dataesr/scientific-paragraphs-categorization huggingface dataset:
The threshold X = 0 corresponds to the case where all protein clusters are selected (called 'Panproteome', abbreviated Pan-P, in reference to pan-genome). A second threshold at X = 0. 95 retains only the clusters containing a protein originating from at least 95% of the proteomes (called 'Soft core proteome', abbreviated Soft-P). A third threshold at X = 0. 5 retains cluster containing proteins occurring in at least 50% of the proteomes (called 'Shell core proteome', abbreviated Shell-P). For each protein cluster, EsMeCaTa selects the representative protein (first sequence in the alignment made by MMseqs2) to represent the cluster. The selected sequences of the representative proteins are stored in a fasta file using biopython  (Cock et al. , 2009). The final step of the workflow annotates the protein clusters by querying the UniProt database (GO, EC, see the columns 'Functional annotation of clusters' in the Table  1 ).
In 9 cases (such as Escherichia), EsMeCaTa returned the reference proteomes found in Uniprot (from 1 to 48). In the last two cases (Enterobacterales and Gammaproteobacteria), more than 99 reference proteomes were found, of which 96 proteomes were selected by the subsampling procedure. We observe that in general (in the column 'Protein clusters (MMseqs2)' of the table 1), the size of the Pan-P increases with the number of selected proteomes, while the size of the Soft-P decreases. The size of the Shell-P appears to be much more stable. The numbers of GOs and ECs recovered follow the same trends and are systematically lower than the Pan-P, Shell-P and Soft-P sizes (column 'Functional annotation of clusters' of Table  1 ). To test EsMeCaTa on environmental samples, we analysed the taxonomic affiliations contained in the 16S rRNA (413 OTUs) and rpoB (309 OTUs) datasets provided in  Ogier et al.
(2019). Run of EsMeCaTa on the 722 OTUs took 2 days and 10 hours on a 20 CPU cluster. For the 16S and rpoB taxonomic affiliations respectively, means of 31 and 22 proteomes were recovered. Soft-P contained respectively 1,335 and 1,669 proteins clusters in average, associated with respectively 815 and 1,014 GOs, and 288 and 367 ECs in average. This suggests that EsMeCaTa can be used for the analysis of environmental samples by predicting proteins and functions. This paves the way to study the metabolic capabilities of taxa present in the sample. Financial Support: This work was supported by Deep Impact ANR-20-PCPA-0004. Conflict of Interest: none declared.
Thermodynamic modeling of volatile elements Our approach was then to determine chemical equilibrium by specifying the bulk chemical composition, the pressure and the temperature of equilibrium using the FactSage software  (Bale et al. , 2016)  with the FactPS, FToxid, FTsalt and FSstel thermodynamic data bases. These data bases include a large number of gaseous compounds, solids with numerous solutions relevant to planetary science as well as solution models for molten iron metal, molten oxide mixtures and sulfides (See Table  S1  for details). The following elements whose abundance is given in Table  1  were used for the calculations. For the molten oxides, the FToxid-[SLAGA] mixtures were used as this includes the largest number of elements, while for the metals the FT-Stel-Liquid data base was included.
Activities of moderately volatile elements in silicate melts and liquid metal For a limited number of trace elements, the activity coefficients are not available in the FToxid and FTstel data base (specifically in FToxid-SLAGA). By default, the activity coefficients of these elements would be assumed to be equal to 1, which means that their volatility would not be properly estimated if there are large deviations of the activity coefficients from 1. However, there has been experimental data recently produced  (Norris, 2016)  for some of these elements at a constant temperature (1973 K) for a range of silicate melt compositions. These activity coefficients were even parameterized as a function of molar abundance of major oxides (e. g. CaO, FeO etc. ). For our model we have used the values of these activity coefficients determined for BSE composition and assumed them to follow the relationship: ln(γ i ) = A RT Similarly, we have added the activity coefficients determined by  Sossi et al.
