dataesr/scientific-paragraphs-categorization huggingface dataset:
2005). Given the large number of galaxies that will be observed by Euclid, it is essential to obtain a fast and reliable way of measuring morphological parameters of galaxies from images. In order to understand the capabilities of measuring morphologies and structures from Euclid-detected galaxies, we have created the Euclid Morphology Challenge to test, quantify, and evaluate the performance of galaxy morphology measurements by existing parametric fitting codes on simulated Euclid data. The structural measurements evaluated in this work are not tailored to a specific science case. Rather, we provide a comparison of the measurements of parameters (Euclid data products) that will enable astronomers to investigate a range of research questions related to galaxy evolution and morphologies or structures with Euclid.
For example, as Sérsic indices are an approximation to statistically distinguish early-from late-type galaxies, probing those indices in a large range of redshift can help us understand morphology evolution. They will also be combined with other parameters of interest such as colour or stellar mass to scrutinise current models. The depth and volume of Euclid will constrain these relations and open a variety of investigations needed to make progress in galaxy evolution science. The challenge comprises a simulated dataset of five fields, each realised with single-Sérsic, double-Sérsic, and neuralnetwork-generated galaxies in the I E band. In addition, one of the fields has been simulated in the NIR (Y E , J E , and H E ) bands, and in the five u, g, r, i, and z Rubin Observatory bands to test the accuracy of multi-band-based model fitting with ancillary data.
While Rubin will only cover the southern hemisphere, other facilities such as CFHT (MegaCam) or DES will also cover the northern hemisphere in similar bands. The companion paper (Euclid Collaboration 2023; hereafter EMC2023) provides a visualisation of the bandwidth and wavelengths (see their Fig. 1 ). In this work, we focus on quantifying galaxy structures through analytic functions that describe the shape of the surface brightness profile of each galaxy. The outcome is a set of parameters that allow the reconstruction of the 2D photometric shape of a galaxy, and thus provides important information for the statistical study of galaxy evolution. To carry out this challenge, we have invited a number of developers of widely used software packages to retrieve morphologies and structures from our large dataset of simulated galaxies.
DeepLeGATo bases its photometric galaxy profile modelling on convolutional neural networks. All of them fitted at least a single profile to each galaxy in the I E band, and some teams and codes have extended the challenge to include the simultaneous fitting of multiple images at different wavelengths. We present the comparison analysis based on the Euclid Morphology Challenge in this paper. We investigate the outcomes from the five participating codes on simulated Euclid galaxies. Each software package incorporates its own preferred scheme for dealing with the data and was run by the developers or developing teams themselves. Each participant was free to choose setup parameters and criteria according to their best practice and experience, with the hope that this would ensure the best possible outcomes.
Realistic simulation Simulated galaxy images are inherently difficult to produce realistically, which is why most tests for morphology measurements focus on simulating and fitting smooth analytic profiles. The Euclid Morphology Challenge also provides a dataset with more realistic galaxies learned following a data-driven approach using deep neural networks. This is described in detail in Euclid Collaboration (2022c, referred to as B22 from here onwards). Very briefly, we use a deep generative model called the variational auto-encoder  (Kingma & Welling 2019) , that compresses and decompresses images to learn a probabilistic latent representation of the training set distribution. Using HST images the model learns how to simulate real 2D noiseless galaxy profiles at a VIS-like resolution. A second generative model, called Normalising Flow  (Papamakarios et al.
2021 ) is then used to condition the latent distribution with the structural parameters. The resulting architecture, called the Flow-Variational AutoEncoder (FVAE), can therefore simulate galaxies directly from a catalogue of parameters, provided that the training set properly covers the range of values. The advantage of the FVAE compared to a classical VAE or other generative network is the ability to constrain the physical parameters of the emulated galaxies. Given the lack of very large and bright galaxies in the HST data used for training, this dataset does not include galaxies larger than 0. 2 arcminutes or brighter than 20. 5 mag. This only represents around 1% of the 314 000 simulated galaxies per field. Although this dataset should allow us to quantify the performance of the different codes in more realistic conditions, it is important to emphasise that these simulations are not perfect.
