dataesr/scientific-paragraphs-categorization huggingface dataset:
BTLBO-KNN outperforms its alternatives in 14 datasets out of 24. RGA-T follows it with 6 top ranks and one tie with BTLBO-KNN. NBABC performs better in 4 datasets. Finally, HLBDA, BSSA_S3, and LBMFO-V3 are never on the top. Table  9  reports the worst observed fitness value of the 20 runs for the algorithms. Even in this worst-case situation, BTLBO-KNN outperforms its alternatives in 16 datasets. RGA-T and NBABC follow it. They are the top performer in 4 datasets (with one tie). HLBDA performs best in the ILPD dataset. Finally, BSSA_S3 and LBMFO-V3 close the rank. They are always behind their alternatives. The mean fitness value computed on the 20 runs reported in Table  10  confirms the superiority of BTLBO-KNN. Indeed, it outperforms the baselines in 14 out of the 24 datasets according to the average fitness values.
RGA-T ranks second with five datasets, for which it beats the alternatives. NBABC is the best for four datasets. HLBDA is the best suited for the retinopathy and prognostic datasets according to the mean fitness value. BSSA_S3 and LBMFO-V3 still close the rank, consistently performing worse than the other algorithms. Table  11  reports the standard deviation of the fitness measured in the 20 runs. BTLBO-KNN exhibits the lowest values in 6 datasets. It is better than HLBDA, NBABC, RGA-T, and LBMFO-V3 in terms of stability. BSSA_S3_CP presents comparable scores with BTLBO-KNN regarding the standard deviations of fitness However, even if it is as stable, always worse.
2) PAIRWISE COMPARISONS WITH THE WILCOXON SIGNED-RANK TEST We perform the non-parametric Wilcoxon Signed-rank test for pairwise comparisons on the fitness values to compare BTLBO-KNN with the baselines. The two algorithms have similar fitness performance if the P-value is higher than 0. 05. Otherwise, they are statistically different. signifies that the compared algorithm outperformed our proposed one. The symbol ∼ indicates no significant difference between the fitness performance of BTLBO-KNN and the baseline. Results reported in Table  12  show that BTLBO-KNN is essentially better than BSSA_S3_CP and LBMFO-V3 in at least 22 medical and gene-expression datasets out of the 24 used ones. It appears to be statistically similar to HLBDA for 11 datasets and to NABC for 13 datasets. Note that these datasets are small and middle-sized medical and gene-expression datasets.
In contrast, the Wilcoxon test demonstrates that it outperforms them in large-sized medical and gene-expression datasets. RGA_T is the most competitive feature selection approach. Indeed, it is statistically superior to BTLBO-KNN in 05 cases out of 24 (i. e. , SPECTF Heart, SCADI, LSVT Voice   13. Average classification accuracy (Avg acc ) and average number of the selected features (Avg feat ) for BILBO-KNN and the baselines in the medical datasets under test. Results are computed on 20 independent runs. rehabilitation, discrete, and Parkinson datasets). However, BTLBO-KNN statistically outperforms RGA_T in 13 datasets and appears similar in 04 cases. Overall, we can conclude that experimental results confirm the superiority of BTLBO-KNN in terms of fitness performance. Furthermore, RGA_T appear to be the most competitive algorithm.
3) CONVERGENCE COMPARISONS We distinguish small/middle-sized datasets from large datasets to discuss the convergence results. Figure  3  presents the convergence curves of the average fitness values of the various algorithms for the small/middle-sized datasets. BSSA_S3_CP and LBMFO-V3 algorithms exhibit the worst convergence behavior. RGA_T is the most competitive algorithm in terms of convergence, especially in middle-sized medical datasets (SPECTF Heart, SCADI, LSVT Voice rehabilitation, Lung discrete, and Parkinson datasets). However, there are no significant differences between the convergence curves of BTLBO-KNN, HLBDA, NABC, and RGA_T for almost all the small-sized datasets. Figure  4  also reports the convergence curves for the ten large-sized datasets. It shows that our proposal excels very  well in terms of achieving the best solutions with a lower number of fitness function evaluations.