At high temperatures, O 2 is one of the most abundant species among the elements in the vapor phase, while H 2 becomes the most abundant at temperatures below 2150 K and its fraction is higher than for H 2 O and CO by an order of magnitude. The large O 2 partial pressure in the vapor phase only prevails at temperatures above 2300 K. Sulfur is present in the vapor phase mainly as SO and SO 2 while it gets dissolved both in the liquid metal and the silicate melt with decreasing temperature. At a pressure of 1 bar, it forms a solid sulfide only below 1100 K. In contrast, H and C remain mostly in the vapor and get dissolved in the metal as the temperature cools down, as the Factsage™ database used here did not include a solution of C and H for silicate melts.
The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation-maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer.
The diagnostic applications of Bayesian analysis in medicine have been evolving over the past few decades  [15] [16] [17]. We recently introduced the method for multiclass cancer classification on biomarkers by applying a Bayesian approach  18, 19. The proposed method allows the discovery of previously unknown groups with different levels of biomarkers by extracting information from molecular assay data using probabilistic modeling. The performance of this method has been validated over datasets of more than 300 clinical samples. In addition, it has been shown to improve the binary classification of clinical markers (HER2 and steroid hormone receptors). Given these successes and the need to improve disease diagnosis and personalized treatment options, we developed g3mclass, a practical Gaussian mixture modeling software for molecular assay data classification.
All other test classes have all parameters adjustable. While our semi-constrained approach preserves parameters of the reference class, it does not affect the convergence and high speed of the analysis regardless of the overlap between the reference and the test values. For example, using a laptop computer, the g3mclass runtime for classifying ten analytes with thousands of measurements takes less than 20 s. In the test classes labeled with negative (e. g. , -1, -2, etc. ) and positive (e. g. , 1, 2, etc. ) integers, the mean values are lower and higher than class 0. The greater the absolute integer value, the further the class is positioned relative to class 0, as illustrated in a plot created by the g3mclass software (Fig. 2A ). To learn the test model, the software initializes the semi-constrained EM algorithm with classes corresponding to peaks in the histogram calculated on the test sample.
g. , in disease or post-treatment). Multiclass classification on a single analyte: biomarker or therapeutic target. First, we demonstrated the capabilities of g3mclass to automatically classify samples on the gene expression data for the established diagnostic/drug-response biomarker, the ERBB2 encoding HER2. The mRNA levels were measured by a QuantiGene Plex 2. 0 (QG2) assay in the intended-use population (as described in "Materials and methods"), where there is a regular need to differentiate among the patients with breast cancer. For the test and reference input, we used mRNA measurements from invasive breast carcinoma (IBC) and mammoplasties (noncancer), respectively. Additionally, we queried this gene expression data from the independent cohort of patients diagnosed with ductal carcinomas in situ (DCIS) on the pre-operative biopsy.
3C  and Table  3 ). For 75 biopsies successfully profiled in QG2 assay, HER2 status by IHC could be assessed in 70 samples freshly cut from the same blocks of tissue that remain available after gene expression analysis. For the g3mclass selected 4-class GMM with the lowest BIC = 1650. 6 and 186. 6 as a cutoff, the accuracy, i. e. , an overall probability that a patient's pre-surgical biopsy being correctly classified on a binary HER2 status from the ERBB2 mRNA expression data, was 92. 86% (95% confidence interval 84. 11-97. 64%), the positive predictive value was 100%, while the negative predictive value was 91. 53% (95% confidence interval 84. 11-97. 64%). Thus, the results of g3mclass data analyses show the accuracy of identifying breast cancer potentially sensitive to anti-HER2 therapy and the robustness of the software in making the correct classification without equivocal results in the test and independent query.
We also provided how human experts may select among probabilistic GMMs automatically learned by the g3mclass software. GMM is often used for unsupervised clustering, mainly for data exploration. An example of such an approach is subgrouping cancers based on the similarity of gene expression patterns  [25] [26] [27]. The g3mclass exploits the customized semi-constrained EM algorithm's ability to learn test models from known (provided by experts) and unknown (missing values) information. This computational approach is the opposite of supervised classifications requiring the predefined knowledge of the number of the mixture components. For example, a two-component mixture model sorts differentially expressed genes in microarray experiments  12. The principal innovation of g3mclass is embedding pre-existing experts' knowledge of reference parameters into the test GMM.
