dataesr/scientific-paragraphs-categorization huggingface dataset:
Classification accuracy and the quantity of chosen characteristics were utilized to build a fitness function. e proposed GA-SVM approach is described in greater detail below: Step 1. A binary string representation of the parameters and chromosomes that reflects the SVM parameters is created. Step 2. e initial population of chromosomes is randomly generated, and then, the population is initialized. Step 3. e parameters that were chosen (C and c) are located. Step 4. To get the trained optimal SVM, you will need to run your data through it with the new parameters. is model predicts the test sets. Step 5. Fitness is assessed. To compute the k-fold crossvalidation accuracy, the optimum chromosomes and optimal pair (C, gamma) are entered into an SVM classifier for each chromosome. Step 6. e maximum number of generations N must be reached before the end of the game, or the fitness value of the preceding M generations must be lower than the current generation.
e. , the proportion of correctly classified images). It can be evaluated using the following formula: |Acc � (TP + TN) (TP + TN + FP + FN) , ( 6 ) where TP indicates that the model correctly predicted the positive class. FP indicates that the model incorrectly predicted the positive class. FN indicates that the model incorrectly predicted the negative class. TN indicates that the model correctly predicted the negative class. All of these values are defined in Table  4. Using the model described in this research, we conducted a series of experiments. Comparing the outcomes of various machine learning methods is also part of our research, for that we use Sklearn (https://scikit-learn. org/ stable/) as Python package. scikit-learn (also known as Sklearn) is the most useable and stable machine learning toolkit available for Python.
Data Availability e Tuberculosis "TB 2020" data set used to support the findings of this study were supplied by the ImageCLEF campaign under license and so cannot be made freely available. Requests for access to these data should be made to https://www. imageclef. org/2020/medical/tuberculosis/; all the information necessary to have the database and the access authorization are mentioned in the following link: https://www. imageclef. org/2020/medical/tuberculosis/. e necessary codes for the machine learning models are.
We dissolved the PAs that overlapped or shared boundaries with another to avoid the potential effect of multiple PAs in the surroundings of the core PA (Fig. 2A  and B ). Next, we created a multiring buffer of 2. 5 and 5 km from the PAs boundaries, which reduced the total number of PAs-conglomerates from 159 to 82 (Fig. 2B  and C ). This reduction of PAs-conglomerates is a consequence of PAs in Spain often being separated by less than 10 km, so that buffers from different PAs merge. The list of PAs agglomerations with their names, types, years, and numeric codes is provided in the S1 Appendix. We used data from Corine Land Cover (CLC)  1990, 2000, 2006, 2012 and 2018  (Fig. 2D ) for land use/land cover (CLC1990, CLC2000, CLC2006, CLC2012, and CLC2018). We then selected codes from 111 to 142, corresponding to artificial surfaces (as labelled in Corine Land Cover database), and started a process of correcting errors.
(5) Main road density: This variable was obtained by dividing the total length of the roads inside the PA (in kilometres) by the total surface area of the PA. This variable has been used in other studies to analyse the impact of species conservation and biodiversity  (Bennett, 2017)  and is also related to urbanisation Step A shows a single PA that shares a boundary and one PA (5) separated from the core PAs. Step B shows how PAs that share boundaries were dissolved to define a single boundary, then buffers of 2. 5 and 5 km were created. Step C shows how those PAs that share buffers (1 and 5 in step B) finally become a single PA agglomeration for the analysis. Step D shows the method followed for the correction of CLC database for the years  1990, 2000, 2006, 2012 and 2018. Step E shows the intersection of CLC with PAs and their buffers (urbanised land in black colour).
Patients and methods: In this study, we reported BRCA 1/2 mutational status, HRD score and mutational signature 3 (S3) expression, in all early breast cancer (eBC) subtypes from the TCGA database, with a particular focus in ER+/HER2-. In this subtype, bioinformatics analyses of tumor transcriptomic, immune pro le, and mutational landscape, were performed, according to HRD status. Overall survival (OS), progression freeinterval (PFI), and variables associated with outcome were also evaluated.
