dataesr/scientific-paragraphs-categorization huggingface dataset:
These components capture the most significant variance within the dataset and effectively reduce each EIS curve from a 320-dimensional point to a 2D point. Visualization in 2D Space: The result is a 2D representation of each EIS curve, where similar curves cluster together. This visualization allows for an intuitive understanding of how different equivalent circuits relate to each other based on their impedance characteristics. Analyzing Clusters and Circuit Behaviors: By reducing each curve to a single point in 2D space, it becomes feasible to assess the performance of PCA in the context of EIS curves clustering. As can be seen from the figure s4, all curves formed a single cluster  In Figure  s5 , each bar represents the proportion of variance explained by a specific principal component.
t-SNE t-Distributed Stochastic Neighbor Embedding (t-SNE) is a non-linear machine learning algorithm used primarily for the task of dimensionality reduction and visualization of high-dimensional datasets  3, 6. Unlike PCA, which is a linear projection, t-SNE is particularly well-suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot. It's especially powerful for data sets where the relationships are non-linear and complex. t-SNE works by converting the high-dimensional Euclidean distances between data points into conditional probabilities that represent similarities. The similarity of datapoint xj to datapoint xi is the conditional probability pj| i , that xi would pick xj as its neighbor if neighbors were picked in proportion to their probability density under a Gaussian centered at xi.
However, it's worth noting that t-SNE does not preserve distances but probabilities, which means that it tends to expand clusters of points that are far apart while compressing clusters of points that are close together. t-SNE is particularly useful for visualizing complex data such as images, sound, and genetic data where the data points can form natural clusters in high-dimensional space. However, it can be computationally intensive, and its results can sometimes be sensitive to the choice of parameters. Despite these challenges, t-SNE remains a popular tool for data exploration and pattern recognition in multidimensional datasets.
Hyperparameter tuning In the process of hyperparameter tuning for t-SNE, it's crucial to select appropriate metrics that align with the objectives of the analysis. Kullback-Leibler (KL) divergence, the core of t-SNE's optimization algorithm, measures the difference between the high-dimensional and low-dimensional distributions of the data. While KL divergence is central to t-SNE's functionality, relying on it for hyperparameter tuning is not advised, primarily due to the risk of overfitting. Overfitting in this context refers to a scenario where the model becomes excessively complex, capturing noise or anomalies specific to the dataset, rather than identifying underlying patterns that generalize well. This can result in a low-dimensional representation that is too finely tailored to the peculiarities of the specific dataset, misleadingly exaggerating certain aspects while ignoring others.
UMAP Uniform Manifold Approximation and Projection (UMAP) is a relatively recent technique used for dimensionality reduction, like t-SNE, but it is often faster and scales better to larger datasets  4, 7. UMAP is grounded in mathematical concepts from topology and geometry, particularly focusing on the structure of the data manifold. UMAP starts by constructing a high-dimensional graph representation of the data, where each point is connected to its nearest neighbors according to a particular metric, typically Euclidean distance. It then optimizes a low-dimensional graph to be as structurally similar as possible, preserving the topological structure of the high-dimensional graph. The key idea behind UMAP is to approximate the manifold on which the data lies in a higherdimensional space and then find a lower-dimensional representation that has a similar topological structure.
UMAP has several advantages over other dimensionality reduction techniques:  It preserves more of the global structure of the data compared to t-SNE, which tends to focus on local structure.  It is computationally efficient and can handle larger datasets.  It allows for a meaningful geometric interpretation of the data clusters and distances between points. UMAP is particularly useful for visualizing clusters or groups within high-dimensional data and is widely used in fields such as genomics, bioinformatics, and machine learning. It is also used as a preprocessing step before applying other machine learning algorithms to improve their performance by reducing the dimensionality of the input data.
Effect of distance metric Manhattan distance is often more effective for high-dimensional, sparse datasets. In such spaces, the Euclidean distance can become inflated due to the 'curse of dimensionality', where the distance between most points tends to be similar. Manhattan distance, being a sum of absolute differences, can better differentiate between points in sparse spaces. As can be seen from figure  s8 , UMAP with Manhattan distance metric improved performance of UMAP: all equivalent circuits are well separated, including R0-p(R1,C1)-p(R2,C2)-p(R3,C3) and R0-p(R1,C1)p(R2,C2)-Wo1, separation of which was relatively bad with Euclidian distance.
