dataesr/scientific-paragraphs-categorization huggingface dataset:
P-ONE is primarily designed for neutrino source searches, and the optimization for tracks also takes advantage of the superior angular resolution of the track channel. The detector design is segmented rather than evenly filled in, the final design calls for eight segments, each with 10 strings. An initial pathfinder called STtrings for Absorption length in Water (STRAW) has been deployed in the Cascadia basin to qualify the site  [630]. STRAW-a, deployed in 2018, measured the optical attenuation length at the site. The measured attenuation length is 28 m at 450 nm, which qualifies the site for deployment of P-ONE  [631]. The STRAW lines include Precision Optical Calibration Modules (POCAMs)  [632] , calibration devices which were designed for the IceCube Upgrade, as well as digital optical modules.
The stations are separated by a large enough distance (2 km) that each station acts as its own independent interferometer. The ARA collaboration has recently analyzed a subset of its full dataset placing strong limits on the neutrino flux at EeV energies with the expectation of stronger limits to come in the near future  [299]. One station, the phased array on ARA5, uses a novel phased array technique to lower the energy threshold of the instrument  [659, 660]. Additionally, the effective volume of ARA may be enhanced by up to 25% through the observation of secondary leptons (including tau leptons) from CC interactions  [555]. There are some indications that flavor may be tagged in ARA and other Askaryan detectors by separating electromagnetic and hadronic showers  [555, 661]. With ARA's large exposure, such studies warrant further scrutiny.
This figure shows the ν μ flux for standard and tau-optimized configurations. As it can be seen that the ν μ flux is important in the neutrino energy region between 0 GeV to 5 GeV, where the δ CP is much smaller. However, the energy region is larger than 5 GeV, at which the cross-section of ν τ interactions is pivotal. The LBNF beam simulation, known as G4LBNF, is a Geant4 based simulation using the QGSP_BERT physics list. These models can be tuned to get more precise neutrino flux by using an external dataset provided by hadron production experiments, such as NA61/SHINE (CERN) and EMPHATIC (Fermilab) measurements. The G4LBNF simulation is currently running for the version v3r5p8 tagged with the GEANT v4. 10. 3. p03b version and the default hadronic model is the QGSP_BERT due to it runs importantly faster than FTFP_BERT.
At low energies, the flux is dominated by the direct D s → ν τ process. At high energies, the chain decay D s → τ → ν τ dominates  [125]. The SHiP detector cross sectional area of 0. 8 m × 0. 8 m at a distance 46 m from the proton target means that neutrinos must arrive in the rapidity range of η ν 5. 8-6. 1. Evaluations of the flux using SHiP's FairShip software based on Pythia 8 (Pythia 6 for heavy flavor, a leading order treatment) for the primary proton collisions, GEANT4 for propagation and GENIE for neutrino interactions yields of order 11 000 detectable ν τ + ντ CC events accounting for detection efficiencies  [323]. Their simulations show an average energy in the ν τ (ν τ ) CC events of 52  (70)  GeV. Evaluations of the ν τ + ντ energy distributions for SHiP in an earlier configuration have been performed at next-to-leading order (NLO) in QCD  [125, 715, 716].
On the other hand, neutrino experiments have been running behind in adopting new models and corrections due to their high dependence on computation simulations and software development that must be implemented in the nuclear event generators. Having the theoretical sector moving faster than the experimental side provokes a deceleration in the field as a whole, testing and improving models of neutrino-nucleus in generators is a factor of fundamental importance. MC simulations are the basis for any analysis, some well know neutrino generators are GiBBU  [406] , NuWro  [750] , NEUT  [751] , NUISANCE  [752] , GENIE  [329] , FLUKA  [753]  and TAUOLA  [754] ; a recommended read about the particularities of these event generators can be found in this notes  [755]. All of them take different approaches for different reasons, but as a general comment we can say that their task are: simulate neutrino interaction, simulate signals and backgrounds observed in the detector, be a bridge to compare real data and theories in order to extract neutrino oscillation parameters, reduce systematic uncertainties in measuring physics observations.
