dataesr/scientific-paragraphs-categorization huggingface dataset:
Ancillary detectors and structures As for other γ -ray arrays, the performance of AGATA can be enhanced by the detection in coincidence of the gammaemitter nuclei as well as other products characteristic of the reaction of interest. In order to perform as realistic and complete simulation as possible many ancillary detectors have been added to the code over the years by different developers. To avoid clashes with previously defined detectors, each new detector must be given a new identification number. The list of these detectors together with their identification number can be displayed when running the simulation code with the help option (. /Agata -h). Amongst these ancillary detectors already coupled to AGATA in real experiment are the FATIMA, PARIS, and SPIDER arrays. The PRISMA spectrometer is the very latest addition.
Outlook The AGATA collaboration has continued to maintain and develop the AGATA simulation code, whether to take advantage of new features distributed in the latest GEANT4 releases or to expand the range of event generators or the list of ancillary detectors. One future improvement that would benefit the predictive power of the code would be to model the electrical passive area of each crystal, individually, estimating it either indirectly from the manufacturer measured intrinsic efficiency or measuring it directly during the crystal scanning process. There are also more recent simulation tools which have been developed in recent years within the nuclear physics community and which often aim at providing a complete data analysis and simulation framework. The migration of the AGATA code to some of these frameworks has already started and a version of the code is available in the Nuclear Physics Tool (NPTool)  [25]  and in the Simultation Toolkit fOr Gamma-ray Spectroscopy (STOGS)  [26].
There are plans to do the same with FairRoot  [27]. All these frameworks are built directly or virtually on top of the GEANT4 radiation transport code and, since GEANT4 is compatible with GDML (Geometry Description Markup Language) format files, developers have converted the AGATA geometry into GDML to facilitate this migration. For the AGATA collaboration, this is seen as an opportunity to rejuvenate the code and make it accessible to a larger community of researchers.
Acknowledgements The authors would like to thank the AGATA collaboration. The development of the simulation code is the result of a tremendous collaborative venture involving a huge number of people from many institutes across the last two decades. Particular thanks goes to  E. Farnea  for initiating the simulation work and all postdoctoral researchers and users who have allowed for the code to advance. The authors are supported by grants from the  UK Science and Technology Facilities Council , including  ST/F004 052/1 ,  ST/T003480/1  and  ST/T000546/1. This work is also supported in France by the  CNRS  and partially supported by the  Istituto Nazionale di Fisica Nucleare, Italy , by  MCIN/AEI/1 0. 13039/501100011033 , Spain with grant  PID2020-118265GB-C42 , by  Generalitat Valenciana, Spain  with grants  PROME-TEO/2019/005  and  CIAPOS/2021/114 , and by the  EU FE-DER  funds.
Moreover, the number of force fields capable of handling simultaneously different scales is restricted to a handful. Among those, the SIRAH force field, developed in our group, furnishes a set of topologies and tools that facilitate the setting up and running of molecular dynamics simulations at the coarse-grained and multiscale levels. SIRAH uses the same classical pairwise Hamiltonian function implemented in the most popular molecular dynamics software. In particular, it runs natively in AMBER and Gromacs engines, and porting it to other simulation packages is straightforward. This review describes the underlying philosophy behind the development of SIRAH over the years and across families of biological molecules, discussing current limitations and future implementations.
Moreover, a substantial effort has been dedicated to making the package easy to use. We created a collection of scripts, referred to as SIRAH Tools  (Machado and Pantano, 2016) , that facilitate the process of mapping all-atom files to CG representations, backmapping, visualizing, and analyzing SIRAH trajectories directly on the popular VMD program for molecular visualization. Step-by-step tutorials for setting up and simulating different systems are also available on our website. Furthermore, a web server for setting up and running MD-CG simulations in GROMACS is available at https://molsim. sci. univr. it/mermai d/sirah/sirah. php (Marchetto et al. , 2020). The following paragraphs provide an overview of SIRAH's underlying philosophy and its most salient features. Readers interested in specific and more quantitative details are referred to the original publications.
