dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2002). We strongly encourage the deposition of voucher specimens from field surveys and experimental studies in museum collections, as has been urged by the Entomology Collections Network (ECN) and required in many PhD programs. No matter how quickly new molecular techniques are developed or refined, there is no substitute for a reliable database of voucher specimens when it comes to ensuring the repeatability of biological research  (Funk et al. , 2005; Lendemer et al. , 2020). Our results show that species richness of the parasitic wasps that attack Drosophila is severely underestimated, and only a fraction of them have been described. In DROP, 38% of the OTUs are provisional species, and more than 46% of the named OTUs have synonyms. Remarkably, Leptopilina heterotoma, one of the world's most studied parasitoids, has more than 20 synonyms! As is generally the case, the rate of species description and revision of Drosophila parasitoids lags far behind that with which molecular sequence data are generated.
Conclusion Taxonomic confusion presents many obstacles in experimental and biodiversity studies. One way of addressing this impediment is to provide a reliable DNA library with traceable vouchers  (Astrin et al. , 2013). We developed DROP as a resource and platform for gathering and sharing reliable genomic sequence data for Drosophila parasitoids. We hope it will serve as a model for researchers working with organisms which present similar difficulties. While compiling DROP, we found that the high number of provisional versus named OTUs suggests that the diversity of parasitic wasps attacking Drosophila is greatly underestimated. With this in mind, DROP represents the start of an important knowledge base that will strengthen future studies of natural host-parasitoid interactions, population dynamics, biocontrol, and the impact of climate change on biodiversity and ecosystem services.
Figures: Figure  1 : An example of difficulties of molecular identification demonstrated on Ganaspis xanthopoda and G. brasiliensis. Only two sequences (in bold text) can be reliably used for identification and are included in DROP database. To select the sequences, we searched the BINs associated with the organism's name "Ganaspis xanthopoda" (green) or "Ganaspis brasiliensis" (purple) in BOLD. From each BIN, two sequences from each species were selected to build a neighbor-joining tree (bottom axis indicated % genetic divergence). There was a total of 6 BINs (gray boxes) in this sequence complex. Of these, 4 BINs contained both species names and without examination of vouchers identification would be impossible. In DROP, vouchers from two sequences, MG755073 and MG755072, were deposited in CNR-IPSP (Table  S2 ), examined by taxonomists and identify as G.
Figure2: Concept of a centralized, vetted, integrated database for Drosophila Parasitoids (DROP) we developed. First, we provide a species and provisional species catalog with correct taxonomy. Second, to provide a reliable genetic reference library, genetic data (DNA sequences, genomes, transcriptomes, proteomes) link to a voucher connected to the species catalog. Third, we link the two primary sources of data (field surveys and laboratory experiments) by requiring a permanent deposition of vouchers and sequences in order to be included in DROP. Tables:.
Acknowledgements The  DROP  project was developed during the 2018 Entomology Society of America conference, during the symposium "Drosophila parasitoids: from molecular to ecosystem level". We thank  Dr. Elijah Talamas  for valuable comments on earlier drafts. We also thank  Chris Jeffs  for providing Australian field samples. We are also thankful for funding support from the  Czech Science Foundation  ( 17-27184Y ). Additional fund for sequencing was provided by  MLB , OTL, and PPAS. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  USDA. USDA  is an equal opportunity provider and employer.
Introduction GATE is an open-source, community-based software effort relying on the Geant4 toolkit  (Allison et al 2016)  dedicated to Monte Carlo simulation in medical physics. GATE is about 15 years old and evolves a lot through users' contributions. It was initially focused on nuclear imaging  (Santin et al 2003 , Strul et al 2003 , Jan et al 2004 , Buvat and Lazaro 2006) , then expanded to external and internal radiotherapy  (Jan et al 2011) , dosimetry  (Sarrut et al 2014)  and hadrontherapy  (Grevillot et al 2020). The simulated physics is managed by the Geant4 Monte Carlo kernel in charge of tracking particles in matter and processing physical interactions. On top of Geant4, GATE gathers multiple developments that facilitate medical physics simulations. Indeed, numerous clinical, preclinical, and prototype positron emission tomography (PET) and single photon emission compute tomography scanners were simulated and confronted with experimental data.