As a result, it substantially improves the differentiation of new-to-test versus reference-like values and provides biological and clinical context for interpretation outcomes. This approach defines the significant difference of g3mclass from other powerful software packages handling Gaussian finite mixture modeling as their clustering capabilities, including the most popular R package mclust  14  and the Addinsoft XLSTAT (https:// www. xlstat. com/ en/ compa ny). Overall, there are two critical applications of the g3mclass in the biomedical field. First, it enables the discovery of previously unknown groups with different levels of biomarkers, including those that are not part of the reference and thus are more likely linked with disease. Second, it allows individual patient classification in line with personalized clinical decision-making.
To further unravel the g3mclass capabilities in determining the biological and clinical value of candidate biomarkers, we provide evidence of the scalability of the g3mclass software to classify on multiple analytes and visualize concurrent classifications with heatmaps. This approach may help manage ER+/HER2+ cancers, as the dual implementation of the hormone and anti-HER2 therapies showed evidence of success in the clinical trials  8. In sum, the applicability of g3mclass may be easily extended beyond one biomarker, dataset, or disease. It provides a cost-effective and straightforward way to examine and deal with the variability of the molecular assay data. The analysis with the g3mclass does not depend on the computing environment, which ensures the research's reproducibility. In clinical settings, applying g3mclass promises more precise stratification that may help improve therapeutic sensitivity.
The tissue samples were accrued randomly from the same geographic region. Summaries of the characteristics of the study populations have been published  19, 40. Direct messenger RNA (mRNA) profiling. For mRNA data collection, we ran QuantiGene Plex 2. 0 (QG2) assay (Genospectra/Panomics/Affymetrix/eBioscience/ThermoFisher Scientific, USA) and read on Flex-Map 3D (Luminex/Merck Millipore) according to manufacturers' protocol and as described in detail  19. QG2 is a highly reliable and validated molecular assay that uses amplified branch DNA (bDNA) technology for parallel gene expression profiling  41, 42. We analyzed measurements of specific probes with the QG2 assay kits for quantitation of multiple target specific RNAs directly in lysates from FFPE tissue and cell lines. Our human Plex Set 12988 included 14 target-specific and two housekeeping gene probes described in detail  40.
Here, we analyzed mRNA for ERBB2 (probe set region 1203-1621), ESR1 (probe set region 5671-6292), and PGR (probe set region 2609-3194). Clinical markers. The status of steroid hormone receptors (ER and PR) and HER2 were determined by IHC and/or FISH, the FDA-approved methods. The status of ER, PR for all tumors, and HER2 for primary IBC were obtained from surgical pathology reports. In addition, HER2 status in DCIS was assessed based on the FDAapproved method for IBC as described  40. Statistical analyses and modeling. In this article, modeling and analyses of datasets were performed with g3mclass v. 1. 2 on macOS Mojave v. 10. 14. 6. with Python v. 3. 9. 5 and wxPython v. 4. 1. 1. Additionally, we used MedCalc, a diagnostic test evaluation calculator at https:// www. medca lc. org/ calc/ diagn ostic_ test.
Code availability The g3mclass software is written in Python, and its graphical user interface (GUI) is based on wxPython. Its documentation is distributed with the software but also is available online https:// g3mcl ass. readt hedocs. io. It includes installation instructions as well as a step-by-step workflow example. The g3mclass was tested on macOS, Windows, and Linux platforms but should run on any other platform offering Python3 and wxPython. It is distributed under OpenSource license GPL-2, freely installable from https:// pypi. org/ proje ct/ g3mcl ass with the standard Python pip tool. The code source is freely accessible from https:// github. com/ Maths Cell/ g3mcl ass.
Radio Pulsation Search and Timing Observations As part of a long-term effort to discover radio pulsars associated with Fermi sources, organized by the Fermi PSC, we made targeted observations of the candidates identified in  Frail et al. (2018). We observed P86Y3595 on 2017 February 1 (MJD 57,785) for 30 minutes at 820 MHz with the Robert C. Byrd Green Bank Telescope (GBT). We used the GUPPI pulsar backend to record 200 MHz of bandwidth with 2048 channels at 61. 44 μs resolution. We performed an acceleration search of these data over a range of trial dispersion measures using PRESTO  (Ransom et al. 2002)  and identified a strong candidate pulsation with 1. 79 ms period (559. 4 Hz) at a dispersion measure (DM) 75. 91 pc cm -3. Following the discovery, a GBT observation of 5 minutes at S-band (on MJD 57,806) confirmed the discovery and determined the pulse width at that frequency to be a very narrow 3%.