There is therefore a theoretical rationale for using immunotherapy in these BRCA mutated tumors, or tumors with BRCAness. This is currently being tested in an ongoing breast cancer trial (NCT03025035). In addition, preclinical work indicates that part of the antitumor effect of PARPi is linked to the activation of immune response, in particular via activation of the STING pathway  [39, 40]. The combination of immunotherapy plus PARPi therefore appears attractive, and has given encouraging initial clinical results  [41] , including for patients with BRCA WT tumors  [42, 43]. Numerous clinical studies are currently testing this concept (NCT02484404, NCT03594396, and NCT03167619), but without prior de nition of the population likely to bene t from these associations. Our study seems to show that the immune context of ER+/HER2-BRCA WT breast cancers with a high HRD score closely resembles that of mutated BRCA cancers, and theoretically would therefore be more favorable to response to checkpoint inhibitors than BRCA WT HRD-low tumors.
Our results raise the question of the value of immunotherapy, and of PARPi + ICB combinations for this very speci c population that we have characterized. Lastly, when compared to BRCA mutated or BRCA WT HRD-low tumors, our study revealed that BRCA WT HRD-high breast tumors showed speci c genomic and transcriptomic features, including differences in the expression of genes involved in estrogen-response, immune response and proliferation/cell-cycle pathways. These results may help to provide avenues for speci cally considering certain targeted therapies in this tumor subtype, such as anti-CDK 4/6, and possibly combining them with immunotherapy approaches. Preclinical studies indeed showed a rationale for combining these treatments with ICB  [44, 45] , but here again, ongoing phase I/II combination studies (NCT03685331, NCT04481113, NCT01434316) proposed the combination without prior selection of patients based on any biomarker.
Likewise, in vitro, CDK 4/6 inhibitor + PARPi combinations have been shown to be synergistic on breast cancer tumor lines  [46]. In vitro and in vivo ovarian cancer models combining PARPi with CDK 4/6 showed synergistic effects with palbociclib by inducing HRR de ciency through downregulation of MYCregulated HR pathway genes, leading to synthetic lethality with olaparib  [47]. In clinical trials, a combination of olaparib, palbociclib and fulvestrant is being investigated in patients with BRCA mutated metastatic breast cancer (NCT03685331). In conclusion, while de nitive evidence will require prospective clinical evaluation of PARPi and/or ICB and/or CDK 4/6 inhibitor combinations in cohorts of patients with HRD-high and HRD-low vs BRCA1/2 mutated ER + breast cancer, our results could have important implications for translational research and/or the design of future clinical trials, and highlight possible novel biological differences among ER+/HER2-breast cancer related to their ability to respond to DNA-damaging agents and ICB.
Patient RNAseqV2 data with RSEM normalization, MAF and corresponding clinical data were downloaded from the TCGA data portal (https://portal. gdc. cancer. gov/). Tumor mutational burden (TMB) for each patient was downloaded from the National Cancer Institute  [48]. Homologous recombination de ciency (HRD) scores and the 3 components of HRD/genome scarring scores, namely HRD-Loss of heterozygosity (LOH), Large Scale Transition (LST) and Telomeric Allelic Imbalance (TAI) were calculated by Knijnenburg et al  [19]. Mutational signatures were generated using DeconstructSigs  [49]  (v1. 8. 0) and COSMIC signatures identi ed by Alexandrov et al  [50]. Somatic and germline pathogenic mutations of BRCA 1 and BRCA 2 were obtained from Kraya et al  [51]. PAM50 subtypes were taken from Knijnenburg et al.
[19]. Thirty-seven driver genes were identi ed using MutSigCV  [52]. Genes with q < 0. 1 and that were mutated in at least 1% of the TCGA cohort were considered signi cantly mutated. A 40-gene signature speci c to lymphocyte, myeloid, stromal and cancer cells was also generated  [53]. In brief, 10 genes were selected as markers respectively of lymphocyte, myeloid, stromal and cancer cells and their RNA expression were averaged for each sample to compute a score for each cell type. TIL score was then de ned as the ratio of the sum of lymphoid and myeloid scores to the total of lymphoid, myeloid, stromal and cancer scores. Abundances for 8 major immune and 2 stromal cell populations were estimated for each patient based on RSEM log2-transformed gene expressions and using "MCPcounter" R package  [54].