Now, in real physics, ISI and FSI cannot be factorized, the nucleon potential links ISI and FSI, in order to factorize ISI and FSI it would require no potential at the point of first interaction. 6. 2. 2. GENIE. GENIE is the most extensive international collaboration for a neutrino event generator; it emphasizes extensibility, modularity, and flexibility at the software level. Diverse experiments hosted in Fermilab (MINERvA, NOvA, SBN, and DUNE) have taken on many GENIE developments and, in this way, serve to support the large user base at Fermilab. Current versions of GENIE, starting with GENIE 2. 10, offers new improvements to the intranuclear rescattering simulation to better characterize the A-dependence of rescattering processes, the inclusion of an effective spectral function model  [762]  that combines a nuclear model based on super-scaling phenomenology with modeling of multi-nucleon scattering processes, the inclusion of a model for neutrino production of single kaons  [763].
The focus of neutrino interaction modeling in GENIE has been the few GeV neutrino energy range, which is relevant for atmospheric and accelerator-based neutrino experiments. A new package, called HEDIS, has been created implementing high-energy cross section calculation  [764]. This new module incorporates DIS off nucleons (modeled at NLO  [434, 765] ), Glashow scattering  [766]  and sub-leading resonant effects due to neutrino interactions with the photon field of the nuclei  [767]. It can be used strictly for neutrino energies above 100 GeV, and has been validated up to 10 EeV. 6. 2. 3. NuWRO. Developed by a theory group at Wroclaw University  [750]. Address for the problem of hadronization  [768]  in nuclei and transitions between models, by example: the transition region between RES and DIS, or the transition between low mass hadronization and PYTHIA  [769].
While the table-based approach has been important for current lower energy analyses in IceCube DeepCore (both ν τ related and otherwise), the reconstructions are a significant computational bottleneck and will only become more onerous in the next generation of lower energy VLVnTs due to the increasing dimensionality of the segmented optical sensors to be deployed. Notably, the multi-PMT digital optical modules-pioneered by the KM3NeT consortium  [807]  and modified for in-ice use in future IceCube extensions  [808] -consist of 24 PMTs with 3 diameter, which in comparison to the single PMT in the existing IceCube DOMs would increase the reconstruction time beyond 'bottleneck' category and into the 'functionally unfeasible' category. The future of reconstruction algorithms for large volume neutrino telescopes with multipixel sensors is being actively pursued with machine learning.
Recently several groups have emerged to accurately model tau propagation, including NuPropEarth, NuTauSim, TauRunner, nuPyProp in νSpaceSim, and DANTON. Table  10  outlines the main features of these codes, and we briefly describe the benefits of these modeling tools here. Figures  61  and 62  show a comparison of the tau exit probability and energy distribution of outgoing leptons for different packages. 6. 6. 1. nuPyProp. The nuPyProp  [822]  open source program is designed to provide look-up tables for charged lepton exit probabilities and energies that are input to nuSpaceSim  [829] , a package for the end-to-end modeling of optical and radio signals of EAS that originate with tau neutrino and muon neutrino incident on the Earth. The intended use of nuSpaceSim is for space-based and suborbital detector design and analysis.
The nuPyProp code is a standalone python package and command line utility that is available on GitHub  61  and through pip and conda installations. Sub-modules written in fortran, wrapped with F2PY, and the use of OpenMP make this code relatively fast. Neutrino interactions and charged lepton interactions are implemented stochastically. Several neutrino cross section models and charged lepton photonuclear energy loss models are included along with standard bremsstrahlung, pair production and ionization energy loss inputs. Templates are provided for the user to input other neutrino and charged lepton interaction models, for example, to allow for BSM physics. Approximate density dependent correction factors to the nuclear charge Z and atomic number A are incorporated in the charged lepton electromagnetic energy loss evaluation.
The tau neutrino energy distribution from tau decays in the regeneration process is parameterized. The PREM Earth density is the default Earth model, with the option to set the outer water layer depth from 0-10 km (in 1 km units), thus allowing its adaptation to neutrino and muon or tau propagation through mountains, for example. 6. 6. 2. TauRunner. TauRunner  [820]  is an open-source, Python-based package for propagating neutral and charged leptons. The standalone version of the code can be installed via pip. Once installed this may be imported into a Python script or a Jupyter notebook. Additionally, the user may download the source code from GitHub 62  and use the command-line interface. TauRunner uses a MC approach to propagate leptons, including stochastic energy losses for all charged leptons via the PROPOSAL  [735]  package.