The flattened output from the pooling layer is then passed through a fully connected dense layer with 64 units, further processing the features into a format suitable for classification. Another dropout layer follows, continuing to mitigate the risk of overfitting. The final layer is a dense layer with a number of units equal to the number of classes in the EIS dataset. It employs a softmax activation function, which is commonly used for multi-class classification problems. The softmax function outputs a probability distribution over the classes, which is used to determine the most likely class for a given input. The model is compiled with an L2 regularization parameter, which penalizes large weights, and is trained using the Adam optimizer, a popular choice for deep learning applications due to its adaptive learning rate capabilities.
Acknowledgements This work was financially supported by the  Agence Nationale de la Recherche 10 (ANR)   Jeunes chercheuses ,  jeunes chercheurs ,  ANR JCJC, program  ( OCTAWA  project,  ANR-11 22-CE290010-01 ). The authors also acknowledge  Dr. J-M Noël  and  Dr. J-F Lemineur  for their fruitful discussions.
Study design EMN23 is a retrospective, observational study aiming to collect real-world evidence for the management of systemic AL amyloidosis in patients who initiated first-line treatment from January 2004 to December 2018 in Europe. The study was sponsored by the European Myeloma Network (EMN). The duration from initiation until the database lock was approximately 24 months (Q2-2019 to Q2-2021). Collection of data and analysis of the main outcomes of interest consisted of patient demographic and clinical characteristics at diagnosis, treatments (at first and second line), and survival outcomes (overall survival (OS); time on treatment (ToT)) analyzed by treatment initiation period, cardiac stage, and hematologic responses. Landmark analyses on time to event endpoints for hematologic responses at 3 and 6 months, were performed for patients with available data.
One of the study aims was to investigate potential differences between the cohorts of patients who initiated first-line treatment in 2004-2010 (pre-2010) and 2011-2018 (post-2010), in order to depict changes in disease management secondary to the wider introduction of bortezomib-based regimens after 2010, especially of bortezomib, cyclophosphamide, and dexamethasone (VCD), in the everyday clinical practice  [11, 12, 14, 22]. Patient data were collected from ten European countries: Austria, Czech Republic, France, Germany, Greece, Italy, the Netherlands, Portugal, Spain, and the United Kingdom (UK). According to local regulations, data were retrieved from patients' medical records (paper/electronic and electronic databases) and were transferred directly to the study database (Germany, Greece, Italy, Spain, and the UK) and/or via an electronic case report form (all countries except Italy, Spain, and the UK).
To the best of our knowledge, this is the first real-world validation of the importance of rapid hematologic responses. Furthermore, the importance of rapid hematologic responses for patients with more advanced disease is emphasized, supporting the primary use of the most effective therapies and early regimen switch in non-responding patients. Even though deeper hematologic responses are associated with improved survival, and despite the fact that these were more frequent and rapid in the post-2010 period, the overall survival did not seem to improve significantly. However, the benefit in survival post-2010 may be underestimated as potential improvements in the OS of stage I patients may be masked by the shorter follow-up time in the more recent cohort. Limitations of the study include the heterogeneity of the cohort, lack of complete datasets for several patients, non-centrally evaluated hematologic response, change of response criteria over time (in 2005 and after 2012  [23, 24] ), and difficulties in the assessment of organ responses.
Solvent extraction Solvent extraction experiments were performed by contacting 10 mL of the aqueous phase with 10 mL of the organic phase in a centrifuge tube (50 mL). The pH of the aqueous phase was adjusted with 10 mol per L NaOH. No precipitation was observed during liquid-liquid extraction experiments (the solution was clear and no solid was formed at the bottom of the ask and at the liquid-liquid interface). The two non-miscible phases were mixed during 15 minutes at room temperature and 200 rpm with a mechanical stirring apparatus (Gerhardt Laboshake) thermostated with a Gerhardt Thermoshake, so that the equilibrium was reached. A mixing time of 15 minutes is enough since literature data showed that the steady state with the investigated extractants can be reach aer contacting the aqueous and organic phases during less than 5 minutes.