D. COMPARISON OF THE CLASSIFICATION ACCURACY AND NUMBER OF SELECTED FEATURES Table  13  reports the average classification accuracy (Avg acc ) and the average number of selected features (Avg feat ) by the various algorithms for the datasets under test. Remember that we perform 20 independent runs for each dataset. The best results are highlighted in boldface. One can see in Table  13  that in most datasets  (18) , especially for the large-sized ones (Colon, Lung, Lymphoma, GLIOMA, TOX_171, Prostate_GE, Leukemia, ALLAML, SMK_CAN_187, and GLI_85), BTLBO-KNN strongly reduces the number of selected features compared to the other algorithms. As we can see also in 13, for the whole middle/largesized datasets, the average number of selected features by the alternative feature selection approaches is higher than 20.
For this reason, we do not report the extracted features by BTLBO-KNN and the baselines from these datasets. We observe that the average number of selected features by the alternative feature selection methods is lower than 20 for the small-sized datasets. Hence, both the accurate classification of disease diagnosis and the best-extracted features (best feature subset) from the proposed BTLBO-KNN and the five baselines are presented in Table  14. It shows that one can use a few features for each medical dataset to obtain good classification accuracy for disease diagnosis. Moreover, as shown again from Table  13 , the average classification accuracy for the selected features by BTLBO-KNN TABLE 15. Wilcoxon Signed-rank test with a P-value equal to 0. 05 comparing BTLBO-KNN with the baselines in terms of Avg acc on 20 independent runs.
To summarize, we investigate the impact of the feature number on the performance of the baselines and the BTLBO-KNN according to the average accuracy classification. Based on the overall summary reported in Table  17 , BTLBO-KNN exhibits the best rank among other competitors in the given metrics, particularly for large-sized datasets. As a conclusion, BTLBO-KNN can drastically reduce the number of selected features compared to the original ones on medical or gene-expression datasets. Doing so also provides high classification accuracy of the KNN classifier. Indeed, it effectively retains the most relevant and prominent attributes in the medical datasets under test, especially when the number of original features is high.
We evaluate the performance of BTLBO-KNN on 24 medical and gene-expression datasets and compare it to stateof-the-art wrapper-based feature selection algorithms using fitness, classification accuracy, and the number of selected features as metrics. Results demonstrate the superiority of BTLBO-KNN. It is particularly effective at reducing the number of features in large-sized datasets while maintaining high classification accuracy values. In future work, we plan to investigate the performance of BTLBO-KNN on other large-scale binary optimization problems, such as OneMax, Knapsack, and uncapacitated facility location problems. Additionally, we plan to develop a multi-objective version of BTLBO-KNN that considers other machine learning classifiers to address the multi-objective feature selection optimization problem.
The sample We now seek to test our method using real accretion rate observations, and for this we use the compilation of data from  Manara et al. ( 2022 ). Their complete sample contains 865 discs, of which 288 have measured accretion rates. Ho we v er, the sample co v ers a wide range in stellar mass, so the global distribution of accretion rates is dominated by the well-known Ṁ ∝ M 2 * trend (e. g. Mohanty, Jayawardhana & Basri 2005 ; Muzerolle et al. 2005 ; Manara et al. 2017 ). To use the accretion rate distribution as a test of disc evolution we must therefore consider a restricted range in stellar mass. Formally the solutions in Section 2 do not depend on the stellar mass, but our model parameters are based on studies of T Tauri stars with masses 0. 5-1 M. We   2022 ): the solid line shows only detections, while the dashed histogram shows both detections and upper limits.
AC K N OW L E D G E M E N T S We thank the anonymous referee for an insightful re vie w. RA and GR acknowledge funding from the  Science & Technology Facilities Council (STFC)  through consolidated grant no. ST/W000857/1. GR acknowledges support from the  Netherlands Organisation for Scientific Research (NWO , program number  016. Veni. 192. 233 ) and from an  STFC Ernest Rutherford Fellowship  (grant no. ST/T003855/1 ). PJA acknowledges support from  NASA   TCAN  award  80NSSC19K0639. GJH is supported by general grant no. 12173003  from the  National Natural Science Foundation of China. BT is a Laureate of the  P aris Re gion fellowship  program, which is supported by the  Ile-de-France Region , and has received funding under  Marie Skłodowska-Curie  grant agreement no. 945298. This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon Europe Research & Innovation Programme  under grant agreements no.