The program propagates all leptons that can travel macroscopic distances-i. e. all except e ± -and returns the initial and final particle energies, particle type, parent particle, final position, and number of interactions. This allows the user to track the full physics of propagation. Furthermore, TauRunner can simulate an Earth-traversing EeV ν τ in ∼10 μs, allowing millions of events to be simulated on a single core in ∼ hours. Notably, stochastic loss treatmentsx allow for this software to function as a MC event generator for next-generation experiments as it can track individual events and record their interactions. TauRunner is also quite flexible, allowing the user to provide their own cross-section models, flux predictions, and propagation media. By default, the software provides two representative cross-section models, one based on NLO QCD calculations, and the other based on a dipole model of the nucleus.
6. 6. 3. NuPropEarth. NuPropEarth  [824]  is a open-source, C++-based package available on GitHub  63. It has the structure of a general-purpose MC event generator, and therefore allows following the path and interactions of individual neutrinos and tau leptons as they travel through Earth on an event-by-event basis. Neutrino interactions are simulated with differential cross sections in GENIE, with the possibility of using customized DIS SF. Several precomputed SF tables (as function of x, Q 2 ) based on NLO models are available  [434, 765]. GENIE also accounts for other interaction channels like Glashow resonance  [766]  or W boson production  [767]. These subdominant interactions can play a significant role in the detection of tau neutrinos from cosmic origin  [830]. Energy losses due to electromagnetic interactions of tau leptons are modeled with TAUSIC  [831]  or PROPOSAL  [735] , while its decays are computed with TAUOLA  [832].
The grammage profile used to propagate events is calculated using the PREM Earth density model with an added outer layer with a user input depth and density to model more local topographies. NuTauSim is a highly modular code and can easily be modified to include different models not currently involved in the base implementation to quickly evaluate novel scenarios. NuTauSim is available for download on GitHub  64. Recent updates to NuTauSim have been made to allow for the propagation of muon neutrinos and muons from τ -lepton decay, as well as the consideration of events from Glashow resonance. This updated version, called NuLeptonSim will be available for public release in 2022. 6. 6. 5. Danton. Danton is a detailed MC engine dedicated to the coupled ν τ -τ transport problem. A peculiarity of Danton is that it can operate in forward or in backward Monte Carlo (BMC), using the method discussed in  [835].
In addition, Danton allows to use detailed Earth topography data through the TURTLE  [837]  library. The Danton package is hosted on GitHub 67  under the terms of the LGPL-3. 0 license. It can be used as a C library (libdanton) or as an executable (danton). The danton executable is steered by data cards, in JSON format. On Linux systems, it is available as a standalone AppImage. 6. 6. 6. νSpaceSim. The simulation package νSpaceSim  [829, 838]  provides a vectorized Python framework that used the Earth-emergent tau flux predicted by tau neutrino generators, baselined with nuPyProp, to then generate the EAS from the tau decays, generate the optical Cherenkov and geomagnetic radio EAS signals, and record these using simulated detector responses. The unique nature of the tau decays, including effects of depolarization due to energy losses in the Earth, induces variability in the EAS particle profiles, as shown in figure  63.
In-ice radio detection simulation codes To simulate the prospects of in-ice neutrino detectors, interpret its data, as well as to develop reconstruction algorithms, a precise and accurate MC code is required. The in-ice radio community is jointly developing the NuRadioMC simulation code  [817]  which is available opensource through github  68  building upon the codes ARASim  [840, 841] , ShelfMC  [842]  and PyREX 69   [843]. The codes were thoroughly checked against each other and yield the same results within a few percent for the same physics settings. NuRadioMC simulates the neutrino interaction in the ice, the generation of the radio signal, the signal propagation to the antennas, and a full detector and trigger simulation. Several milestones have already been achieved which are relevant for tau detection.