For diluent coming from used cooking oil, the different steps of the process are: (i) collection of used cooking oil, (ii) rening step including hydrocracking, isomerization and hydrogenation of used cooking oil to transform triglyceride into hydrocarbon liquid and (iv) purication by high pressure hydrogenation. ELIXORES are produced by highpressure hydrogenation and distillation of diesel or kerosene feedstocks coming from reneries whereas kerosene is produced by a conventional rening process. The LCA (cradle-to-gate) of the different routes described previously have been performed by using the EcoInvent 3. 8 database. Each step contributes to the greenhouse gas emissions as shown in Table  3. It is important to note that the CO 2 emissions are calculated per category of diluent (ELIXORE, kerosene, recycled plastic, etc.
Bioinformatics The amino acid sequences of fruit allergens were retrieved from the WHO/IUIS database and the nr NCBI database (accessed on 18 August 2022). Multiple amino acid sequence alignments were performed with ClustalX [109]. Unrooted phylogenetic trees were built from the multiple amino acid sequence alignments using the neighbor joining method, and were represented with TreeView  [110]. The atomic coordinates of the endo-β1,3-glucanase from banana (Musa acuminata) (PDB code 2CYG)  [7] , Pru av 2 from cherry (Prunus avium) (PDB code 2AHN)  [14] , Bet v 1 from birch (Betula verrucosa) (PDB code 4A88) [111], and Pru p 3 from peach (Prunus persica) (PDB code 2ALG)  [21]  were retrieved from the Protein Data Bank (https://www. rcsb. org) (accessed on August 2022)  [112]. Homology modeling of other allergens, including the potential allergens from the tomato fruit, was performed with the YASARA Structure program [113] using various threedimensional structures of the PDB as templates.
GRP allergens of fruits, seeds and pollen are highlighted in red low, and green, respectively. GRP allergens located in vegetative organs of plants are in bold le For clarity, many other pollen defensin allergens listed in the official WHO/IUIS database hav been included in the tree (Art an 7, Art ar 7, Art c 7, Art gm 7, Art la 7 and Art si 7 from Arte annua, A. argyi, A. capillaris, A. gmelinii, A. lavandulifolia and A. sieversiana, respectively).
GRP allergens of fruits, seeds and pollen are highlighted in red, yellow, and green, respectively. GRP allergens located in vegetative organs of plants are in bold letters. For clarity, many other pollen defensin allergens listed in the official WHO/IUIS database have not been included in the tree (Art an 7, Art ar 7, Art c 7, Art gm 7, Art la 7 and Art si 7 from Artemisia annua, A. argyi, A. capillaris, A. gmelinii, A. lavandulifolia and A. sieversiana, respectively). 2. 7. PR3 Chitinases Plant chitinases essentially consist of endo chitinases that are classically divided into five classes, including class-I, class-II, class-III, class-IV, and class-V [55]: -Class-I and class-IV chitinases exhibit a structural organization in two domains, comprising a N-terminal chitin-binding domain made of a cysteine-rich polypeptide, similarly folded as the hevein domain of the latex from rubber tree Hevea brasiliensis (Hev b 6), linked by an extended linker loop to a C-terminal α-helical folded domain with a chitin-cleavage activity (Figure 29a).
Observations We obtained optical spectroscopy and photometry of SN 2009ip on 2021 September 10 UT 1 , using the IMACS instrument (+ f/2 camera) at the 6. 5 m Magellan Baade Telescope. The spectra were obtained using long-slit observations with a 300 l mm -1 grism, the FourS_2471 slit mask, and a wavelength coverage of 4200-9400 Å. The final spectrum was the result of the median combining of three exposures, with a time of integration of 1800 s each. The combined spectrum was reduced using the kosmos 2 pipeline, following standard procedures of long-slit spectroscopic reduction, including bias and flat-field correction, wave-1 UT dates are used throughout this paper. 2 https://github. com/jradavenport/kosmos length and flux calibration. We did an absolute flux calibration on the 1D spectrum using the r-band photometry and synthetic photometry obtained with the pyphot 3  package.
