dataesr/scientific-paragraphs-categorization huggingface dataset:
3  Once simulations that treat all six flavors-3 neutrino plus 3 antineutrino flavors-as separate become more frequent, we expect to update SNEWPY to accommodate this. The customized get initial spectra member function of each model class extracts the neutrino spectra Φ(E ν ) from the simulation data if it is provided but, more commonly, the method constructs a Kuroda 2020 20 0. 00 -0. 476, depending on rotation and magnetic field 3D simulation of a magnetized rotating star from Kuroda (2021) using a 20 M progenitor by  Woosley & Heger (2007)  and SFHo EOS.
Fornax 2021 12  , 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27 -0. 21 -4. 49 (12) -0. 31 -4. 59 (27)  Axisymmetric models simulated to 4. 5 s post-bounce using the FORNAX code, described in  Burrows & Vartanyan (2021). Zha 2021 16, 17, 18, 19, 19. 89, 20, 21, 22. 39, 23, 24, 25, 26, 30, 33  -0. 21 -2. 02 (16) -0. 30 -0. 35 (33) Failing CCSN simulations with a hybrid EOS including a hadron-quark phase transition  (Zha et al. 2021). Table  1. CCSN simulation models with neutrino emission tables included in SNEWPY. User-defined CCSN models can be created by subclassing SupernovaModel in the module snewpy. models. spectrum according to a parameterization. A common parameterization of this spectrum-though not required by SNEWPY-is in terms of the luminosity L ν , the mean energy E ν , and the spectral shape parameter α  (Keil et al.
Currently, the module snewpy. flavor transformation supports fifteen transformation scenarios: six for the normal mass ordering (NMO), six for the inverted mass ordering (IMO), and three which are independent of the mass ordering. The list of transformations included in SNEWPY is provided in Table  2. The values used for the neutrino mixing parameters are contained inside two instances of a class called Mix-ingParameters that is also part of SNEWPY, one instance for the NMO and another for the IMO. They are set by default to the values from  Esteban et al. (2020)  but may be modified by the user. With both the model and flavor transformation prescriptions now defined, the previously mentioned get transformed spectra member function of each model class is seen to be the implementation of equations (  13 ) to (  16 ).
Generating SNOwGLoBES Input Files The snewpy. snowglobes module contains the two functions generate time series and generate fluence. The generate time series function constructs a set of data files at a set of snapshot times from a chosen simulation. For each snapshot, the code extracts (or constructs) the neutrino spectra at the neutrinosphere. It then applies a user-selected flavor transformation prescription to those spectra and scales with a user-selected supernova distance to generate the flux at Earth. Finally, the function writes the fluence-the flux multiplied by the time bin width-for the snapshot to a SNOwGLoBES-formatted file and collates the files for different snapshot times into a single compressed output file. A number of options are available to the user to control how many snapshots are created and their spacing in time.
Simulating Detector Effects with SNOwGLoBES The snewpy. snowglobes module contains a function named simulate which takes a compressed file, such as those generated by generate time series or generate fluence, and runs the contents through the SNOwGLoBES software. The bulk of the simulate function is a translation into Python of the script supernova. pl that comes with SNOwGLoBES: simulate also creates a supernova. glb file then invokes supernova, the executable in SNOwGLoBES. The mandatory arguments passed to simulate are the location of the SNOwGLoBES installation directory and the path to the compressed input file. Another, optional argument is the name of a detector. If this argument is not supplied, SNOwGLoBES is repeatedly invoked for all supported detectors. There is no output from the simulate function itself; instead, upon successful execution of the function a large set of files (each containing the event rate in a given set of energy bins for a particular interaction channel due to a particular flavor) is generated in the SNOwGLoBES output folder.
Using SNEWPY with SNOwGLoBES In this section we provide a simple example of using SNEWPY with the SNOwGLoBES software. This particular script reproduces the Super-Kamiokande entry in Table  4  of Al  Kharusi et al. (2021)  for the 11. 2 M model assuming adiabatic MSW oscillations with the normal mass ordering (4045 neutrinos). A script that calculates the entire Table  4  in Al  Kharusi et al. (2021)  is one of the previously mentioned Jupyter notebooks that are included in the package. total_events += n_events print(f"{channel:10}: {n_events:. 3f} events") print("Total events in Super-K-like detector:", total_events) 6. 2. Using SNEWPY as Part of the Event Generator sntools sntools  (Migenda et al. 2021 ) is an event generator that takes neutrino fluxes from supernova simulations and generates a list of resulting neutrino interactions in a detector with a realistic distribution of event time as well as energy and direction of outgoing particles.