This first mode will be presented in section 2. 2. In the second simulation mode in GATE, the response of the photodetection components is simulated by a specific module called a digitizer. In that case, an analytical model is used to generate detection events from the list of interaction events within the crystal, assuming the number of generated digital pulses is proportional to the number of scintillation photons in the crystal. This digitizer converts photon interactions in the crystal into digital counts and assigns time stamps to every event. Numerous parameters are provided to the user who can apply successive signal processing operations to generate a final response adapted to the hardware: pixelated or monolithic scintillator detectors, depth-of-interaction (DoI) modeling, dead time, etc.
In PET imaging, it is common to consider several types of coincidences: the scatters (coincidence events resulting from scattered photons inside the subject), the randoms (accidental coincidences), the trues (real, expected coincidences). The sum of those three types are called the prompts (total detected coincidences). In reality, the number of randoms is not known from experimental data and is estimated by the delay coincidences obtained from delayed time coincidence windows  (Strydhorst and Buvat 2016a). GATE generates all types of coincidences for detailed analysis. The digitizers processes are common to PET, SPECT and CCMods and may be used either 'online', during the Monte Carlo particle tracking, or 'offline', after the end of the simulation. In this latter case, the simulation output requires hits, singles, and/or coincidences data to be saved in root (or Python) files that can be postprocessed.
Compton camera modules The recent GATE CCMod  (Etxebeste et al 2020)  provides a framework where different Compton camera configurations can be simulated and facilitates comparison between the performance of different prototypes in medical experimental settings such as hadron therapy monitoring or nuclear medicine. CCMod is designed to reproduce the response of most common configurations in medical applications composed of a scatterer and an absorber detectors working in time coincidence  (Everett et al 1977). However, it can be adapted to accommodate other designs such as one single detector layer system that acts as scatterer and absorber at the same time  (Lehner et al 2004 , Mihailescu et al 2007 , Maier et al 2018 , Montémont et al 2017)  frequently employed in homeland security applications.
The GATE model of the Vereos described the hierarchical structure of all the detection modules, for a total of 23 040 LYSO scintillator crystals (4 × 4 × 19 mm 3 ). The complete digitization chain was simulated including background noise (natural radioactivity of 176 Lu in the crystals), dead time and pile-up, temporal resolution and detector quantum efficiency. The model has been evaluated following NEMA NU 2-2018 guidelines, including NECR, scatter fraction, TOF and energy resolution, sensitivity and spatial resolution. The authors reported very good agreement between experiments and simulations for clinical activity concentrations, with differences at maximum lower than 10%, concluding that the proposed GATE model can be used to very accurately reproduce PET images from Vereos system. PET2020 long axial FOV PET.
In Abi Akl et al (2019), Vandenberghe et al (2017) a long axial FOV PET scanner was simulated in GATE. Each ring consists of 36 detector modules made up of 50 × 50 × 16 mm 3 monolithic LYSO crystals. The system has an inner diameter of 65 cm. GATE was used to study the effect and advantage of axial lengths between standard 20 cm, 1 m and 2 m long systems, covering the whole human body versus a coverage of only head-to-hip. A 104 cm long system was 16 times more sensitive than a system with 20 cm axial length (1 m long uniform phantom). The effect of axial splitting of the detector rings to increase the FOV was also studied, in turn for sensitivity as well as an adaptive system bore that allows a sensitivity gain and advantages in spatial resolution due to the reduced acolinearity effect. The study showed that for objects shorter than 1 m the sensitivity gain of a 2 m scanner is limited while the detector cost is doubled compared to the 1 m system.
The acquisition FOV are 354 mm (axial) and 307 mm (radial). TMBi is encapsulated within a transparent sapphire window coupled to the MCP-PMT with optical gel. The read-out ionization pad structure is also integrated. The GATE digital detection model used dedicated parameterized modules to simulate the detector response for the ionization and light signal readout. These semi-analytic models were calibrated using detector prototypes. The first estimation of the non-paralyzable dead time was 3. 5 μs, corresponding to mean drift time and the shaping time for electronics readout. Estimated spatial resolution of reconstructed images was 1. 1 mm in the complete scanner FOV and sensitivity was 17 kcps/MBq. J-PET long axial FOV PET with plastic scintillators. J-PET is a PET system based on plastic scintillators allowing for a cost-effective total body solution  (Moskal et al 2019a, 2018, Moskal and  Stepień 2020,    readout, diameter and scintillator length, with a strip cross-section of 7 mm × 20 mm, a NECR peak of 300 kcps was reached at 40 kBq/c.