Acknowledgements. T. P. acknowledges the support by  ANID  through the  Beca Doctorado Nacional 202221222222. J. L. P. acknowledges support by  ANID  through the  Fondecyt regular grant   1191038  and through the  Millennium Science Initiative  grant  ICN12_009 , awarded to  The Millennium Institute of Astrophysics, MAS. This work makes use of the following softwares: kosmos (https://github. com/jradavenport/kosmos),Astropy (Astropy Collaboration 2013, 2018),  SWarp  ( Bertin 2010 ), Astrometry. net (Lang et al. 2010), DAOPHOT (Stetson 1987(Stetson , 2011)), SUPERBOL (Version 1. 7; Nicholl 2018) and Pyphot (https://mfouesneau. github. io/pyphot/).
Height and weight were not measured in this study, but self-reported. Thus, the runners may have under-or overestimated these parameters. Nevertheless, it should be noted that runners are known to selfreport their anthropometric data accurately  [17]. Also, to avoid the possible influence of physical fitness between the marathon and the 100-km race, the two performances had to be performed within a time interval of 9 months. Yet, this time interval may not be negligible, thus allowing for significant changes in physical fitness. Similarly, although environmental conditions during the 100-km races were collected, this information was not available for the marathons. Therefore, it cannot be ruled out that some runners performed in different environmental conditions in the marathon and the 100-km race (e.
e. , equal to 2 m 3 /s, during the prediction step, although random offtake values within the bounds are simulated. This mismatch is compensated by the closed-loop nature of the control strategy, although it is worth noting that the resulting optimal control inputs may be over-conservative. The control problem is designed in Matlab R2020b (64 bits), using the Gurobi 9. 2 optimization package 4 as solver and YALMIP  (Löfberg, 2004)   be realized that the control strategy is able to steer the water levels to the time-varying references with minimal error despite imperfect disturbance knowledge. Moreover, the water levels are kept within the navigation rectangle at all times, and the control inputs (not provided due to space limitations) are kept within admissible bounds. In order to quantify the performance of the controller, the tracking indices given in  Segovia et al.
05% < MAF < 0. 1) of allelic odds ratios 1. 6-2 (effects size)  33. The imputation of SNPs also increased the number of markers to ne map genetic loci identi ed by GWAS to narrow down the regions of interest more precisely  25, [35] [36] [37]. Imputed genotyped individuals thus revealed variants of disease-susceptibility genes not present on the array. For instance, Jonsson et al  38  discovered by imputation in the APP gene the rare variant (rs63750847-A) that protects against Alzheimer's disorder (AD). They also con rmed that a rare mutation in the TREM2 gene (rs75932628-T) was associated with AD by replicating their ndings in cohorts imputed with the 1000GP panel (1kGP) from phase 1 (1000GP1)  39. GWAS from different cohorts studied in meta-analyses showed similar statistical power to the pooling of individuals from different genome-wide association datasets  14, 40.
New structural variants were further uncovered with the 1000GP3  60  and the genome of the Netherlands project (GoNL)  61. The 1000GP3 was then integrated into the large Haplotype Reference Consortium (HRC) panel  62  , while the re-phased 1000G reference Panel 1 (v3)  63  combined with the UK10K panel  64  enhanced the ndings and accuracy of imputed rare variants  65. The larger and multi-ethnic imputation panel from NHLBI (Trans-Omics for precision medicine -TOPMed) was recently deeply sequenced (WGS 38x) in approximately 97,256 individuals (TOPMed freeze 8 reference panel)  66, 67. The 2,504 1kGP was further sequenced to a higher depth coverage of 30x with an additional 698 subjects  68. Other genome aggregation databases from numerous studies (gnomaD v2 inc. ExAC and v3) also provide low-frequency variants  [69] [70] [71].
72  reported that the average density of variants imputed across chromosomes was higher in the 1000GP3 panel compared to the HRC panel. Currently, the 1000GP3 combined with ~2,100 African samples also constitutes the African Genome Resource (AGR) reference panel at the Sanger centre (Sanger imputation server : https://imputation. sanger. ac. uk/?about=1#referencepanels). In addition to the existing African genome variation project (AGVP)  73  , Mathias et al. 2016 mentioned the necessity for combined African datasets. They suggested the expansion of data for the H3 Africa Consortium  74  with the genomic variations produced by the Consortium on Asthma among African-Ancestry Populations in the Americas (CAAPA), also available on the Michighan server  75. The reference panels of CAAPA (dbGAP with accession ID:phs001123.
