dataesr/scientific-paragraphs-categorization huggingface dataset:
The hashed rectangle from F-measure 0. 79 to 1 highlights the values of F-measure that are unreachable because 1019 reactions in EcoCyc were not present in the panmetabolism of the 29 non-degraded bacteria. Figure  4 : AuCoMe results on the Calvin cycle pathway in the algal dataset. AuCoMe was applied to the dataset of 36 algae and 4 outgroup species (columns). Each row represents a MetaCyc reaction of the pathway, the table shows whether it is predicted by AuCoMe: blue -draft reconstruction, orange -robust reactions predicted by orthology propagation that passed the filter, green -structural verification, and gray -non-robust reactions predicted by orthology propagation and removed by the filter, black -not predicted, yellow -manually added because the MetaCyc database 23. 5 does not contain a reference gene-reaction association for this reaction).
Acknowledgements We acknowledge the GenOuest bioinformatics core facility https://www. genouest. orgfor providing the computing infrastructure. We also acknowledge  Pauline Hamon-Giraud  for fruitful discussions. This work benefited from the support of the  French Government  via the  National Research Agency investment expenditure program IDEALG  ( ANR-10-BTBR-04 ) and from  Région Bretagne  via the grant  SAD 2016 -METALG (9673 ).
Availability and dependencies AuCoMe is a python package under GPL-3. 0 license, available through the Python Package Index at https://pypi. org/project/aucome. The source code and the complete documentation are available at https://github. com/AuReMe/aucome. Running AuCoMe on the datasets studied in the paper required as dependencies Blast v2. 6. 0  (Altschul et al , 1990) , Diamond v0. 9. 35  (Buchfink et al , 2015) , Exonerate v2. 2. 0  (Slater and Birney, 2005) , FastME v2. 1. 15 (Lefort et al , 2015) , MCL  (Enright et al , 2002) , MMseqs2 v11-e1a1c  (Steinegger and Söding, 2017 ), OrthoFinder v2. 3. 3 (Emms and Kelly, 2015, 2019), Pathway Tools v23. 5  (Karp et al , 2019). These following Python packages are needed to install AuCoMe: matplotlib mpwt v0. 6. 3 (Belcour et al , 2020a), padmet v5.
The same instructions were provided for both types of testing and given prior to the series of tests. The instructions included: "You will hear sounds, sometimes very faint. Please raise your hand when you hear a sound, even a very faint one, and point to the ear that heard it. If you hear a sound in the other ear, ignore it". The test signals (pulsed pure tones) and masking signals (narrow-band noise centred on the frequency of the test signal) were emitted according to IEC 60645-1. The same audiologist completed both tests and was instructed to ignore the results of the first audiogram while performing the second one. Patient information was stored in an approved health data hosting server (French HDS) and in the Noah databases (HIMSA V R , Copenhagen, DK) of the three audiology clinics where testing took place.
Statistics Personal identifiers (e. g. name, date of birth) were removed from the study data using a unique identifier for each participant. Study team members were not permitted access to the identifying information unless within the circle of care. Study data was stored on an HDS (H ebergeur de Donn ees de Sant e) certified database. This was a comparative clinical study whose objective was to test equivalency for a continuous response variable, i. e. hearing thresholds. BIAP (Bureau International d'Audiophonologie, https://www. biap. org) hearing loss categories were used to classify hearing ability of participants based on the pure tone average (500, 1000, 2000, 4000 Hz) of the better hearing ear. Clinical equivalence, the primary objective of the study, was evaluated by determining the proportion of thresholds within 5 dB HL and 10 dB HL of conventional (gold-standard) audiometry; 95% confidence intervals were also calculated using the Wilson score method.
5 kHz, 1 kHz, 2 kHz and 4 kHz. In the present study, the measurements were carried out sequentially using a random draw to determine which system to use first and with the same types of transducers (reference, acoustic attenuation mode) used for each of the systems, at each of the measurement sites. These test-retest conditions, identical to those used in previous studies, make it possible to compare our results. Thus, it could be shown that, whatever the frequency tested in air conduction, the absolute differences calculated for this study were always lower than those published for air conduction test-retest conditions for the same patient on a single device  (Landry and Green 1999; Zwislocki et al. 1988 ). For bone conduction, Laukli & OddbjØrn  (Laukli and Fjermedal 1990)  and  Swanepoel & Biagio (Swanepoel and Biagio 2011)  independently published clinical data on the variability of measured hearing thresholds.