Untargeted Metabolomics by High Resolution Mass Spectrometry Differences between must and wine samples were further investigated by untargeted metabolomics analysis using UHPLC-HRMS. Therefore, all samples were randomly injected, in a single batch, along with QC and control samples, as described in metabolomics studies  [11]. Positive ion mode was selected because it is better suited for the detection of anthocyanin pigments. The features obtained were filtered as described in the Materials and Methods section. The resulting 729 feature dataset was used for multivariate statistical analysis. The PCA plot of the metabolomics spaces obtained by sample injections in the positive ion mode is shown in Figure  3. The projections of QC samples, consisting of a mixture of all analyzed samples and regularly injected throughout the sequence, were very close together in a central position, indicating the reliability of the dataset.
This unsupervised analysis also showed that the Cinsault must and wine samples, on the one hand, and the Syrah must, on the other hand, are separated from all other samples along the first principal component, while musts, pilot-scale wines, and laboratory-scale wines are distinguished on the second axis. Molecular formula tentatively attributed from LC-HRMS signals by the Compound Discoverer software suggested that many of the metabolites belong to phenolic compounds but also to compounds containing nitrogen atoms such as amino acids, peptides, and indoles, as reported by earlier wine metabolomics studies  [24, 25]. Annotation was performed manually by comparing retention times, mass spectra and, when available, UV-visible spectra with an internal database and with data published in the literature  [24, 25, 26, 27].
A given molecule could be represented by several features with the same retention time but different m/z values, corresponding to molecular or pseudomolecular ions, adduct and fragment ions, and some isotopic clusters. The heatmap The projections of QC samples, consisting of a mixture of all analyzed samples and regularly injected throughout the sequence, were very close together in a central position, indicating the reliability of the dataset. This unsupervised analysis also showed that the Cinsault must and wine samples, on the one hand, and the Syrah must, on the other hand, are separated from all other samples along the first principal component, while musts, pilot-scale wines, and laboratory-scale wines are distinguished on the second axis. Molecular formula tentatively attributed from LC-HRMS signals by the Compound Discoverer software suggested that many of the metabolites belong to phenolic compounds but also to compounds containing nitrogen atoms such as amino acids, peptides, and in-doles, as reported by earlier wine metabolomics studies  [24, 25].
Annotation was performed manually by comparing retention times, mass spectra and, when available, UV-visible spectra with an internal database and with data published in the literature  [24] [25] [26] [27]. A given molecule could be represented by several features with the same retention time but different m/z values, corresponding to molecular or pseudo-molecular ions, adduct and fragment ions, and some isotopic clusters. The heatmap (Figure  4 ) obtained by unsupervised hierarchical cluster analysis on the 729 selected features (Figure  3 ) confirmed that injections of triplicate biological samples are grouped together and showed separations between musts and wines and between varieties. However, the Grenache wine made at laboratory scale was the most different sample, while the pilot-scale Grenache wine clustered with the Grenache must.
Nevertheless, the data obtained in the present study suggest that the Cinsault must was particularly oxidized compared to the other two. Comparison of the relative intensities of major hydroxycinnamic acid derivatives detected by HRMS with absorbance values determined by spectrophotometry indicates that most of the absorbance at 320 nm measured in the Cinsault samples is actually due to other compounds. These unknown compounds might correspond to the higher molecular weight compounds identified as markers of Cinsault musts and wines (cluster E2) and may be enzymatic oxidation products formed from hydroxycinnamic acids during must preparation. They might also correspond to the higher molecular weight compounds identified as markers of Cinsault musts and wines (cluster E2). Cinsault musts and wines also contained much lower amounts of all anthocyanins, although their pigment levels measured by spectrophotometry (A520) were intermediate between those of Syrah and Grenache samples.