The results are presented as follows. For each type of simulation -single-Sérsic, double-Sérsic and realistic -we measure our three metrics B, D, and O for each structural parameter and every code on a common dataset containing only galaxies for which all codes provide a valid fit (see also the companion paper EMC2023). In this way, we ensure a fair comparison between the different codes. These values are summarised in Tables  1  (single-Sérsic  and realistic)  and 2 (double-Sérsic).
Half-light radius Figure  3  shows that the global behaviour of all five software packages is similar, with the expected trumpet shape visible in all plots: the scatter increases for faint objects. Moreover, the scatter plots generally do not show a significant bias (with the exception of DeepLeGATo for bright objects). Another commonality of all codes is that the trumpet plot is skewed towards positive values, that is the majority of outliers (points outside the two red dashed lines) are due to an overestimation of the size. Beyond this common general behaviour, some peculiarities are notable. This includes the bias in Morfometryka's plot (in red), indicating a bi-modality at the faint end, with around 13% of objects consistently fitted with a lower radius than expected (the relative bias is around -0.
The reason for this is that removing them would bias codes that were generally able to fit these objects, and because of the non-negligible fraction of the catalogue they represent. Nevertheless, even if Morfometryka is not able to fit these objects, they are able to recognise the problem and flag them. We show in Fig. D. 6 a version of the trumpet plot without those particular objects. DeepLeGATo (in purple) also shows a characteristic behaviour, with a strong negative bias and dispersion for very bright objects (I E < 18), and an apparent discontinuity around 24. 5 mag. The first can be explained by the fact that the dataset used to train the model lacks bright objects which are rare in the observations. This is a well known effect of machine learning models, which are sensitive to the distribution of properties apparent in the training dataset.
Double-Sérsic results We now analyse the measurements from the double-Sérsic simulations. Figure  13  summarises the results, along with As expected, separating the galaxy light into two components is a more degenerate problem than the single-Sérsic model fitting. This is enhanced by the fact that bulges and b/t in our sample are generally small, that is the bulge component has a low S/N compared to the disc (see Fig. 1 ). We also note that Morfometryka did not provide results for the bulge-disc decomposition. It is therefore excluded from the comparison in the following sections. Another difference compared to the single-Sérsic dataset is that one of the fields contained multiple bands including Euclid NIR and Rubin filters. In the following we only show results for 3/5 fields with VIS-only data.
The multi-band dataset is analysed separately in Sect. 4. 2. 6. Finally, we note that while the simulations were made with a bulge Sérsic index fixed to n = 4, and a disc with a fixed n = 1, we asked the participants to also model the galaxies with a free bulge Sérsic index. We compare the results for free and fixed bulge fittings in Sect. 4. 2. 7. Here, we concentrate on the model using a fixed value of n. Notice that because DeepLeGATo does not fit a model, it does not have those two different versions.
A possible explanation for the SourceXtractor++ and DeepLeGATo ability to avoid this effect could be the use of favourable priors. Surprisingly, the figure shows that the metrics are better for faint objects, where the constraining power of the data is theoretically the lowest, and therefore the estimation is mostly driven by the prior. SourceXtractor++ uses an explicit prior of 0. 022 for b/t, which matches the average b/t in the simulation. It was calibrated by the participants on a sub-sample of the dataset with known ground truth. DeepLeGATo also implicitly learns the prior from the data during training, by maximising the likelihood. Galapagos-2 uses arbitrary priors and initially places half the light in the bulge and half in the disc. ProFit starts with reasonable initial guesses for the profile solution based on runs of the ProFound software on the cutouts  (Robotham et al.