Towards simpler analysis via Python Historically, Geant4 and High Energy Physics community are linked to the ROOT CERN framework  (Brun and Rademakers 1997 ) that allows to efficiently manage and analyze physics data. Since the beginning, GATE also allows to write ROOT files, e. g. detector events or phase space, or use them as input, e. g. use phase space file as a source of particles. Python has become the tool of choice for data analysis with modules such as NumPy  (Harris et al 2020)  or Matplotlib  (Hunter 2007). Since version 9. 0, GATE now includes additional options to read/write data in NPY file format  (Harris et al 2020)  that can be processed with Python. Also, ROOT files can be processed in Python with the uproot module  (Pivarski 2020). Details about the NPY file format can be found in the GATE user guide  25  and examples of Python analysis are available on the GateContrib repository 26.
Finally, the GATE community recently started a new repository, called GateTools 27  , that gather Python functions that can be useful for simulation setup and analysis. As example, it contains tools to convert or resize images in various file formats, to convert DICOM RT structures, to manage phase-space files or analyze dose map, with DVH (Dose Volume Histogram) or gamma-index for example. All tools are available as Python function and as command-line independent scripts. At the time of writing, there are more than twenty different tools. The installation is very easy thanks to conventional Python pip install. Like GATE itself, the code is open-source and community driven. It should evolve in the future thanks to users' contributions.
STL geometry Thanks to the Geant4 G4TessellatedSolid class, tessellated mesh geometries composed of triangle primitives are now available in GATE. Users can import STL (stereolithography) files containing the coordinates of vertices and faces of the meshed surface, and GATE generates the corresponding volume in the geometry. One application of this new kind of geometry has been the modeling of the body contouring limited to cubic shapes allows the gamma camera head to move as close as possible to the phantom/patient model thereby preventing their collision in the GATE environment  (Kayal et al 2020a (Kayal et al , 2020b)). Meshes for each segmented volume of interest from the phantom/patient model were generated individually using Python scripts and imported in GATE where individual volumes could be assigned with their respective materials from CT  (Kayal et al 2021 (Kayal et al , 2020c)).
Care should be thus put in selecting the appropriate size and position of the enclosing volumes. 4. 5. Link to third-party reconstruction software Output from PET, SPECT or Compton camera simulation, either as list mode data or projections images can then be used as input for reconstruction software  (Gillam and Rafecas 2016). Among others, we can cite the following software used to reconstruct 3D images from GATE simulations. STIR (Software for Tomographic Image Reconstruction)  (Thielemans et al 2012 , Khateri et al 2019 , Kang et al 2018)  was among the first to propose dedicated modules allowing to reconstruction GATE simulated data, both for PET and SPECT, with MLEM and OSEM methods. More recently, the CASToR project (Merlin et al 2018) (Customizable and Advanced Software for Tomographic Reconstruction) also proposed various reconstruction algorithms for PET and SPECT, with dedicated tools to use GATE generated data.
For SPECT images, the QSPECT software  (Loudos et al 2010 , 2014 , Spirou et al 2015)  was also employed, in particular to investigate the effect of attenuation correction. Recently in  Robert et al (2019) , the Reconstruction Toolkit (RTK)  (Rit et al 2014)  was used to provide 4D whole body SPECT reconstruction using image-based breathing signal extraction  (Robert et al ). The PRESTO toolkit (PET Reconstruction Software Toolkit)  (Scheins and Herzog 2008 , Scheins et al 2011 , 2015)  can also be used with GATE output, such as in  Xu et al (2019). Finally, several Compton Camera reconstruction approaches have been used with GATE data, such as in  Muñoz et al (2018) ,  Kohlhase et al (2020)  (MLEM or Origin Ensemble) or in  Feng et al (2020) , Maxim (2019) (with conical Radon transform modeling).