Survival curves were estimated by the Kaplan-Meier method and compared with the Log-rank test (univariate analysis). Optimal cut-offs for gene expression were estimated based on maximally selected rank statistics  [58]. Multivariate Cox models were tted including variables with p-values < 0. 10 in univariate models using lasso penalty. To limit optimism and over tting, 500 bootstrap samples were generated, and only variables mostly (> 90%) selected through the 500 corresponding lasso Cox models were kept in the nal model. Then, mean hazard ratios (HR) with con dence intervals based on 5th and 95th percentiles were estimated over bootstrap samples. Note that survival information was missing for one patient of the ER+/HER2-cohort. Differential expression analysis was performed with the DESeq2 R package  [59].
Three groups were compared. Raw p-values associated with each gene were adjusted using Bonferroni correction, as advised. Gene Set Enrichment Analysis (GSEA) was used to identify biological pathways that are enriched in the lists of differential gene characteristics of each group of patients. Pathways from the Hallmark database were used. GSEA was conducted using clusterPro ler R package  [60]. Statistical analyses were performed using R software (http://www. R-project. org/) and graphs were drawn using GraphPad Prism version 9. 3. 1 (GraphPad Software, LLC, San Diego, USA).
Declarations Availability of data and materials: The datasets analyzed during the current study are available from the corresponding author on reasonable request. The dataset supporting the conclusions of this article is available in the TCGA database portal (https://portal. gdc. cancer. gov/). Authors' contributions: E. B, L. G. , H. M, R. B, L. A. , I. D. , D. M. , C. K. , S. I. , A. H. , A. B. , V. D. , F. G, C. T and S. L contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. Ethics approval and consent to participate: The study was approved by the CNIL (French national commission for data privacy) and the local ethics committee, and was performed in accordance with the Helsinki Declaration and European legislation.
Competing interests: The authors declare no competing nancial or non-nancial interests. Statement of translational relevance: date, the vast majority of research studies describing the links between DNA damage repair or HRD score, and tumor biology, have concerned either triple negative breast cancers or cancers with mutation of BRCA1/2. As we recently described a subgroup of BRCA-pro cient but with high HRD score in metastatic luminal BC, we sought to describe in greater detail the genomic, transcriptomic, and immunological landscapes of ER+/HER2-in early stage BC, given the BRCA 1/2 mutation and HRD or mutational signature 3 (S3) status. As in the metastatic setting, we found a subset of BRCA-pro cient ER+/HER2-eBC in the TCGA database, who had high HRD score. These tumors display different immune, mutational, and tumor molecular signature landscapes, compared to BRCA-mutated and BRCA-pro cient HRD-low tumors.
Since we had only a few samples from some of the vertically exposed bodies in the study area (e. g. , the tabular limestone), we decided to also include reef samples belonging to equivalent limestone bodies exposed in other, gentler areas of Lime Peak (e. g. , the tabular bodies exposed in the northwest side of Lime Peak; see Supplementary Materials: Figure  S1 ). The samples were point-counted to assess the relative abundance of each reef component. Point-counting was performed using the image analysis software JMicrovision (User-friendly software for analyzing large images, version 1. 3. 3-Switzerland). According to the software's pointcounting evolution plot, stabilization is achieved between 150 and 200 counted points. Hence, a total of 200 points by thin section were counted using a random grid.
In the interstitial sediment group are skeletal and non-skeletal grains, such as dwellers (e. g. , foraminifera, molluscs, ostracods and brachiopods, among others), peloids and ooids, along with different forms of micrite (clotted and peloidal, interstitial and dense micrite) and cement (either cavity-filling or intergranular cements). In order to be able to compare the reef associations of Lime Peak with those of other coeval reefs in Panthalassa and Tethys (see Section 8), we performed a similarity analysis for reef taxa (calcareous sponges, microproblematica and foraminifera). To do so, we used an updated version (see Supplementary Materials: Figures  S6  and S7 ) of the database originally compiled by  Peybernes et al. [22]  that includes the occurrences of reef taxa in different Upper Triassic (Norian-Rhaetian) reef sites.
In our updated version, we added the new taxa from Lime Peak (mostly reefal foraminifera) that were identified in this study. To be consistent with the work of  Peybernes et al. [22] , we made sure to add only the taxa coming from the reef microfacies (i. e. , MCF 6A, 6B, 7) and the microfacies found closely associated with the latter (MCF 5). Foraminifera from other depositional environments were not added to the database (e. g. , lagoon and slope deposits). Moreover, we added some new entries (sponge taxa) from Slovenia  [89]  that were not included in the original database. Hierarchical cluster analysis (hCA) was obtained using PAST software (v. 4. 04) with a UPGMA algorithm and Dice (=Sørensen) coefficient of taxonomic similarity. For details regarding this type of analysis, the reasons for its selection and the choice of included taxa, the reader is referred to  Peybernes et al.