A precise calculation of the Askaryan emission including a realistic modeling of the LPM effect for high-energy ν e -CC interactions  [595]  was implemented which allows to study the identification of ν e -CC interactions vs other types of neutrino interactions, and thereby enables a estimation of the combined muon and tau neutrino flux vs the electron neutrino flux at UHE energies  [454]. In addition, the PROPOSAL code  [735]  was integrated into NuRadioMC to simulate the radio emission generated by secondary muons and taus produced in the initial neutrino interactions  [555, 665]. These features allow to study the prospects for tau and tau neutrino detection in in-ice RDs. The simulation of tau regeneration through the Earth is currently being integrated into NuRadioMC which will provide another handle on tau detection with in-ice RDs.
Acknowledgments PBD and JG acknowledge support from the  US Department of Energy  under Grant Contract  DE-SC0012704. SAW acknowledges support from the  National Science Foundation  through Awards  2111232  and  CAREER 2033500  and  NASA  through Awards  80NSSC20K1288 ,  80NSSC20K0925 , and  80NSSC20K0925. MHR acknowledges support from  US DOE  Grant  DE-SC-0010113. SRK acknowledges support from the  National Science Foundation  under grant number  PHY-1307472  and the  US Department of Energy  under Contract Numbers  DE-AC-76SF00098. AA acknowledges support from the  US Department of Energy, Office of Science, Office of High Energy Physics , under Award Number  DE-SC0011784. CAA is supported by the  Faculty of Arts and Sciences of Harvard University  and the  Alfred P Sloan Foundation. AG acknowledges support from  European Union 's  H2020-MSCA Grant  Agreement No.
101025085. JLF is supported in part by  US NSF  Grants  PHY-1915005  and  PHY-2111427 , Simons Investigator Award # 376204 ,  Simons Foundation  Grant  623683 , and  Heising-Simons Foundation  Grants  2019-1179  and  2020-1840. ST is supported by the Grant 'AstroCeNT:  Particle Astrophysics Science and Technology Centre ' carried out within the  International Research Agendas programme of the Foundation for Polish Science  financed by the  European Union  under the  European Regional Development Fund  and by the  Polish Ministry of Science and Higher Education  through its scholarship for young and outstanding scientists (Decision No. 1190/E-78/STYP/14/2019 ). YSJ acknowledges support from the  National Research Foundation of Korea (NRF)  Grant funded by the  Korea government through Ministry of Science and ICT  Grant  2021R1A2C1009296.
Software description Programming details. The source code (in Java) is available here under the open source MIT license: https://github. com/jeris90/fusionAF. Our implementation is based on the solver jArgSemSat  [5]  for computing the extensions. For guaranteeing the ease of evolution of the software, it highly relies on the Factory design pattern  [12]. We exemplify it on the case of distances. The abstract class Distance only provides an abstract method that computes the distance between two sets of arguments. If the user wants to add a new distance to the application, everything s/he has to do is implement a new class MyDistance that inherits from Distance, and to modify the method makeDistance in the class DistanceFactory, by adding the line 6 in the following code: The strings ("HM" for Hamming, "JC" for Jaccard,.
• -a (or --arg) <argument> is a mandatory option with the option -p DC or -p DS to specify the targeted argument. • -print prints all details of the aggregation process. • -h (or --help) is the help option. The default parameters are --distance HM, --agg_function SUM, and --problem EE. Notice that every AF in the profile can be evaluated with its own semantics, specified by the extension of the file name. For instance, af1. apx. st means that the first AF uses the stable semantics, while af2. apx. pr means that the second AF uses the preferred semantics. All the semantics implemented in jArgSemSat are available. Figure  3  is an example of how to use our program (with inputs and output) to merge the profile of AFs described in Example 1.
Whole-Exome Sequencing DNA underwent whole-exome sequencing (WES) with SureSelect Human All Exon V6 (Agilent) capture and sequencing on the NovaSeq 6000 (Illumina). All WES data were processed using the Cpipe pipeline  (24)  and deposited into SeqR for analysis (https://seqr. broadinstitute. org/). We performed 2 phases of analysis as previously described (25)-the first was gene-centric (POI genes, list available upon request) and the second was an unbiased variant-centric approach. Variant-centric analysis focused on high-priority variants (those considered likely to lead to loss of function: frameshift, nonsense, and splice site variants) in any gene and with any inheritance, or potentially biallelic moderate-high priority variants (missense, in-frame indels, frameshift, nonsense, or splice spite).