1  84  , GENEIMP v1. 3  90  , GLIMPSE 91 , LOIMPUTE v0. 1. 1  92  , QUILT  93  or STITCH  94  ). Currently, in the absence of consensus for the quality control of imputation, the methods applied are mainly based on the standards used for GWAS. There is no standard protocol to obtain a balance between retaining enough variants and maintaining su cient good quality before (pre-ltration) and after imputation (post-ltration) without stringent conditions. Here, we have established a procedure to keep markers that are valuable for improving imputation metrics  95. Our work ow consists of pre-and post-imputation ltrations approaches that are different from conservative methods. After genotyping a) at pre-ltration, we considered the allele frequencies of the sample set (MAF-DS, MAF Dataset) instead of a stringent or arbitrary MAF b) and a 2-step post-ltration strategy after imputation with (1) a speci c MAF to compute (MAF-DH, MAF Dosage representing 1.
5 Heterozygous) and (2) a determined minimum imputation quality score (INFO). A database of control samples was used to compare the MAFs and alleles for a given SNP of similar number and population origin. In our experience, the post-ltration approach with the speci c MAF (MAF-DH) in the rst step maintained SNPs with con dence above the INFO quality score (information) of 0. 3. Only 1% of SNPs remained below this score and were further eliminated in the second step. Previous publications reported the possible role of MAFs and quality scores in improving imputation accuracy and quality  76. The number of heterozygous individuals who would be represented in at least in 1,000 subjects was in accordance with the number evoked by Coleman et al. 2016  96. We also found that the properties of the region studied applied to the genome and that applying our procedure reduced information loss  95.
4| Variant pre-ltration Autosomal SNPs are usually removed with a low call rate <99% and HWE <1E-06. In this dataset, SNPs with a low HWE correspond to a small number of common and high allelic frequencies. Call rates below 99% include only 5 SNPs with rare frequencies. The main cause for removal of the SNPs during pre-ltration would thus be the MAF stringency selection criteria, as in most QC, e. g. MAF <0. 01. Therefore, in our protocol, the variants are ltered with the MAF of the sample set, MAF-DS (i. e. , not performed below the MAF of the sample set 5E-04), limiting the exclusion of markers while pre-ltering the monomorphic variants. This can be done for any study. The correlation between the MAFs of the SNPs and the control database after pre-ltration was veri ed in step 6.
5| Individual ltration Following pre-ltration of the variants, QC is performed on the individuals. The lowest success rate remains between 97%-99% for 4 individuals both before and after elimination of the variants. In this sample set, the individuals are thus all retained following genotyping, as their pass quality scores are in the range used by most GWASs. 6| Variants 6a| SNP control database (Figure  2 ) (i) The control database needs to be of similar origin and sample size to the samples studied to compare the MAF for each SNP, so we suggest either a public reference or a designed combination (combo) database. In this example, the number of individual genotypes (1,031 IDs) is close to the NCBI MAFs dbSNPB137 based on 1,089 subjects from the 1000GP1. Thus, the minimum MAF in dbSNPB137 was 5E-04, identical to that of the sample set used referring to at least one heterozygous genotyped individual.
The MAF from the 1000GP in NCBI was determined from the global minor allele frequencies (gmaf)  20. (ii) Upload the NCBI database dbSNP of the human genome build B137 based on GRCh37 assembly 37. 4 (dbSNP B137), e. g. , in Mysql Enterprise 5. 6. 13, or query a public online database. (iii) Extract the list of SNPs with their MAFs from the database with their alleles. (iv) Compare the MAFs of each SNP genotyped computed in step 2 against the MAFs of the same SNPs in the NCBI control database (Figure  2. 1); similarly, this can be done for the alleles as per Figure  2. 2. The same control database is used to compare the MAFs of the genotyped variants and to compare the MAFs of the imputed variants as a control of accuracy (step 9).
7b | Imputation Perform the imputation of the phased haplotypes with IMPUTE2 against the reference genome 1000GP1. Optional step: Comparison between the two different ltration conditions of the study genotypes. In this case, phasing and imputation should be run in both conditions with the same seed as previously described  95. In sequencing phasing and imputation can be carried out with the same program such as GLIMPSE  91. Problem Some variants on the array might be absent from the reference panel used for imputation.