The positions of these beads are designed to correspond with the intended interactions of the selected functional groups in terms of size and charge. Our mapping scheme results from a pragmatic combination of empirical databases, knowledge of organic chemistry (including canonical structures), and physicochemical insights. As illustrated in the following sections, this leads to a heterogeneous distribution with higher bead density in moieties that establish more diverse intermolecular interactions. The all-atoms to CG mapping of different biomolecular families are described in the next sections. Once the CG mapping is defined, the next decision to make regards the nature of the interaction potential among the different beads. We made a couple of strategic choices. First, we adopted the classical twobody Hamiltonian used by most MD simulation packages (Equation  1 ).
However, using this classical two-body Hamiltonian (eq. 1) requires the determination of a relatively high number of parameters. This led us to the second strategic choice. As per the predetermined mapping guidelines, CG moieties in SIRAH contain beads placed in the position of selected atoms in their fully atomistic representation. Therefore, all equilibrium distances can be directly derived from statistical data from the Protein Data Bank (PDB, https://www. rcsb. org), quantum-level calculations, or canonical conformations defined from organic chemistry rules. This mapping strategy considerably reduces the number of parameters to be determined and facilitates the implementation of a simple backmapping scheme that recovers pseudo-atomistic information at any frame of the MD simulation  (Machado and Pantano, 2016).
Indeed, the CG model represents a compact double helix where minor and major grooves are clearly recognized (Fig. 1 ). Therefore, effective beads representing the bases adopted the LJ size of the Barcelona force field (bsc0) implemented in the AMBER package for MD simulations  (Pérez et al. , 2007). Beads representing the backbone were assigned a bigger size, which was determined by a spherical approximation to the excluded volume of the corresponding functional group. Initial guesses for the depth of the LJ potential were also taken from the bsc0 force field and progressively adjusted. Partial charges were assigned with the primary criterion of adding a negative integer to every base. To this end, a -1 charge was assigned to the phosphate beads, and partial charges adding to zero were assigned to the Watson-Crick edges to ensure electrostatic recognition in A-T and G-C base pairs.
Finally, the mass of the beads (50 a. u. ) was adopted to match a density of 1 kg/dl. Considering that a water molecule is a tetrahedron with vertices at the Hydrogen atoms and Oxygen's lone pair electrons, the WT4 model can be conceived as a bulkier "water molecule". Indeed, the radial distribution function matches the second peak of the atomistic water. This feature prompted the development of monovalent ions that could modulate the ionic strength of the solution. The first version of electrolytic ions in SIRAH was represented by single beads with a net charge set to +/-1 e. The size of ions was adjusted based on neutron scattering data to reflect the chemical identity of sodium, potassium, and chloride ions according to their second solvation shell  (Darré et al. , 2010). As ions are supposed to carry the first solvation shell, the depth of the LJ well was identical to that of the WT4 beads.
Performance As stated previously, the implementation of the latest version of the SIRAH force field profits from all available GROMACS and AMBER package options. The GPU implementations found in these packages facilitate CG simulations of medium-size systems at a rate of a few microseconds per day in desktop computers, while for larger-sized systems of about a million particles, the rate can reach hundreds of nanoseconds per day. In order to provide a quantitative illustration of SIRAH performance, a comparison was made between a simulation of a system in SIRAH CG representation and the corresponding system in its atomistic model (Amber's FF14SB) with AMBER 20 using a node computer equipped with two Intel® Xeon® Gold 5317 processors (3. 0 GHz × 24 cores), 252 GB RAM DDR4 memory (3,200 MHz) and accelerated with an NVIDIA GeForce RTX 3090 (24 Gb) GPU.
The selected system for the study comprised the receptor binding domain (RBD) of the SARS-CoV-2 Spike protein, along with the complete human ACE2 and in the presence of the neutral amino acid transporter B0AT1 (PDB code 6M17). This system was previously simulated using SIRAH and subsequently analyzed in the work of  Garay et al. (2021). Keeping similar dimensions, the use of an atomistic representation instead of a CG one resulted in a ten-fold increase in the number of elements, with the former consisting of 110,910 beads and the latter consisting of 1,162,885 atoms, including water, electrolytes, and metal ions bound to the catalytic site. The SIRAH CG model exhibited a simulation speed of approximately 660 ns per day using a 20 fs time step. In comparison, the atomistic model showed a simulation speed of approximately 11 ns per day using a 2 fs time step.
