AML; • allows for the identification of NPM1 + AML cases that have an APL-like phenotype and may benefit from ATRA therapy; and • allows for the identification of DfN patterns, which may be useful for advancing MRD evaluations in AML.
The blast events from each individual AML case were compared against each well-classified AML group and with the normal my-HPC populations using balanced APS plots. The similarity between blast events from the new case and any defined populations was scored based on the position of the median from the new case relative to the 1 and 2 standard deviation (SD) curves for the defined AML groups or normal my-HPC groups included in the database. A total of 101 cases were correctly classified, whereas 24 cases were incorrectly assigned to the wrong AML group or to the other group. The incorrectly assigned t(15;17) AML case was an NPM1 + AML with an acute promyelocytic leukemia (APL)-like phenotype; the three false-positive t(8;21) AML cases were RUNX1-mutant AML cases, and for the false-positive MLL-r AML case, the array-comparative genomic hybridization (CGH) analysis identified a PICALM/ MLLT10 (CALM-AF10) fusion gene.
(C) Phase 3: Evaluation of the Compass database-guided DfN analysis. Immunophenotypic data acquired at different MRD follow-up time points from four AML patients with different genetic abnormalities who were in cytological remission were used, including one MLL-r AML case, one t(8;21) AML case, one t(15;17) AML case, and one inv(16) AML case. Compass database-guided analysis was compared against quantitative reverse transcriptionpolymerase chain reaction (qRT-PCR).
The same terminology is adopted in the present paper. The ML algorithm developed in the study of Jaza et al. achieved only moderate success rates, and this was attributed to severe limitations in the morphological and rheological databases that were built from the experimental campaign. It was concluded that an enrichment of these databases by more appropriate geometrical descriptors might allow better performance in predicting rheology from morphology. The present study aims to bring such improvement. Third body particles are produced with a pin-on-disk tribometer, which is presented in the next section. They are studied without being collected from the disk. For this purpose, a scanning electron microscope is used to acquire images, then an image analysis program developed in python language is developed to extract relevant quantitative information about the morphology of wear particles.
This is the case for powder-like third body, for which the darker areas on their surfaces could be interpreted as holes by Otsu's thresholding, an example of this problem is illustrated on Fig. 20. To solve this issue, another python code using machine learning algorithms was developed to segment the images. The operation of this program is shown in Fig. 19  (orange rectangle on the right, i. e. "ML"). First, the user chooses a particle image whose pixels serve as the input base for machine learning. This particle is manually segmented by the user in order to create the labels "belongs" or "does not belong" to the particle, affected to each pixel. In a second time features are extracted in this training image, such as morphological profiles and profiles of texture  [1]. Finally, the intelligent algorithm is trained on these extracted characteristics.
Monitoring of structural vibrations and operational modal analysis are clearly essential to effectively control structural safety and the operational behavior of tidal current turbines. In order to satisfy industrial requirements, generally related to a mass gain problem, hybridization provides an excellent method to improve the breaking strength of composite materials, while keeping adequate mechanical performance for marine renewable energy applications. In this context, this work aims to study the structural modal analysis of a tidal turbine nozzle and the effect of hybrid materials (carbon/Glass) on the natural frequencies and corresponding mode shapes of the three laminates. The modal analysis was calculated by the Finite Element Method using ABAQUS software. According to the results, the stacking sequence has a considerable impact on the natural frequency of the nozzle.
The model was evaluated numerically using ABAQUS software that uses the finite element approach to identify all significant behavioural and stability issues. The numerical results showed that the stacking sequence has a significant impact on the natural frequency, displacement and resonance of the structure. This study could subsequently contribute as a reference for a more detailed study of the tidal current turbine's vibrations, structural dynamics, and other problems. First, this paper presents a description of the structure, the hybrid materials and the boundary conditions studied. Then, a presentation of the numerical model and a study on the convergence of the mesh were conducted. At the end, some results and discussions have been presented.
Nozzle design To estimate the natural frequencies of the ducted tidal turbine and the vibration mode shapes, the numerical model is developed in Abaqus software using finite element method. The hydrodynamic profile of the nozzle was obtained using Heliciel software as shown in Fig. 1. Fig. 2  represents the 3D nozzle of the tidal current turbine. Indeed, the total diameter of the nozzle is about 20 meters.
Numerical Investigation In order to study the overall behavior related to, for instance, global stress/strain levels, tip deflection, and Eigen-Frequencies. The Finite Element Method (FEM) is highly practical and has historically been successfully deployed in the development of tidal turbine blades  (Luo et al. , 2020). No publications have been conducted previously on finite element (FE) modeling using ABAQUS software to investigate all pertinent behavioral aspects and stability problems of tidal turbines. This field of research is increasingly important; given this technology is progressively becoming large enough to induce flutter instability. In this regard, the finite element modeling was developed in the Abaqus software to estimate the natural frequencies and mode shapes of the structure.