Composition of the Reef Facies The average results from the point-counting analysis (for the whole dataset see Supplementary Materials: Figure  S3 ) are shown in Table  2  and Figure  17. At Lime Peak, the main framebuilders are by far sponges, microbialites and tubular crusts. Of the latter, sponges account for 25. 22% and 35. 05%, microbialites for 20. 14% and 15. 20%, and tubular crust for 10. 61% and 15. 00% of the massive and tabular mounds limestone, respectively. Corals (0. 66% in the massive and 1. 15% in the tabular limestone) and encrusters (2. 67% in the massive mound limestone (MM) and 2. 55% in the tabular mud limestone (TM)) are only of secondary importance. Within the MM, in the interstitial sediments fraction, we find abundant clotted and peloidal micrite (17. 13%) followed by interstitial micrite (5.
occurrences are not necessarily indicative of very shallow water. The depositional depth of each finding must be evaluated using other co-available data (e. g. , microfacies and biotic association). During LSL, Retiophyllia sp. is very rare and was only found (as fragments) in MCF 3. During this period, the corals' contribution within the reef is given by solitary corals or colonial forms that have an encrusting or massive growth form (e. g. , Astraeomorpha sp. ). Sponges are very abundant at Lime Peak. Inozoans, sphinctozoans, disjectoporoids, Spongiomorpha ramosa and chaetetids peak in number within the reef microfacies. As already put forward by Reid  [51] , there is a major change in the sponge community within the reef between HSL vs. LSL. In particular, relatively tall sphinctozoan (e.
Acknowledgments: The authors acknowledge the  Government of the Yukon as well as Kwanlin Dün and Ta'an Kwäch'än First Nations  for granting access to their lands. We do thank  Trans North Helicopters  and  Capital Helicopters  for their services and for being so helpful with our field missions. Sampling in the Yukon was conducted under the Yukon-Canada Scientists and Explorers Act license number  17-60S&E. The authors thank  Andrea Fucelli  for the help during the conodont extraction process as well as  Martyn Golding  for the help with their successive identification. We thank  François Gischig  for the great quality of the thin sections he made for this study. The two anonymous reviewers are thanked for their thorough reading and detailed comments that greatly improved an earlier version of this manuscript.
Study procedures Written consent from the eligible participants was gathered through an interview conducted by three well-trained investigators. Afterward, the participants were followed up for data collection at 3-, 6-, and 12-months post-stroke. Clinical information was collected through a data collection form. It included the following: (1) age, gender, place of residence, marital status, number of kids, age of subject's custodian, level of education of the subject and his/her custodian, employment status, number of household members, number of rooms and type of health insurance, (2) lifestyle (eating habits, smoking, practice of physical activity, alcohol and other substances consumption, social support), (3) health indicators (anthropometric indices, family/medical/surgical history, comorbidities, treatment taken by subjects), (4) the disease and its severity (types/subtypes/location/symptoms, length of hospital stay, severity of disease, degree of disability, evaluation of the quality of life (QoL) and (  5 ) the stroke consequences (neuropsychiatric disorders, cognitive disorders, hyperglycemia, fatigue, post-stroke pain, falls, pressure ulcers, pulmonary and urinary infections, deep vein thrombosis, pulmonary embolism, seizures, recurrence of stroke, and death).
946]. The QoL was assessed by the short form (SF12), which consisted of 12 items including eight scales: physical functioning (PF), role limitations due to physical problems (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role limitations due to emotional problems (RE), and perceived mental health (MH), and was divided into two summary scores [physical (PCS) and mental component summaries (MCS)]. They demonstrated the mental and physical functions and overall health-related QoL. PCS and MCS were computed through the scores 12 questions and ranged from 0 (lowest level of health) to 100 (highest level of health) with a cut-off of 50 for PCS and 42 for MCS. The scoring was calculated using the United States (US) norm-based scoring algorithm in the Statistical Package for the Social Sciences software (SPSS)  (47, 48).