In the case of SIRAH, this process necessitates data gleaned from experimental databases, organic chemistry (canonical structures, for instance), and physicochemical intuition. Unlike in the fully atomistic realm, where chemical principles are well established, the CG universe can be somewhat fuzzy. Indeed, diverse topologies for the same molecule may emerge from different academic backgrounds. Hence, there is no definitive "correct" or "incorrect" CG parameterization. A "good" parameterization is one that yields an accurate portrayal of the intermolecular interactions being studied, keeps compatibility with the rest of the force field, and grants a significant acceleration in calculations (at least one or two orders of magnitude).
Thus, our analysis led to three independent censuses of the aggregation-prone regions. If all three censuses yielded similar regularities, then these findings were considered as more reliable and treated with special attention. Selection of proteomes for large-scale analysis 76 reference proteomes with 1 123 749 proteins in total were selected from the UniProt databank (see Supplementary data)  (35). The proteomes belong to the three kingdoms of life: eukaryote, bacteria and archaea. The selection of species was made to have well-annotated and complete reference proteomes covering the diversity of living organisms. Viral proteomes were not considered in this analysis due to small size of their proteomes yielding very different results depending on the strains. Their analysis will be a subject of our future study.
Relationship between cellular abundance of proteins and ARs/EARs frequencies The amount of genome-wide data on gene expression has drastically increased in the past few years  (54). The data comes from various technologies, organisms, and tissues (normal or disease related), making it difficult to compare them in a large-scale analysis. In this case, we find that the data from the Protein Abundance Database (PaxDb)  (55)  are the most suitable for our purposes. PaxDb represents protein abundance by "protein per million" (ppm) and by doing so, overcomes the problem of variability in cell size or dilutions in the samples used, making comparisons between them possible. The PaxDb has the protein expression level in different tissues and organs of organisms. Additionally, it provides the average abundance of a protein in the whole organism.
We used this average abundance value to analyze the expression level of AR-/EAR-containing proteins, which are available both in PaxDb and in our dataset. Expression levels range from almost zero up to more than 100 000 ppm. The majority of proteins have values of less than 2 ppm. The number of proteins with the abundance more than 50 ppm drops significantly (Figure  7 ). Therefore, we grouped these proteins together in our analysis. The analysis revealed that the frequency of occurrence of EAR-containing proteins decreases with the ppm growth and is becoming lower than non-EAR-containing proteins. From the observed dependence of the difference between EAR-and non-EAR-containing proteins, we can conclude that highly expressed proteins are less prone to aggregate, with this finding being consistent in the three predictors used.
EAR levels in essential proteins As demonstrated previously, essential genes are subject to a greater selection pressure than non-essential genes  (55, 57). It has also been shown that essential proteins are less prone to aggregation  (20, 23). In order to find essential proteins in our database, we used the DEG database of essential proteins  (58)  and run BLAST program with E-value < 0,001  (59). By this approach we identified 705692 essential proteins (~62,6 %) in our database. Analysis of these proteins by the three predictors showed a lower EAR coverage, and in a lesser extent, proportion of essential and non-essential EAR-containing proteins in eukaryotes (Figure  8 A, C ). Our results are in agreement with previous conclusions that essential proteins have a lower aggregation score than non-essential proteins  (23).
Short Linear Motifs (SLiMs) in EARs A significant portion of protein interactions are mediated by short linear motifs (SLiMs) preferentially found in IDRs  (41). As EARs are also located within the IDRs, it was interesting to analyze the co-occurrence of SLiMs and EARs in proteins. Although both prokaryotes and eukaryotes have functional SLiMs, the eukaryotic linear motifs are more common, as well as better classified and documented. Most of the eukaryotic SLiMs can be found in the ELM resource (41) alongside with their descriptions, experimental evidence from the literature, and Regular Expressions (RegEx) of the recurrent patterns. Therefore, we focused our analysis on the SLiMs from eukaryotes. For this purpose, we applied the RegEx from the ELM database  (41)  to the IDRs and EARs determined by our pipeline  (33).