(  8 )). In this figure, the colour of the square shows the bias | Bp | (lighter colours indicate smaller bias), and the coloured discs indicate the dispersion (the redder the point the smaller the dispersion). The first column plots results for the bulge radius, the second for the disc radius, and each line is a different software code. We note that we limit the magnitudes to faint galaxies (I E > 18. 5) and that for ProFit and Galapagos-2, the colourbars are on a logarithmic scale to accommodate the large values. The expected behaviour is particularly clear for ProFit (third row), which we use here to for demonstration. The bias of the bulge radius B becomes smaller for brighter and more bulgedominated galaxies (lower right corner of the plot) and the dispersion is low. On the contrary, a faint galaxy with small b/t has A102, page 12 of 29 Fig.
Disc axis ratio In general, software packages were able to measure the axis ratio q of the disc components (Fig. 12 ) more accurately than for the bulges. They are comparable to results from the single-Sérsic case, albeit with a higher dispersion and a larger negative bias for faint objects for Galapagos-2 which tends to under-estimate q. We can make a more in-depth comparison of the metrics by looking at the last row of Fig. 13. Their general behaviour is comparable to the bulge axis ratio, but with smaller values. The absolute bias remains smaller than 0. 3 for all codes for I E < 23 (apart from DeepLeGATo which is again unreliable for bright objects). Galapagos-2's and ProFit's biases are well calibrated for I E < 22. 5, but then decline to a value of -0. 2. For the faintest bins, SourceXtractor++ has a slight tendency to under-estimate the axis ratio, while ProFit and DeepLeGATo over-estimate it.
In addition to the usual double-Sérsic parameters, we show in the last row the fitting of the bulge Sérsic index for the "bulge free" model. to 89. 37. For the disc components, we see that the average score is much lower than for bulges, with values smaller than 1. 2 and hence comparable to the single-Sérsic simulation. DeepLeGATo again scores lowest for the two components, since it maintains accuracy for faint objects, which are the majority of the dataset. This outweighs its poor performance at the bright end. On the contrary, software packages that achieve better results for the bright objects are penalised by their lower performance at the faint end. The table also summarises differences between free and fixed Sérsic index, as discussed in the previous section.
Summary and conclusions The Euclid surveys will become a reference dataset for studies involving galaxy structure and morphology due to the unique combination of a wide area and high spatial resolution. In this paper, we have described the results of the Euclid Morphology Challenge to quantify the performance of five state-of-the-art surface-brightness-fitting software packages on simulated Euclid imaging data. This paper is the second part of the two papers discussing the EMC. While the companion paper (Euclid Collaboration 2023) focusses on the results related to photometry, we have focussed on the results concerning the morphological parameters only. We compare the results after measuring structural properties of simulated galaxies in fields that mimic Euclid observations with the VIS instrument from DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++.
) and that we had to make choices that meant not every aspect and every strength or weakness was included in our analysis. For example, we do not include the computation time, required resources, compatibility with the current pipeline, accuracy of uncertainty budget estimates, etc. Naturally, other science cases with other priorities may lead to different results and decisions. We invite interested readers to use the online tool to test and manipulate the dataset according to their science cases. We hope this will be useful for others to make decisions and explore the software packages.
Throughout the next sections, we step through our metrics analysis for each of the datasets by discussing two main types of figure. The first figure type is a scatter plot of magnitude versus b or b for individual objects. Because the dispersion increases towards fainter fluxes (high magnitudes), the scatter plots produce a trumpetlike shape, and are therefore referred to as 'trumpet plots'. The two metrics, B and D are represented with a running orange line (D represented as error bars centred on B). In this first type of figure, we also show the distribution of the bias b on the right inset plot, with the reference 0 bias in thick blue lines, and the overall bias in dashed white lines. The outlier threshold t b is represented by dashed red lines. The second type of plot, which we call the 'summary figure', shows our three metrics B, D, and O values in 11 bins of magnitude, from magnitude 14 to 26.