We also obtained a 30 minute observation on MJD 57,788 using the Karl G. Jansky Very Large Array (VLA) in phasedarray pulsar mode. The data were taken while the array was being reconfigured; 20 antennas were in their D-configuration positions (maximum baseline 1 km), and seven still in A-configuration (maximum baseline 30 km). The observation was split into three scans of 10 minutes in order to allow for rephasing. Interferometric phases were determined using the nearby calibrator J1554 -2704, then individual antenna data streams were coherently summed to create a single-pixel data stream with high time resolution at the previously determined radio source position. The summed voltage data were processed in real time using dspsr (van Straten & Bailes 2011) and recorded using 2048 0. 5 MHz channels (1024 MHz total bandwidth, centered on 1500 MHz) at 32 μs time resolution.
The eclipse and total intensity pulse profile with high signal-to-noise ratio (S/N) from that observation are shown in Figure  1. We also obtained five observations of varying duration using the 76 m Lovell telescope between 2019 November 15 and 2020 September 21. We used a backend based on ROACH FPGA boards  (Bassa et al. 2016a ) to record coherently dedispersed, phase-folded data covering a bandwidth of 384 MHz in 0. 25 MHz channels centered at 1534 MHz, with 256 phase bins per pulse period. To obtain approximate flux-density calibrations for these observations, we estimated the system equivalent flux density from observations of the Crab nebula on nearby epochs, adjusting for ground spillover due to the low elevation. We estimate the fractional uncertainty on this calibration to be around 20%.
The pulsed flux density was then estimated from each observation by fitting the high-S/N 820 MHz GBT pulse profile as a template, along with an arbitrary baseline, to the flux-calibrated profiles. The average flux density over the five observations was S 1534 = 0. 20 ± 0. 05 mJy. A log of all the radio observations is presented in Table  1. The pulse profile at a range of frequencies is shown in Figure  2. Combining these radio data, we obtained an initial orbital solution with a period of 5. 60 hr and a semimajor axis of 0. 151 lt-s. Because we have only a small amount of radio data, most of the system parameters are better determined by the gammaray timing in Section 4. 2. However, the precise radio times of arrival over the 5 hr observation allow us to determine some of the orbital parameters more precisely than is possible from the gamma-ray data alone.
(2020). Denser grids are required for higher harmonics and thus the grid is built for the highest harmonic, here M = 5. The search space was split into smaller parts and the search was carried out in parallel on the ATLAS computing cluster in Hannover. On one single computer the search would have Significant gamma-ray pulsations were detected over most of the 10 yr LAT data span used in this search. The resulting pulse phase showed residual time dependence, indicating time evolution of the pulsar spin-down rate. Still, the maximum H statistic detected in the search was H 5 = 276. 3. Conservatively assuming that all 6 × 10 11 trials were independent (the actual effective number of trials is smaller), the false-alarm probability is P FA = 9. 6 × 10 -37 , which confirms the detection of gamma-ray pulsations.
184 s). The timing solution was obtained using the 12 yr Fermi-LAT data set described in Section 4. cutoff rigidity, or the total count rate, in our initial extraction. This initial extraction yielded 117. 8 ks of good time. For each photon, we computed the pulse phase using photonphase from PINT  (Luo et al. 2021 ) and the timing model provided in Table  2. The par file for this model, compatible with the Tempo2 and PINT pulsar timing packages, is included in the. tar. gz package associated with this paper. To exclude high background regions, instead of making an arbitrary cut on count rate, we developed a tool ni_Htest_sortgti. py that divides the data into segments of no more than 100 s (and no less than 10 s). These segments are sorted by mean count rate. Since the count rate from the pulsar is very low and presumably constant, this is equivalent to sorting by background rate.