Conservation of EARs sequences Another approach to find new functional amyloids is to search for EARs that are conserved among different species. For this purpose, we reduced EARs predicted by either ArchCandy 2. 0, TANGO or Pasta 2. 0 with CD-HIT (63) at 70 % sequence identity and 90 % of coverage, to obtain a non-redundant set of the EARs (Table  1 ). Then, we ran BLAST  (59)  for each EAR sequence against all proteins from our redundant database to select only conserved EAR sequences (Figure  10 ). This gave us for each EAR a Multiple Sequence Alignment (MSA) of similar sequences found in other proteins. Some sequences of the MSA were EARs and the others were not according to the predictors. We selected the MSAs with EARs in more than five other proteins and further reduced the MSA number by merging those that shared at least 80 % of the same conserved EAR.
DISCUSSION The recent progress with computational approaches predicting aggregation  (10, 12, 13, 15, 16, 18, 31, 33) , and an increasing number of whole-proteome sequencing data, opened an avenue for the comprehensive census of aggregation-prone regions in proteins. In this work, we performed the detailed analysis of 76 full reference proteomes from the UniProt databank. As a result, a number of interesting correlations, confirmed by all the predictors used in this work (ArchCandy 2. 0, Pasta 2. 0 and TANGO), were discovered. First, we detected a significantly lower percentage of EAR-containing proteins (about 10%) in comparison with a high percentage of AR-containing proteins in proteomes (about 80%). The number of EARs correlates better with a small number of the known proteins forming aggregates in vivo, and, therefore, EARs can be suggested as a more precise measure of the aggregation potential of proteins.
FUNDING This work was supported by  REFRACT  project with  Latin America in RISE program  ( 2018-2023 )  H2020-MSCA-RISE-2018  to A. V. K. ;  Azerbaijan National Academy of Sciences and The Ministry of Science and Education of Azerbaijan  to Z. O. ; the  Ministère de l'Education Nationale de la Recherche et de Technologie (MENRT)  to E. V. and F. R. ; by a  CNRS   PhD fellowship  to T. F.
Background: The progression of targeted radionuclide therapy requires the development of dosimetry software accounting for patient-specific biokinetics. New functionalities were thus developed in the OEDIPE software, to deal with multiple 3D images or multiple planar images and a SPECT image. Materiel & method: Methods were implemented to recover patient biokinetics in volumes of interest. If several 3D SPECT images are available, they are registered to a reference CT scan. When several planar images and a single SPECT are available, the planar images are registered to the SPECT and counts of the planar images converted to activity. To validate these developments, six SPECT/CT and planar images of a Jaszczak phantom containing I-131 were acquired at different dates. Cumulated activity was estimated in each sphere using the SPECT/CT images only or the planar series associated to one SPECT/CT.
Many in-house or commercial software have been developed to enable treatment planning in nuclear medicine  [12, 22, 23]. Some of them, such as STRATOS (Philips Technologie, Aachen, Germany)  [24]  or PLANET® Onco Dose (DOSIsoft, Cachan, France)  [25]  allow combined analysis of 3D and 2D images. Some examples of dosimetry studies taking advantage of the hybrid approach have been reported recently. In  [26] , Lu-177-PSMA-617 timeactivity curves were obtained from 2D and 3D SPECT images using the QDOSE software, but dosimetry was performed with IDAC-Dose2. 1. This software enables calculation of organ absorbed doses from cumulated activities based on organ S-values pre-calculated on reference voxel phantoms  [27]. In  [28] , pre-therapeutic imaging with In-111 was performed for patients treated with Y-90 ibritumomab tiuxetan; 2D and 3D SPECT images were acquired and quantified, and doses were calculated with MIRDose 3.