Artificial intelligence integration There are several potential interactions between artificial intelligence, deep learning in particular, and Monte Carlo simulations. Within the context of medical imaging, these synergies can be classified in two categories. First, Monte Carlo simulations can produce highly accurate imaging device and patient training datasets for neural networks applied to various signal and image processing tasks. At the detector level, multiple works have been carried out to better determine DOI and/or events positioning within pixelated and continuous monolithic scintillators by using trained neural networks  ( Oliver et al 2013 , Conde et al 2016 , Pedemonte et al 2017 , Berg and Cherry 2018 , Müller et al 2018 , 2019 , Iborra et al 2019 , Yang 2019 , Zatcepin et al 2020).
In terms of image processing, images generated with GATE have been used as part of the training dataset for a functional tumor volume segmentation challenge. Deep learning-based segmentation approaches trained using this dataset showed improved performance compared to state-of-the-art methods  (Hatt et al 2018). Second, deep learning approaches can be applied to improve Monte Carlo simulation performance, mostly computational efficiency. One example is the simulation-trained neural networks to model detector response. In  Sarrut et al (2018) , the authors determined the Angular Response Function (ARF) of a SPECT collimatordetector system with simulation output. The resulting net modeled the probability that a photon is detected, using variance reduction to speed up the simulation. Recently, the concept of generative adversarial networks (GAN)  (Goodfellow et al 2014)  that allow modeling of multidimensional data distributions was proposed to learn a phase space  (Sarrut et al 2019 (Sarrut et al , 2021) )  generated by Monte Carlo simulation.
Conclusion This article presents an overview of the current status of GATE for emission tomography imaging system simulation, including a large list of systems that have been simulated and, at least partially, validated against experimental data. Recent developments have been described as well and give insight on future improvements in GATE. This comprehensive summary aims at providing an evaluation of the current capabilities and limitations of GATE for imaging systems simulation. The GATE software has some limitations, partly rooted in the general nature of the Monte Carlo approach, domain-specific problems, and code complexity. First, simulations are generally considered slow, in particular, compared to analytical or semi-analytical methods. Attempts have been made to provide GPU-based acceleration within GATE  (Bert et al 2012 (Bert et al , 2013) )  with acceleration factors from 20 to 400 depending on the simulation type.
However, GPU integration in GATE was discontinued due to difficulty to support generic enough graphic card types. Part of this work has been ported to the GGEMS Monte Carlo code  (Garcia et al 2016). One common alternative solution is to apply parallelization techniques by using multiple CPUs (Camarasu-Pop et al 2010). Users need, however, to pay attention to the time management in the simulation when splitting into parallel jobs, e. g. dead-time or coincidences sorting. Typically, when splitting a PET simulation into several jobs, each job should keep the same activity rate and the whole simulation should be split according to the total simulated acquisition time. Moreover, true coincidences estimation relies on the particle's ID (identification number) provided by the Geant4 kernel in order to ensure the gamma came from the same events.
Another difficulty lies in the need to precisely know the material and geometrical information of all elements in an imaging system which is often private information that can only be obtained through NDA, preventing publicly share complete imaging models. Another limitation of GATE is that the code source is rather old, more than 15 years, and acknowledges the contributions of numerous volunteers (more than 70 on the current repository that only keeps track of all authors since 2015). Hence, the size and the diversity of the C++ code, more than 350 000 lines of code, makes it relatively hard to maintain. Currently, an effort has been made to modernize the code and adapt a set of engineering techniques e. g. unit tests and continuous integration to improve the code quality. The GATE code typically quickly follows the Geant4 evolutions, thanks to the high responsiveness and support of the Geant4 community.
Numerous collaborations with the major manufacturers in this field (Philips, GE, Siemens, Spectrum Dynamics, etc) show that GATE is used not only in academia but also in industry. It might be difficult to make predictions about future GATE development directions because it is largely guided by users' projects and emerging needs. However, we can mention an ongoing trend such as Python binding, which allows performing GATE data analysis using Python scripts, providing access to a large ecosystem of libraries and tools. Moreover, the integration of PyTorch is just at the beginning and may open the doors to developments exploiting Artificial Intelligence methods. The range of PET and SPECT imaging biomarkers is rapidly expanding for both diagnostic and nuclear theranostic applications  (Czernin et al 2019) , and fast timing has now become a key issue for further improving TOF-PET image contrast and reducing radioactive doses injected to the patients  (Lecoq et al 2020).