Methods In this review, peer-reviewed and publicly available journal papers were identified from a variety of online databases, while publicly unavailable copies were obtained through our institutional access to journal publications and databases. The search for appropriate journal papers was performed using specific keywords such as AI, ML, blood cancer, hematology, malignancy, leukemia, management, and cancer, which were linked and combined using 2 Boolean operators "AND" and "OR" to produce more focused outcomes (Figure  1 ). The paper collection process targeted all the articles written in English in the last 7 years with the aforementioned keywords in their abstracts or titles. The search identified 567 papers, of which only 144 were retained for review. The 423 excluded papers either focused on oncology without paying special attention to hematology, addressed hematologic malignancies from an automated perspective without employing AI models, or covered purely technical drug treatments for blood cancers.
Using the mapping and analysis tools provided in the Biblioshiny software (K-Synth Srl), a word tree map was generated to highlight the top 10 most frequently used keywords by scholars in the field. Figure  4  shows the hierarchical visualization of author keywords. The highest frequencies were for ML (29%), leukemia (15%), classification (12%), and DL (11%), with 32, 17, 13, and 12 occurrences respectively, indicating the recent focus on ML and DL applications in the detection and classification of leukemia and its types (acute myeloid leukemia [AML] and acute lymphoblastic leukemia  [ALL] ). The tree map also highlights the different techniques and tools used in this field of research through keywords such as "segmentation," "image processing," and "flow cytometry," with an identical frequency of 5% each.
While Pan et al  [131]  built a model to predict disease relapse, Hauser et al  [144]  studied the possibility of predicting CML prior to diagnosis using only CBC test results and ML algorithms such as XGBoost and LASSO algorithms on 1623 patients with a definitive CML status. The variables used in the study included laboratory CBC test results, patient demographic features such as their age and gender, and patient encounter information (the number of patient visits to outpatient clinics, etc. ). A forward feature selection process was employed to measure the predictive performance of the most potential predictors. The data set was then divided into 7 subsets, in which time of diagnosis was set as a patient baseline, and the 6 remaining sets corresponded to the different time periods preceding the diagnosis test.
Collected Data We collected both quantitative and qualitative data. Regarding quantitative data, we collected the time to perform each action as well as the position, and rotation of users to analyze their navigation behaviours. First, users were asked to fulfill a simulator sickness questionnaire (SSQ,  Kennedy et al. (1993) ) at the beginning and at the end of the experiment. Our goal was to be sure that cybersickness did not impact the results of the study and to evaluate potential differences between the four conditions. After each interaction mode users had to fulfill two questionnaires. First, a NASA Task Load Index (TLX) questionnaire  (Hart and Staveland (1988) ) was fulfilled to evaluate the cognitive workload of the application. Second, users were asked to rate 10 statements on a Likert scale from 1 (strongly disagree) to 7 (strongly agree) to evaluate the usability and the user experience of the application.
We think that AR and VR current technical limitations could question the equivalence between reality and simulation in specific cases but as these domains make continuous technological progress, we believe that such issues are only temporary. Differences in sound perception could also be evaluated. Indeed, our study mainly focused on selection and simple manipulation interactions with flat user interfaces in a relatively small space. Our study also focused on simulating AR glasses. Future work could then also evaluate the simulation of handheld AR devices. Simulating smartphones and tablets in a VR headset could raise challenges such as haptic feedback and display resolution. From a technical point of view, it could be interesting that software tools such as Unity and Unreal Engine 11  integrate native AR simulation features to ease the work of applications developers and designers.