Background Rare diseases affect more than 400 million people worldwide, corresponding to an estimated cumulative population prevalence of ≈ 3. 5 to 5. 9%, according to the Orphanet database  [1]. Genetic disorders represent 72% of rare diseases, and 70% have an exclusive pediatric onset  [1]. The advent of high-throughput sequencing methods for DNA analysis has revolutionized the diagnostic approach for patients with suspected genetic disorders  [2]. In recent years, several studies have reported the usefulness of clinical exome sequencing (CES)  [2] [3] [4] [5] [6] [7] [8] [9] , whole-exome sequencing (WES)  [10] [11] [12] [13] [14] [15] , and wholegenome sequencing (WGS)  [14] [15] [16] [17] [18] [19] [20]  in patients with highly suggestive Mendelian phenotypes, reporting diagnostic yields ranging from 9 to 61%, 32 to 74%, and 16 to 42%, respectively.
The 100,000 Genomes Project confirmed the highest diagnostic yields of WGS among family trios and families with large pedigrees  [20]. In line with these results, including at least one family member in addition to the proband was independently associated with confirming the suspected clinical diagnosis by CES in our study. Using a systematic approach and predefined outcomes, we explored the clinical utility of CES as a first-tier genetic test using a large dataset of consecutive adult and pediatric patients with various Mendelian phenotypes. Our results can be translated into several perspectives from a clinician's point of view and a well-structured dialog framework between prescribing clinicians and molecular medicine physicians. First, the clinical utility of CES should be assessed in well-powered prospective studies using predefined inclusion criteria to allow for better inference of the results in a large population.
We assessed model discrimination using ROC analysis and the percentage of cases correctly classified by the model. We assessed the goodness of fit of the model using Nagelkerke R 2 and Cox & Snell R 2 statistics  [41]. All statistical analyses were conducted using MedCalc, version 19. 5. 3 (MedCalc Software, Ostend, Belgium) and SVS (v8. 8. 1; Golden Helix, Inc. , Bozeman, MT, USA). This study was funded by INSERM UMR_S 1256, Nutrition, Genetics, and Environmental Risk Exposure. University Hospital of Nancy.
Data availability Anonymized patient data are available for use in collaborative studies to researchers upon reasonable request (abderrahim. oussalah@univ-lorraine. fr). Data will be provided following the review and approval of a research proposal (including a statistical analysis plan) and the completion of a datasharing agreement. Responses to the request for the raw data will be judged by the IRB of the INSERM UMR_S 1256 and the University Hospital of Nancy.
Animal studies CanSel screening. To select the cancer cell type for screening, B16-F10 melanoma and LLC lung adenocarcinoma cells, were in C57BL/6 mice, whereas 4T1 breast cancer cells in Balb/c mice. For the screening, each AAV9 pool was injected bilaterally in the tibialis anterior muscle, followed by LLC injection. Total muscle DNA was extracted after 10 days for barcode ampli cation and NGS to identify relative barcode (transgene) abundance. To compare different pools, we rst calculated the mean log value of the frequency of each transgene in muscles injected with each AAV pool (mean log control AAV). Then, we assessed the log of the frequencies of the same transgene in every muscle injected with the same AAV pool and cancer cells (log AAV LLC) and normalized the relative abundance of each transgene, as log AAV LLC: mean log control AAV ratio.
We evaluated barcode frequency, representing the frequency of each transgene in muscles injected with the AAV pool, either in the presence or in the absence of cancer cells, and expressed it as a ratio (Supplementary Fig. S1D ). At day ten, when cancer cells have invaded a massive portion of the injected muscles, ve factors resulted enriched, including our positive control, Sema3A (Supplementary Table  S1 ). Thus, this time point was chosen for the eventual CanSel experiment, using the whole AAV library.