This list can then be used with a full detector simulation and event reconstruction toolchain for situations in which the approximate treatment of detector effects in SNOwGLoBES is insufficient. In a recent update, sntools integrated SNEWPY to benefit from the large number of supernova models and flavor transformations it implements. For example, generating a set of neutrino events in the Hyper-Kamiokande detector  (Abe et al. 2018)  for the first 500 ms of one of the models included in SNEWPY at a supernova distance of 50 kpc can be done as follows: # install sntools (this automatically installs SNEWPY as a dependency) pip install sntools # download supernova model files that are part of SNEWPY python -c 'import snewpy; snewpy. get_models("Bollig_2016")' # run sntools using an input file from SNEWPY sntools SNEWPY_models/Bollig_2016/s27.
SUMMARY The SNEWPY software package connects supernova simulations with detector response software such as SNOwGLoBES and sntools, allowing users to calculate the expected event rates in various neutrino detectors for each model. We expect SNEWPY will prove useful to modelers and theorists interested in what detectors will observe given some new piece of physics in a simulation, and to experimentalists wishing to evaluate the sensitivity of their detector to supernova neutrinos. In the future we plan to enhance the capabilities of SNEWPY and suggestions from the community about the features we should add are warmly welcome. Modelers interested in adding their simulations to the model library are encouraged to contact us.
ACKNOWLEDGMENTS This work is supported by the  National Science Foundation  "Windows on the Universe: the  Era of Multi-Messenger Astrophysics " Program: " WoU-MMA: Collaborative Research: A Next-Generation SuperNova Early Warning System for Multimessenger Astronomy " through Grant Nos. 1914448, 1914409, 1914447, 1914418, 1914410, 1914416, and  1914426. This work is also supported at  NC State  by  DOE  grant  DE-FG02-02ER41216 , at King's  College London  by  STFC , and at  Stockholm University  by the  Swedish Research Council  (Project No. 2020-00452 ).
Stochastic simulation programs are useful to study the evolution of crop populations in changing environments. These programs require to take into account some specific features characterizing farmers' practices such as seed circulation or seed mixing. However such human-mediated evolutionary processes are different from the classical colonization or migration models used in most existing stochastic simulation programs for quantitative population genetics. To fill this gap, CropMetaPop is an individual based forward-time simulation program for quantitative population genetics developed to represent a diversity of socially established rules underlying seed circulation between farmers. For instance, one farmer can give seeds to neighbours according to his stock and receive from them according to his needs.
QuantiNemo2  [9]  is considered as the most comprehensive and flexible quantitative and population genetics simulation software. Indeed, it enables to easily address point 1) by using a high value for fecundity parameter; point 2), by specifying the dispersal rate explicitly for each pair of patches and using the propagule-pool island model of seed dispersion; point 3) by using a low migration rate and point 4) by using a high value of fecundity to ensure large amounts of seeds. The main difference between QuantiNemo2 and CropMetaPop (CMP) population life cycles is that most of the events in CMP happen during the seed phase, while QuantiNemo2 is divided in juvenile and adult phases. In CMP, during withinpopulation regulation the number of seeds produced by a population is reduced to the carrying capacity according to individual plant fitness.
To fill this gap, the CropMetaPop simulation program models the genetic evolution of the populations of a crop mepapopulation submitted to human farming and selection practices and allows the generation or import of various connection matrices to link these populations together. These matrices make it possible to represent an infinity of social arrangements between farmers growing these populations and they can differ depending on the seed circulation process (migration or colonization). Crop-MetaPop is therefore particularly designed to study simultaneously the consequences of different management practices, different social organizations or environmental conditions on the evolution of the neutral and adaptive genetic diversity of simulated crop metapopulations.
Methods Technical features of CropMetaPop CropMetaPop is a wrapper library for simuPOP to ease simuPOP's configuration for the simulation of crop metapopulations. simuPOP  [11]  is a python library for the simulation of stochastic individual-based model accounting for mutation, genetic drift, migration and selection and for demographic processes such as extinction and colonization. CropMetaPop provides a useful and practical addition to simuPOP by integrating specific characteristics of crop metapopulations, such as in particular the possibility to generate or import various connection matrices to link the field together. CropMetaPop is a console program written in Python 3 using an object-oriented approach. It allows running simulations without knowledge about coding by just filling a text based simulation file and it can be run on any computer platform.
Special features In CropMetaPop, the focus has been on describing seed circulation practices in order to represent a diversity of socially established rules that underpin the circulation of seed among farmers. Two different processes are considered to model seed circulation: colonization and migration. Colonization arises after an extinction of a local population while migration happens when farmers mix intentionally or unintentionally their own seed lot with foreign ones. Unlike other metapopulation simulation softwares, CropMetaPop can model both colonization and migration in the same simulation, with the possibility to use two different social networks, if necessary, because we consider that farmers can solicit two different social networks depending on the context. In both cases, seed circulation is a stochastic process and seed supply can come from one or from several non-empty fields when farmers are socially connected.