Functionalization and Hybridization with Different Target Concentrations While the detection of the multiple CV curves is a remarkable first validation for our device and system, the real goal is to detect the functionalization and hybridization events at different concentrations. For this purpose, the experiments were carried out with different concentrations down to 10 -18 mol/L, which is compatible with our limit of detection defined by working electrode sizes. In this subsection, the results for the functionalization and hybridization with different reporter probes concentrations are reported. These measurements are performed ac- In order to interpret these results, the stored data was processed with a MatLab ® script that divided the curves in three parts and extracts the most important information.
After the formation of the self-assembled monolayer (SAM) on the surface of the electrodes, the coverage of the electrode increases, and, thus, the faradaic current and the slope reduces. With higher concentrations of miRNA, the electrode surface coverage due to the hybridization between the immobilized DNA-probes with the DNA targets increases, and, thus, the slope decreases. In order to extract the information from these voltammograms, they were treated with a MatLab ® script to obtain their slopes, as previously described. Results are shown in Figure  5B , where the extracted slope of the different curves is shown in a histogram. This histogram clearly shows a decrease in the slope of the voltammograms: 0. 790 nA/mV for the bare gold electrode, 0. 334 nA/mV for the functionalized electrode, 0.
Considering the complexity of identifying proteins by reading their sequence, a new approach to identify proteins without reading their sequence has recently been demonstrated  [36, 37]. This approach consists on identifying proteins by monitoring fragments resulting from the reaction products of a protease through biological nanopores and using protein databases to determine to which protein those fragments correspond, in a manner roughly similar to mass spectrometry (Fig. 3 ). Specifically, it has been demonstrated that the AeL nanopore enables discrimination between three different proteins with approximately the same molecular mass, opening up the possibility of extending this approach to the identification of proteins irrespective of their size, charge, and structure/conformation  [36].
These predictions, however, are often limited to nested and stacked canonical base pairs (i. e. , AU and GC Watson-Crick and GU wobble base pairs), while ignoring other hydrogen-bonded bases stabilized by tertiary interactions. The ignorance reflects the difficulty to predict ''tertiary" base pairs without knowing three-dimensional structures. Structure-stabilized base pairs include un-nested (pseudoknot), noncanonical and isolated lone base pairs as well as base triplets. Thus, a gold-standard dataset for these ''tertiary" base pairs requires experimentally determined three-dimensional structures. However, there are only a few thousand RNA struc-tures that have been solved so far. Such a small number of RNA structures makes it challenging to apply deep learning to RNA structure prediction.
Recently, Singh et al. showed that the above problem can be addressed by using a highly accurate but approximate database of RNA secondary structures for initial training  [98]. The initial training is followed by transfer learning with a small dataset of full base-pairing structures derived from RNA structures. The resulting method called SPOT-RNA showed that a single sequence (i. e. , without information of homologous sequences) can provide a significant improvement over the stagnant accuracy of secondary structure prediction by folding-based algorithms in recent years. [84, 96]  and mutants that transform the 3_6 motif and stabilize all conformers, using graph based models, 2D structure prediction, SHAPE structural probing, thermodynamic ensemble modeling, and molecular dynamics simulations  [97].
Acknowledgements Y. W. and Y-G. S. thank  Research Fund of Wenzhou Institute, UCAS  (Grants No. WIUCASQD2020009 ,  WIUCASQD2021043 ). S-J. C. thanks  Dr. Yuanzhe Zhou  for the help with plotting the figures. This work was supported by the  National Institutes of Health  under Grant  R35-GM134919. Research reported in this publication was supported by the  National Institute of General Medical Sciences  of the  National Institutes of Health  under Award Number  R35GM139587  (to K. A. A. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. T. S. gratefully acknowledges funding from the  National Science Foundation   RAPID Award 2030377  and  DMS-2151777  and from the  National Institutes of Health/NIGMS  Award  R35GM122562.
a b s t r a c t The data presented in this article is supplementary to the research article "Phase instabilities in austenitic steels during particle bombardment at high and low dose rates" (Levine et al. )  [5]. Needle-shaped samples were prepared with focused ion beam milling from a 304L stainless steel that was irradiated with fast neutrons (E > 0. 1 MeV) in the BOR-60 reactor at 318 °C to 47. 5 dpa. Atom probe tomography (APT) experiments in voltage mode were then conducted on a Cameca LEAP 50 0 0X HR. Atom position, range, and mass spectrum files after reconstruction with Cameca's IVAS software are included. Cu-and Ni-Si-Mn-rich solute nanoclusters were identified and analyzed using the Open Source Characterization of APT Reconstructions (OSCAR) program. Python code for OSCAR  [4] , information on the program's underlying algorithm, and sample output files are provided.