Acknowledgments The Opengate collaboration would like to warmly thanks the Geant4 collaboration for their great support and fruitful discussions. This work was performed within the framework of the  SIRIC LYriCAN Grant INCa-INSERM-DGOS-12563 , the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the  program 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ) operated by the  ANR , and the  POPEYE ERA PerMed 2019 project  ( ANR-19-PERM-0007-04 ). Optical modeling developments were supported by  NIH  grants  R03 EB020097  and  R01 EB027130. STL related work was performed and supported by the  MRTDosimetry  project  32  which is funded by  EMPIR program  co-financed by the Participating States and from the  European Unions   Horizon 2020 research and innovation program. The patient images used were obtained as a part of  IAEA  Coordinated Research Project (CRP) on 'Dosimetry in Radiopharmaceutical therapy for personalized patient treatment' ( E2.
LRRK2 mRNA codon usage assessment In order to determine whether an mRNA of interest uses rare codons, relative synonymous codon usage (RSCU) values associated with each codon were calculated using GeneDesign 3. 0  (Richardson et al. , 2010)  and human coding sequence (CDS) from RefSeq (NM_ mRNAs-version 20160607; https:// www. ncbi. nlm. nih. gov/refseq/). RSCU values are the number of times a particular codon is observed, relative to the number of times that the codon would be observed in the absence of any codon usage bias. In the absence of any codon usage bias, the RSCU value is 1. 00. A codon that is used less frequently than expected will have a value of less than 1. 00 and vice versa for a codon that is used more frequently than expected. Codon bias of LRRK2 CDS (GenBank accession number BC117180.
Quantitative analysis of TH + neurons Quantification of TH-expressing neurons in naïve, WT and G2019S LRRK2-expressing LUHMES cells at day 1 and day 6 of differentiation was performed using ImageJ. Cells were counted as TH + (green) neurons when TH staining colocalized with nuclear Hoechst staining (blue). All data were presented as the mean±s. d. from two separate experiments and statistically analyzed by one-way analysis of variance (ANOVA) with Turkey's post test for multiple groups. The differences were considered statistically significant when the P-value was less than or equal to 0. 05 with n≥6 from two independent experiments.
Quantitative imaging of nuclear, mitochondrial and lysosomal morphology For quantitative imaging of nuclear morphology, LUHMES cells were seeded at 3500 cells/well in 96-well plates. After 3 days of differentiation, Nuclight Rapid Red Reagent for live cell nuclear labeling (Sartorius, catalog no. 4717) was added at 1:2000 final dilution in medium following the supplier instructions. Images were collected using an Incucyte S3 Live Cell Imaging System (Essen Bioscience) 1 h after dye loading using one-phase contrast and one fluorescent channel (excitation/emission wavelength of 655/681 nm) and a 10× objective. Red eccentricity object average was measured with the Incucyte analysis software. For quantitative imaging of mitochondrial morphology, LUHMES cells were cultured in 80 cm 2 flasks (ThermoFisher Scientific, catalog no.
H3570) and Cellmask Orange (Invitrogen, catalog no. C10045). Pictures were taken on the Operetta CLS high-content imaging system (Perkin Elmer) using a 40× water objective. The algorithm to analyze mitochondria morphological changes, was developed with the Harmony 4. 9 analysis software (Perkin Elmer). For quantitative imaging of lysosomal morphology, LUHMES cells were differentiated in 96-well plates (Greiner, catalog no. 655946) pre-coated with poly-D-lysine and fixed, permeabilized and stained as indicated in the 'Immunofluorescence' section above. An anti-LAMP2 mouse (Santa Cruz Biotechnology, catalog no. sc18822) primary monoclonal antibody was used at 1:2000 dilution. Secondary antibody was goat anti-mouse IgG Alexa Fluor 488 (Invitrogen) at 1:200 dilution. Images were taken using an Operetta CLS high-content imaging system (Perkin Elmer) using a 40× water objective.