Data Analysis MS data from real samples, QC and blank samples were processed by Compound Discoverer software (v. 3. 2. 2. 421, Thermo, Waltham, MA, USA) with a WorkFlow constituted by several steps: spectrum processing nodes which extract the mass spectral data from the input set file and align retention times of the compounds in the sequence; compound detection nodes which detect compounds by the extraction of masses as well as manage missing values, group adduct and isotopic peaks; and peak area refinement nodes which apply QC correction and remove background compounds found in the blank samples. Compound Discoverer parameters are available in the Supplementary Data (File S1). After Compound Discoverer data treatment, the number of features dropped from 1232 features to 755, each corresponding to a retention time associated with an m/z value.
Features were then filtered based on the variability of the biological triplicate (CV < 10%) and the two-way ANoVA results (p-value < 0. 05), keeping only features (729 features) with at least one modality exhibiting low variability and significant differences with another modality. Annotation was performed manually by comparing retention times and mass spectra accuracy with a mass tolerance of 5 ppm based on the previous experience of the group with the specific instrumentation mass resolution and in accordance with the four levels of annotation described by Sumner  [44]  and presented in the Supplementary Material (Table  S1  acquisitiondataset. xlsx).
Statistical Analysis A one-way analysis of variance on spectrophotometric data was performed with RStudio version 4. 0. 3 software (www. rstudio. com (accessed on 10 October 2020)) using a Student-Newman-Keuls post hoc test in the Agricolae package. ANOVA results were considered significant at p-value < 0. 05. Untargeted composition analysis data (729 features dataset) were processed using Compound Discoverer version 3. 1 software for two-way ANOVA, unsupervised hierarchical clustering and principal component analysis. Hierarchical clustering was generated using complete linkage and Euclidean distance methods and PCA using centered and scaled values.
Such events result in the mixing of the water column in Elandsbaai and the advection of nutrients into the surface layer. Wind data from 15 February to 15 April 2020 were used to assess the potential for upwelling events in the sampling region before and after sampling (Table  S1 ). During the 10-day experiment, seawater samples were collected daily using a hand-held 5 L Niskin bottle from seven depths over the water column: surface (0 m), subsurface (5 m), deep  (10, 15, 20, 25 m, and bottom ∼30 m) , unless otherwise specified. Hydrographic data (temperature and salinity) were generated using a Seabird conductivity-temperature-depth (CTD) profiler. Potential density (σ θ ; kg m -3 ) was then derived from absolute salinity and conservative temperature with respect to a reference pressure of 0 dbar following IOC (2010).
Data Analysis A multivariate permutational analysis (PERMANOVA;  Anderson et al. , 2008)  was performed to test for differences in the phytoplankton FA composition with depth (n = 4) and day (n = 10). Canonical analyses of principal coordinates (CAP) were used to explore differences in the phytoplankton FA composition between Depth and Day, focusing on the 0 and 5 m samples where most of the FA were recorded. FA analyses were based on Euclidian dissimilarities calculated from percentage data. Only FA that contributed >1% to the total FA (TFA) were included in the analyses. The analyses were conducted using the PERMANOVA + add-on package of PRIMER v6  (Anderson et al. , 2008). No statistical analyses were performed on the other data. PRIMER v6, SigmaPlot, Ocean Data View (OVD), and R software were used for visualization of the data.
e. , upwelling followed by relaxation) typically last 3-10 days  (Nelson & Hutchings, 1983). The high non-diatom TM recorded pre-upwelling could be linked to the post-diatom bloom community associated with a previous upwelling event. Diatom TM were also present pre-upwelling, albeit at lower concentrations than the non-diatom TM (20% versus 50% of TFA), even though the microscopy data suggest that large diatoms (>50 μm) were not abundant pre-upwelling. The diatom Chaetoceros spp. was the main type of phytoplankton identified by microscopy and was present in extremely high numbers post-upwelling (Figure  4 ). Since this species can occur in both chains and as single cells  (Stoermer & Julius, 2003) , it could also have been present in relatively high abundances pre-upwelling but not represented in the microscopy samples.