Value of the Data • The data is useful for understanding radiation-enhanced precipitation (REP) and radiationinduced precipitation (RIP) in austenitic stainless steels (AuSSs), which are commonly used for light water reactor (LWR) core internals. This APT volume contains Cu-rich clusters in a neutron-irradiated AuSS, regarding which there are few available datasets in literature. • REP and RIP information in AuSS samples neutron-irradiated to high doses at LWR-relevant temperatures (270 -370 °C) is limited. Thus, the data given herein is valuable for benchmarking degradation in AuSSs. This information benefits regulators who must decide on license extensions for aging LWR reactors. • The influence of radiation-enhanced Cu-rich clusters on the irradiated properties of AuSSs is unknown. As a result, the data herein can be used by future studies to assess the role of Cu-rich clusters in radiation hardening and embrittlement or cavity swelling in AuSSs.
• Methods for APT solute cluster analysis are continually evolving. Here, an open-source Python program, OSCAR, for APT cluster identification and analysis is provided. The given details on data treatment are important, since clusters detected in APT datasets can vary widely based on mass spectrum ranging or the method used for cluster identification  [1, 6]. • The maximum separation method built into IVAS tends to erroneously identify clusters when there is low contrast between matrix and cluster solute concentrations  [1]. By comparison, OSCAR uses an iso-position method to improve cluster identification accuracy. The included program and information enable reproducible analysis of APT data sets with OSCAR. • In irradiated AuSSs, Si-rich clusters may form by radiation induced segregation (RIS) or RIP.
Data Description The raw data for this article  [4]  was collected by APT from a 304L stainless steel irradiated by fast neutrons (E > 0. 1 MeV) in the BOR-60 reactor at 318 °C to 47. 5 dpa. The run number for the APT data set is R94_10497. The raw data files include R94_10497. pos, which gives the positions and mass to charge ratio of ions in the APT reconstruction, R94_10497. rrng, which gives the ion assignments for mass spectrum ranges, and R94_10497_massSpec. csv, which contains the mass spectrum for the reconstruction. Average bulk composition measured from 20 APT needles after performing isotopic decomposition with IVAS is provided in Table  1. The OSCAR program that was used for Cu and Ni-Si-Mn cluster analysis is included as a Jupyter notebook (OSCAR Iso-Position. ipynb) in  [4]. The Python modules required to run the notebook (cluster_analysis.
Finally, the * summary *. txt provides a brief report on the cluster analysis results. The graphs provided in Figs. 1  and 2  contain data generated when Ni-Si-Mn rich and Curich clusters in data set R94_10497 were analyzed with OSCAR. The data in Fig. 1  is a proximity histogram (proxigram) measured for a Ni-Si-Mn-rich cluster. Concentrations for Fe, Cr, Ni, Si, and Mn are plotted as a function of distance from the initial cluster interface (the interface prior to erosion). Negative distances represent positions in the cluster, while positive distances represent positions outside the cluster (in the matrix). The erosion distance, d erosion , was calculated by fitting a four parameter logistic function to the Fe curve. For this cluster, the calculated erosion distance (0. 55 nm) is shown in gray.
50 % detection rate. Volumes were reconstructed using Cameca's IVAS software with an electric field factor of 4. 5 and an image compression factor of 1. 65. Both Cu-and Ni-Mn-Si-rich clusters were identified and analyzed in the given APT data set using OSCAR. OSCAR is an iso-position algorithm written in Python. The code can be run interactively using the "OSCAR Iso-Position. ipynb" Jupyter notebook in  [4]. For successful operation, the apt_pkg folder must be saved to the same directory as the Jupyter notebook. Besides. pos and. rrng files from IVAS, required user inputs are run number ( run ), the solute species for cluster identification ( solute ), bulk atomic density ( bulkAtDens ), detector efficiency ( q ), local concentration threshold ( thres_conc ), linking distance ( dlink ), minimum number of solute atoms allowed in a cluster ( Nmin ), ranged elements ( elements_list ), and whether to use the sphericity filter during analysis ( separate ).