We report here a new application, CustomProteinSearch (CusProSe), whose purpose is to help users to search for proteins of interest based on their domain composition. The application is customizable. It consists of two independent tools, IterHMMBuild and ProSeCDA. IterHMMBuild allows the iterative construction of Hidden Markov Model (HMM) profiles for conserved domains of selected protein sequences, while ProSeCDA scans a proteome of interest against an HMM profile database, and annotates identified proteins using user-defined rules. CusProSe was successfully used to identify, in fungal genomes, genes encoding key enzyme families involved in secondary metabolism, such as polyketide synthases (PKS), non-ribosomal peptide synthetases (NRPS), hybrid PKS-NRPS and dimethylallyl tryptophan synthases (DMATS), as well as to characterize distinct terpene synthases (TS) sub-families.
Results Development of the CusProSe software. CustomProteinSearch (CusProSe) is a generic genome mining software, consisting of two distinct but complementary customizable programs: IterHMMBuild and ProS-eCDA. IterHMMBuild is an HMM profile building tool based on an iterative learning process. ProSeCDA is a protein search and annotation tool based on user-defined domain architectures. The two programs can be run independently. An overview of the CusProSe workflow and package functionalities is presented in Fig. 1. Detailed information about its implementation and functioning is provided in the "Methods" section as well as in the CusProSe documentation page (https:// i2bc. github. io/ CusPr oSe/). The CusProSe IterHMMBuild tool allows the users to construct HMM profiles representatives of their protein sequences of interest, by an iterative learning process starting from seed sequences and a fasta protein dataset (Fig.
1a, b ). The IterHMMBuild procedure starts building an HMM profile from a set of related protein or protein domain seed sequences or from a single query sequence. This initial HMM profile is then used to identify sequences with similar domains in any user-specified protein sequence dataset, in order to enrich the profile model. If matching sequences are found, they are added to the initial query sequences and a new HMM profile model is built. This new HMM profile is then searched against the same target dataset in order to find more distant similar sequences to the original query sequence(s)  19. This process is repeated (iterations) until convergence is reached, i. e. , no new sequences are recovered from the dataset (Fig. 1b ) (see also the supplementary Fig. S1  and "Methods" section for technical details).
When convergence is reached a final HMM enriched profile is build. A database of HMM profiles is then created by concatenation of the individual final profiles, either automatically or manually depending on input parameters and the user's choice. Additional information about the HMM database creation procedure can be found in the "Methods" section and in the Documentation page in the IterHMMBuild usage guideline chapter. As regards ProSeCDA, the tool allows to search in a given protein dataset, for multiple proteins of interest defined by a user-specified set of rules (Fig. 1a, c ). The first step of the ProSeCDA pipeline is to annotate the protein dataset of interest, with functional protein domains from a user-specified HMM profile database (Fig. 1c ). This database can be the one generated using the IterHMMBuild package of CusProSe, as exemplified in the scheme of Fig.
1a , or, alternatively, any other compatible HMM profile database (. hmm file format). In the second step, annotated proteins are filtered following a set of rules which are also user-determined (Fig. 1c ). The rules describe any protein families of interest based on the user-defined specific domain architectures. Features defining each rule include the protein family name, the "mandatory" domains (list of domains the protein must contain) and the "forbidden" domains (optional, list of domain the protein must not contain). All proteins matching those rules are then finally accessible in the ProSeCDA output files directory. The output files include, for each identified protein, a summary in xml format, containing information such as the protein sequence and the boundaries of the conserved domain architectures (.
First, HMM profiles were constructed using M. oryzae protein sequences of conserved functional domains (Table  1 ) characteristic of PKS, NRPS, PKS-NRPS and DMATS enzymes  25. At this stage three PKS, three NRPS, three hybrid PKS-NRPS and three DMATS were used to seed IterHMMBuild (Supplementary Data S1). HMM profiles were also constructed for type III PKS (t3PKS), using conserved domain sequences of two M. oryzae t3PKS enzymes (Supplementary Data S1). These initial domain profile models were then used to screen the M. oryzae proteome to identify potential new domains by homology search, to improve the HMM profiles. The database of domain profiles generated by IterHMMBuild was then given as input to ProSeCDA to annotate the M. oryzae proteome, together with the rules file. The rules used to define each type of SMKE above cited can be found in the supplementary File S1.