Only variants with minor allele frequency (MAF) <0. 001 in the 1000 Genomes and gnomAD (https:// gnomad. broadinstitute. org/) databases and with high quality scores (Q > 50 and allele balance > 25) were considered. Missense variant pathogenicity was predicted in silico using Mutation Taster (http://www. mutationtaster. org/), Polyphen-2 (http://genetics. bwh. harvard. edu/pph2/), SIFT/Provean (http:// provean. jcvi. org/), and CADD (Combined Annotation-Dependent Depletion) (https://cadd. gs. washington. edu/snv). The conservation of affected residues was assessed by Multiz Alignments of 100 vertebrates (UCSC Genome Browser https://genome. ucsc. edu/). Described variants were submitted to ClinVar (SCV002515966-SCV002515967).
RNA Seq Lymphoblast RNA quality was assessed using TapeStation and 400 ng RNA was used for Illumina TruSeq Stranded mRNA library preparation. Sequencing was performed on the NovaSeq 6000 with 2 × 150 bp reads at depth of ∼80 M reads per sample. Raw data were filtered for quality, then trimmed using Cutadept. Reads were aligned to the human genome (GRCh38) using HISAT2  (26). Splicing effects were assessed visually using the Integrative Genomics Viewer8 (Broad Institute, MA) and its in-built Sashimi plot function  (27).
For data-independent acquisition (DIA) experiments, full MS resolutions were set to 120 000 at m/z and scanning from 350-1400 m/z in the profile mode. Full MS automatic gain control (AGC) target was 250% with an IT of 50 ms. AGC target value for fragment spectra was set at 2000%; 50 windows of 13. 7 Da were used with an overlap of 1 Da. Resolution was set to 30 000 and maximum IT to 55 ms. Normalized collision energy was set at 30%. All data were acquired in centroid mode using positive polarity. Raw data were processed using the Spectronaut platform (version 15. 2. 210819. 50606, Rubin) and searched against the UniProt human database (canonical peptides + isoforms, reviewed, 42 386 entries). The protein report was imported into the Perseus software package (version 1. 6. 15. 0). Briefly, MS2 Quantity outputs for patient and control samples were Log 2 -transformed and potential contaminants (eg, keratin) were removed using a curated list.
Proteins were annotated using the MitoCarta 3. 0 dataset by matching against UniProt ID. Patient and control samples were grouped (patient/control) and proteins with fewer than 2 hits per group were filtered. The whole cell dataset was normalized to the mean using the "subtract" function. For the mitochondrial complement, all non-mitochondrial proteins were excluded. For both whole cell and mitochondrial datasets, a two-sided Student t test was performed with significance determined by P value (P value < 0. 05), displayed as a volcano plot using the scatter plot function in Perseus. For the relative peptide abundance plot, the "PEP. MS2 Quantity" option was selected in Spectronaut to export the peptide report. CLPB peptides were filtered from "PG. Genes' column and the difference of the means of the Log 2 MS2 intensities from patient and control were calculated for each CLPB peptide.
45 µM PVDF membrane (Millipore) using a semidry transfer apparatus prior to immunoblotting with mouse monoclonal SDHA primary antibody (Abcam: Ab14715, RRID: AB_301433) or rabbit polyclonal CLPB primary antibody (Abcam: Ab235349, RRID: AB_2847899) followed by secondary antibodies. Proteins were detected using Clarity Western ECL Substrate (Bio-Rad) on a ChemiDoc MP imaging machine (Bio-Rad). Quantification of bands was performed according to the manufacturer's instructions using the Image Lab software (Bio-Rad).
WES Identifies Compound Heterozygous CLPB Variants The patient was referred to our research program after pediatric endocrinology assessment and a diagnosis of POI at age 15 years with an elevated FSH of 73 IU/L. Further clinical details were not communicated at this time and initial data analysis was performed on the assumption that she suffered isolated POI. Median exonic depth of coverage was 69× with > 95% of bases having a depth of greater than 10. There were 152 moderate-high priority variants detected in the exome of the patient that were further filtered to identify the likely causative genetic defect (Fig. 1A ). For gene-centric analysis, 9 missense variants in candidate POI genes were considered (Fig. 1A ). These were discounted because their frequency in public databases was too high to cause POI in heterozygous state, the variants were predicted benign by online algorithms and/or were inherited from the unaffected mother.