The basic steps for cluster identification in the OSCAR program are as follows. First, the local solute concentration within a fixed radius around each atom is calculated. In the sample outputs provided, the radius was selected to ensure ∼ 100 atoms were contained in each sphere. Next, atoms with a local solute concentration below a user defined threshold ( thres_conc ) are filtered out. The remaining atoms, which we shall call core atoms, are then formed into clusters based on a user defined linking distance ( dlink ). If the distance between two core atoms is less than or equal to the linking distance, those core atoms are merged into one cluster. After a cluster assignment is given to each core atom, clusters with less than a user defined number of solute atoms ( Nmin ) are excluded from further analysis.
75. It should be noted that for a perfectly spherical cluster, ψ is expected to be unity. After Cu-rich clusters were identified by OSCAR, clusters were examined for evidence of trajectory aberrations. This analysis was done by measuring the relative atomic density across clusters in the x-direction, similar to as in  [2, 3]. Here, relative atomic density is the ratio of the locally measured atomic density, N local , to the measured bulk atomic density, N bulk. When trajectory aberrations in APT data are significant, the relative atomic density spanning a cluster in the x-direction will deviate from unity. Local atomic density measurements were made for a series of rectangular volume slices. The thickness of each slice was 0. 4 nm, and the y and z bounds for the volume were the same as the minimum and maximum y and z values bounding the cluster.
Funding Data analysis and manuscript writing sponsored by  Office of Fusion Energy Sciences, U. S. Department of Energy  under grant # DE-SC0 0 0 6 6 61  with the  University of Tennessee  (SML, SJZ) and contract  DE-AC05-00OR22725  with  UT-Battelle, LLC (PDE, AB). In addition, this work is supported by the  U. S. Department of Energy Nuclear Energy University Program (NEUP)  and  Nuclear Science User Facilities (NSUF)  under grant  DE-NE0 0 08520. This material is based upon work supported under an  Integrated University Program Graduate Fellowship  and the  Graduate Advanced Training and Education (GATE) program  of the  University of Tennessee (SML). Specimen preparation for APT experiments were performed at the  Low Activation Materials Development and Analysis (LAMDA)  lab using instruments provided by the  Nuclear Science User Facilities  and the  Fuel Cycle Research and Development Program, Office of Nuclear Energy, US DOE.
The EPOS-PP Project During the EPOS-PP, the EPOS community worked to develop the architecture of the future e-infrastructure and, at the same time, to establish an efficient coordination and management structure at European level. Within the "Technical preparation" working package of EPOS-PP project (WP 5), seven thematic working groups were created: (i) Seismological data; (ii) Volcano Observatories and other Observatories; (iii) Geological and Surface Dynamics data; (iv) GNSS-GPS Geodetic data; (v) Other Geophysical data; (vi) Analytic and Experimental Rock Physics Laboratories; and (vii) e-infrastructures and virtual community (HPC and Grid). These groups were asked to provide an overview of the existing and planned infrastructures (incl. instrument networks, laboratories, observatories, etc.
) and data repositories at national and European level, as well as to identify the responsible institutions, contact persons, and data policies. In addition, it was requested to define the expectations and requirements of the user community represented by the respective working groups to the EPOS e-infrastructure and to list specific software and tools to be integrated. Finally, each working group was tasked with identifying the requirements to their specific community (related to instrumentation, data sampling, data quality and availability, etc. ) to develop plans for future implementation within EPOS. During the kick-off meeting of the EPOS-PP (November 2010), the representatives of various Observatories and research institutions (INGV, IMO, IGME, UI, DIAS, OPGC, ETH, IPGP) met in the "Volcano Observatories and other Observatories" working group (WG 2) organised in the framework of EPOS-PP WP 5.