Then the H-test detection statistic is evaluated cumulatively, going from the lowest background to the highest. This algorithm thus finds the optimal background rate cut that maximizes the S/N of any detected signal. The script repeats this process over a large grid of E min and E max energy cuts to search for an optimal energy band as well. For our detection statistic, we chose to use the H-test (de Jager et al. 1989), which is preferred to the Z 2 test for unknown pulse profiles that may be very sharp, as is seen in the nonthermal X-ray MSPs. For PSR J1555-2908, this procedure did not reveal any very strong candidate pulsation. The highest H-test found corresponded to a single-trial significance of 3. 05σ, when searching the range 0. 26-2. 74 keV, with the algorithm selecting the 106. 7 ks with the lowest background out of the total.
10. 1029/2021JF006447 10 of 27 parameter p j. The probability mass function P, that is, the probability that n j, boulder is exactly equal to a given value k (which is a dummy variable), is defined by: 𝑃𝑃 (𝑛𝑛𝑗𝑗𝑗 boulder = 𝑘𝑘) = ( 𝑁𝑁𝑗𝑗𝑗 packet 𝑘𝑘 ) ⋅ 𝑝𝑝 𝑘𝑘 ⋅ (1 -𝑝𝑝) 𝑁𝑁 𝑗𝑗𝑗 packet -𝑘𝑘 (13) = 𝑁𝑁𝑗𝑗𝑗 packet ! 𝑘𝑘! ( 𝑁𝑁𝑗𝑗𝑗 packet -𝑘𝑘 ) ! ⋅ 𝑝𝑝 𝑘𝑘 ⋅ (1 -𝑝𝑝) 𝑁𝑁 𝑗𝑗𝑗 packet -𝑘𝑘 (14) Random sampling on this distribution is easy to code. For our implementation, we used the function rbinom of the package "stats" in R (R Core Team, 2020). As noted previously, boulder classes cover a range of diameters [D min , D max ]. Each time a class-j boulder is successively generated, its diameter must be defined. Rather than using the mean diameter value computed previously for the packet size  ⟨ boulder ⟩ , we randomly select a value in the range [D j,min , D j,max ] assuming a uniform distribution.
10. 1029/2021JF006447 13 of 27 the full fundamental mathematical basis of possibility distributions and hybrid-uncertainty propagation, that is, merging possibilities and probabilities, is beyond the scope of this paper. We refer the reader to  Guyonnet et al. (2003)  and  Baudrit et al. (2006)  for the full story. Notwithstanding, we will present a simple synthesis of the method (see also Figure  6 ). The statistical framework of possibility distributions was implemented in this study using the software library HYRISK, developed by  Rohmer et al. (2018) , which is available for the programming language R. Our implementation was used to perform uncertainty propagation using possibility distributions in our model. The objective of uncertainty propagation using hybrid-or possibility distributions is to define probability boxes ("p-boxes"), that is, upper and lower cumulative distribution functions (CDF).
Assessing Binomial Probabilities of Each Class of Boulders Although it is rarely done, it is possible to estimate the quantity and size of boulders conveyed by debris flows at a given site, of course with some uncertainties. BGC provided such an estimate for the Cheekye barrier site to support modeling of debris flows with the barrier's slit outlet. The boulder count estimate was organized around the probable number of boulders of a given size class in a 100,000 m 3 reference volume. The estimate was developed by recording the size and quantity of boulders upstream and downstream of the barrier site, using aerial imagery, field surveys, and test pits in debris flow deposits. The test pit data included the estimated percentage of boulders within the overall deposit (2%-5%); these percentages were used to scale the boulder counts to the reference volume.
It is also worth highlighting that despite their clear value for problems involving many unknowns that are difficult to characterize, possibilistic and hybrid probabilistic-possibilistic approaches have yet to become widely used in the geosciences or in the field of natural hazards  (Baudrit et al. , 2006; Guyonnet et al. , 2003; Rohmer & Verdel, 2014). Although they are well-suited to such fields of application, where some model input data are systematically imperfectly known, they remain seldom used. We believe this is partially because they are not easily implemented from scratch, and so we emphasize the immense utility of the HYRISK R-package of  Rohmer et al. (2018). Another reason for the lack of widespread adoption of possibilistic approaches is likely that existing explanations of their functioning tend to be insufficiently intuitive.