0. Similarly, in  [29] , hybrid images were quantified and then doses calculated with pre-calculated S-factors in order to assess bone marrow doses following Lu-177-DOTATATE therapy. Finally, Kupitz and colleagues  [30]  have used the dosimetry toolkit from GE Healthcare to acquire, register and quantify hybrid images following Lu-177-DOTATATE therapy, doses were calculated with OLINDA/EXM, which uses reference organ S-values  [14]. The OEDIPE software has been continuously developed at the French Institute for Radiation Protection and Nuclear Safety (IRSN) and applied to carry out personalized 3D dosimetry in nuclear medicine using Monte Carlo simulation  [31, 32]. Developments had been initiated in OEDIPE to derive biokinetics from SPECT/CT and PET/CT images  [33]. However, series of 3D images are not always available in the clinical practice, whereas it is more feasible to acquire planar images at several timepoints.
OEDIPE software Patient's anatomical data can be imported in OEDIPE as CT or MRI images. VOIs are defined by threshold-based segmentation on image values. VOIs can also be directly imported as DICOM RT-structure file, generated from contours delineated using external software such as PLANET® Onco Dose (DOSIsoft, Cachan, France). A voxelized phantom of the patient is subsequently generated and the user defines the composition and densities of VOIs. Functional images are imported as SPECT or PET images associated with a CT exam. The cumulated activity in the voxel phantom can be distributed either homogeneously within selected VOIs or heterogeneously through the phantom according to the quantitative emission data available in input. In this case, each voxel is assigned a fraction of the cumulated activity derived from a single functional image.
The source spatial distribution can be associated to one of the 246 radionuclides available, with emission spectra taken from ICRP publication 38  [36]. Hence, the patient's treatment with a radiopharmaceutical can be fully modelled using OEDIPE (anatomy, material, density, cumulated activity distribution of the radionuclide). OEDIPE subsequently enables automatic transcription of this model into an input file for MCNPX Monte-Carlo code  [37]. MCNPX may directly compute mean absorbed doses or a dose-rate map used in OEDIPE to provide mean absorbed doses, biological effective doses, isodose maps and dosevolumes histograms for the selected VOIs. Fig. 1  shows the flow of input and output data involved in the OEDIPE environment.
Phantom data acquisition Images of a Jaszczak phantom were used to validate the new functionalities (Cylindrical Jaszczak phantom, Data Spectrum, Durham, NC, USA). Each sphere of the Jaszczak phantom (0. 5 mL to 16 mL) was filled with a 9. 36 MBq/mL solution of I-131. The total activity in the phantom was hence 294. 7 MBq. The phantom contained no background so that the validation results could be interpreted with a minimum of artefacts. Six SPECT/CT images were acquired with a GE Discovery NM/CT 670 gamma camera at 4, 7, 11, 14, 18 and 22 days post-injection (see Fig. S. 2 in Supplemental Material). After each SPECT/CT acquisition, a planar image was acquired with the phantom in the same position. The SPECT acquisition was performed using the parameters summarized in Table  1. SPECT reconstructions were carried out using Xeleris 3.
Multi-SPECT approach A voxel phantom of the Jaszczak phantom was created after delineation of the spheres (VOIs) and of the outside contour on the CT on the first SPECT image. Delineation was carried out with PLANET® Onco Dose (PDOSE) software from DOSIsoft (Cachan, France). As described in the presentation of the analysis process for multiple 3D NM images, a CT-CT REFERENCE registration was carried out. The calibration factor was obtained using the same phantom, acquisition and reconstruction parameters as described in the previous paragraph, and was used as an input parameter in OEDIPE (25. 5 counts. s -. MBq -1 ). Recovery factors, as a function of the object size, were obtained from the Jaszczak experiment and entered in the software to correct for the partial volume effects and improve the quantitative accuracy of the dosimetry workflow (see Table  S.
Imaging data Four planar whole-body images and a SPECT/CT were acquired (Fig. 3 ). The planar images (256 × 1024 pixels), were acquired with a GE Discovery NM/CT 670 equipped with a HEGP collimator, at 23. 7, 43. 5, 65. 3 and 88 h after the radioiodine administration. The SPECT/CT scan was acquired a few minutes after the last planar imaging using the same gamma camera. The SPECT acquisition and reconstruction parameters are the same as those used to acquire images of the Jaszczak phantom. VOIs contours were outlined manually using the PDOSE software by an experienced physician on a diagnostic CT image, acquired few days before the radioiodine treatment. The VOIs and the corresponding contour characteristics are listed in Table  2.