The information available on LC omega-3 production for the BUS (NBUS + SBUS) is limited  (Puccinelli et al. , 2021) , with the only published data being the LC omega-3 content of suspended organic matter (1-5 μg L -1 ) collected from a rocky shore environment in the SBUS  (Puccinelli, McQuaid, et al. , 2016; Puccinelli, Noyon, et al. , 2016). Here, we report TFA concentrations of up to 215. 5 and 175. 3 μg L -1 and LC omega-3 of up to 49. 3 and 44. 5 μg L -1 pre-and post-upwelling, respectively (Figure  6 ). That is, we measured LC omega-3 concentrations that are 10 times higher than previously reported for the SBUS. The discrepancy between our measurements of those reported in the earlier SBUS study may be due to sampling location, with  Puccinelli, McQuaid, et al. (2016)  focused on a rocky shore environment, in contrast to the open water site investigated here.
, 2022). The results of our study indicate that the LC omega-3 supply is not as limited as previously surmised, as we measured LC omega-3 concentrations that were 10 times higher than earlier estimates from the SBUS. Indeed, our new concentration data for the SBUS, combined with the general lack of information available on LC omega-3 production in upwelling systems, suggest that global LC omega-3 production may be significantly underestimated. Similar high-resolution (temporal and spatial) studies are needed to better quantify the stock of LC omega-3 available for higher trophic levels (including humans) across the global ocean. "Investissements d'Avenir" and SAD programme fellowship to EP. Additional funding was provided by the South African National Research  Foundation (115335, 116142, 129320) , the University of Cape Town (UCT) Research Committee, and a Royal Society/African Academy of Sciences Future Leaders Africa Independent Researcher (FLAIR) fellowship to SF.
Crystallographic orientation of sample N19 were collected via EBSD analysis using a Zeiss Merlin SEM coupled with an Oxford Instruments Nordlys S detector at the University of Tromsø. EBSD data were collected after SEM-CL on the same representative microstructures. All thin sections were chemically polished with colloidal silica prior to EBSD analysis. Crystallographic patterns were acquired and processed using Aztec software (Oxford Instruments) on rectangular grids with a step size of 1 μm using an accelerating voltage of 20 kV, a 70° sample title angle, and a 24-29 mm working distance. EBSD results for plagioclase are presented as phase maps, pole figures, and misorientation angle distribution histograms. Pole figures are orientated with their horizontal diameter corresponding to the trace of the mylonitic foliation (E-W) and parallel to the stretching lineation.
Synchrotron X-Ray Microtomography (SμCT) In order to document the porosity and mineral phase distributions, we scanned cylindrical subsamples from LM1726 and N19 in four μCT data sets each at the TOMCAT beamline X02DA of the Swiss Light Source at the Paul Scherrer Institute in Switzerland. The cylinders (∼5 mm height and 2 mm diameter) were drilled perpendicular or near-perpendicular to the foliation and to the pseudotachylyte vein boundary. Two N19 volumes acquired at 10X magnification and two acquired at 20X magnification. All four LM1726 volumes were acquired at 10X magnification. The samples were imaged with a monochromatic beam energy of 28 keV. A charge-couple device camera collected images at 0. 1° angular steps over 180° for a total of 1,801 projections with dimensions of 2,560 × 2,160 pixels.
Slice interval was every 100 nm for both locations, with Site 1 having a pixel dimension of 4. 9 × 6. 2 nm 2 and Site 2 having a pixel dimension of 4. 5 × 5. 7 nm 2. Images were then segmented using Ilastik  (Berg et al. , 2019)  and visualized with Avizo software. Segmented volumes have a "stepped" appearance because of the 100 nm between slices that prevented Avizo from interpolating the distance between.
Overall survival is defined as the time since randomization into the clinical trial to event of death or censor (end of trial or follow-up), and is considered to be the most reliable cancer end point. However, the introduction of second-line treatment after disease progression could influence survival and be considered a confounding factor. The aim of the current study was to set up a multistate model framework, using data from the IMpower131 study, to investigate the influence of second-line immunotherapies on overall survival analysis. The model adequately described the transitions between different states in patients with advanced squamous non-small cell lung cancer treated with or without atezolizumab plus nab-paclitaxel and carboplatin, and characterized the survival data. High PD-L1 expression at baseline was associated with a decreased hazard of progression, while the presence of liver metastasis at baseline was indicative of a high risk of disease progression after initial response.