Interobserver agreement of hemodynamic measurements with both sequences was substantial for the abdominal aorta (Table  2 ; CV < 15%) and portal vein (CV < 20%). Poor interobserver agreement was observed with the k-t acquisition in the less conspicuous splenic vein (Table  2 ; CV = 26. 8%). Inter-acquisition differences in vessel identification and flow quantification. Flow parameters measured by both observers with both acquisitions in all vessels of interest are shown in Fig. 2  and Tables  3, 4. Table  1. Qualitative scoring of vessel conspicuity and background artifacts with the two acquisitions, as evaluated by two observers. Data is shown as median (IQR) of the vessel conspicuity (0: vessel not seen; 1: severely to moderately blurred; 2: mildly blurred; and 3: well delineated) and background artifacts scores (1: severe artifacts; 2: moderate; 3: minimal or none).
The k-t accelerated acquisition used a PEAK-GRAPPA spatio-temporal acceleration factor R = 5 in the y-and z-directions, prospective ECG gating, but no respiratory control, as previously demonstrated in the aorta  18. Both acquisitions used a velocity encoding parameter (VENC) of 60 cm/s, as previously used in 4D flow studies of liver disease  1, 4, 5, 7, 8, 10, 27. For both 4D flow sequences, the acquisition and reconstruction time was recorded. Inline reconstruction time for the k-t acquisition was 30 min. , while the reconstruction time for the navigator-gated acquisition was 2 min. per dataset. Image analysis. Two independent observers (O. B. , an MR physicist with 5 years' experience, and D. S. , a body radiologist with 6 years' experience in abdominal imaging) performed vessel segmentation for the respiratory-navigated and k-t accelerated acquisitions.
Images were analyzed using prototype software (4D Flow Demonstrator, Siemens Healthineers) for 4D flow data visualization and vessel segmentation using a graph-cut centerline model  28. The software performed corrections for eddy currents and motion, as well as for phase aliasing, by correcting for pairs of opposite changes in velocity larger than 2xVENC in the velocity-time curves  28, 29. After performing the corrections, the portal, superior mesenteric, splenic and middle hepatic veins, the supraceliac aorta, celiac trunk, hepatic and splenic arteries (Fig. 4 ), were identified and segmented from a 3D phasecontrast angiogram constructed based on the 4D flow velocity data. Anatomical T 2 -weighted and post-contrast T 1 -weighted imaging was used as a reference for vessel identification. Particle traces were visualized in all vessels to confirm physiological direction of flow, and to identify if a hepatofugal flow pattern was present in the portal vein.
Optimization of the leaching parameters using response surface methodology Response surface methodology (RSM) was used for designing the experiments instead of the traditional one factor at a time approach. The factors considered were temperature (40-85 • C), pulp density (20-40 g/L) and glycine concentration (0. 3-1. 0 M). The experiment design was generated by the MINITAB 18 (USA) software. A full factorial central composite design (CCD) (face centred, unblocked) was used. Table  1  presents the generated coded levels and range of leaching variables for the experimental design. Further leaching experiments were also conducted at the optimum conditions based on the results of experimental design. Another leaching study was conducted to determine the effect of pH control on the leaching of Co from LiBs at the following conditions, temperature: 100 • C, pulp density: 13.
System evolution When considering the system evolution between time and +1 , the resulting linear system for the current velocity and rate of change of director field is solved when considering the shell thickness to be fixed to its previous value i. e. = +1 , ̇ +1 = arg min , ̇ ( , ̇ ; ) (56) where ( , ̇ ; ) = ∫ ( , ̇ ; ) d ( 57 ) is the system total Rayleighian, with given by (  54 ). Here, we assume no external loading of the shell, such that all shell deformations are powered by internally generated active stress. Note that, due to the linear expressions of the strain rates (  51 ), (  52 ), (  53 ) and the quadratic form of the dissipation potential , the resulting system is in fact linear. We then update the geometry in an asynchronous and Lagrangian manner, by solving the discretized thickness evolution equation and updating the midsurface position and the director using (50) as follows: +1 - Δ = -+1 Δ , +1 -+1 + (1 - +1 ) (58a) +1 = ( + Δ ̇ +1 ) (58b) +1 = + Δ +1 (58c) The full finite-element implementation is done within the FEniCS software package  [65, 38]  and the meshes are produced with the Gmsh generator  [66].