Conclusion CropMetaPop is a stochastic simulation model of population genetics that can help study the evolution of crop populations submitted to new environments or breeding practices. It allows to take into account the specificity of the collective management of crops diversity such as seed circulation and seed mixtures operated by farmers, without the hassle and prerequisite of writing code. CropMetaPop is flexible, as it based on the simuPOP Python library, and additional features can be easily implemented. It can be very useful to forecast the outcome of possible management actions decided by farmers and/or to help them adapt their practices to new environmental or socioeconomic constraints. It can also be used to monitor participatory plant breeding programs.
Acknowledgements The authors are very grateful to the MIRES network financially supported by  INRAE  and to  François Massol  for his very helpful advices. This research was funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  633571  ( DIVERSIFOOD  project) for the period 2015-2019 and BR received a grant from the  École Doctorale Frontières de l'Innovation en Recherche et Éducation -Programme Bettencourt.
Methods: CuidaTEXT is informed by the stress process framework and social cognitive theory. We developed and refined CuidaTEXT using a mixed methods approach that included thematic analysis and descriptive statistics. We followed 6 user-centered design stages, namely, the selection of design principles, software vendor collaboration, evidence-based foundation, caregiver and research and clinical advisory board guidance, sketching and prototyping, and usability testing of the prototype of CuidaTEXT among 5 Latinx caregivers.
The booklet is not necessary to use CuidaTEXT. However, the research team considered it to be useful for those who want to learn about the intervention faster or increase personal sense of agency. On the basis of our previous development experience with the Latinx community  [22, 30] , we made the booklet available in both English and Spanish and used lay language and a pictorial format. A total of 7 research team members tested the SMS text messaging prototype powered by the vendor on their own cell phones from early June to August 2021 and provided feedback that was used for message refinement iteratively, as suggested by the literature  [68, 69]. We recorded the feedback via SMS text message responses within the vendor platform and emails from the research team to the vendor's programmer.
Third, we tailored the response to the keyword STOP (discontinuing the intervention). We also tailored the notification CuidaTEXT automatically sends out when a participant texts outside of business hours by including both languages within the same message because the platform did not allow separate messages in English and Spanish. Participants shared mostly positive feedback at the end of the interview, including the following. First, satisfaction with the intervention in terms of general content, logistics, and simplicity was high. Comments included the following: "I think the program is great," "I love the information and the testimonials," "The messages made me feel like I'm not alone and put things into perspective," and "The messages are simple, and the gratitude-theme messages helped.
Final Product Figure  1  summarizes the final CuidaTEXT product, including an example of the 3 types of SMS text messaging interaction modalities (daily automatic, keyword-driven, and live chat messages) and the reference booklet. The final version of CuidaTEXT includes 244 English-and 244 Spanish-language messages within the daily automatic SMS text message library. These messages will be automatically sent to all participants, starting with approximately 3 messages per day for the first 2 weeks, 2 per day for the following 2 weeks, and 1 per day for the remainder of the intervention. This daily automatic SMS text message library includes logistics messages that greet the participant on starting and completing the intervention, explain the intervention functions (eg, reminding participants of the keywords they can use for help with specific topics), and reinforce participants for being in the intervention after 2 weeks initially and monthly.
If CuidaTEXT proves to be effective in future studies, this intervention could be easily implemented in clinics and community organizations, by having the caregivers send an SMS text message to enroll or by having staff enter their phone numbers and names on a website. The SMS text messaging modality may be combined with other modalities to enhance its effectiveness. For example, coaches or social workers could, in addition to interacting via live chat SMS text messages, conduct ad hoc visits or calls with the caregiver. Other findings from this study might also be useful to clinicians, including the need to consider shared caregiver roles within Latinx families and other preferences. The findings reported in this manuscript may also inform future research. Future SMS text messaging studies (whether they are dementia-related or not) might decide to address the content or logistics of their interventions based on the feedback we received from the advisory board sessions or usability testing feedback.
Conclusions This study describes the development of CuidaTEXT, the first tailored SMS text messaging intervention specifically designed to support family caregivers of individuals with dementia in the Latinx community. The prototype of CuidaTEXT has shown very high usability, addresses Latinx caregiver needs, and has the potential for widespread implementation. The findings from several stages of the user-centered design provide useful information to guide the development and refinement of caregiver support interventions for Latinx individuals and other groups. This information contributes to efforts to address dementia disparities among Latinx individuals and gaps in the implementation of caregiver support interventions for this sizable population. We will soon test the feasibility and acceptability of this promising intervention (CuidaTEXT) in a 1-arm trial among Latinx family caregivers of 20 individuals with dementia (ClinicalTrials.
Acknowledgments This work was supported by the  National Institutes of Health  under grants  R21 AG065755 ,  K01 MD014177 , and  P30 AG072973. All authors made substantial contributions to the conception or design of the work, interpretation of data for the work, and drafting and critically revising the work for important intellectual content. JPP thanks the national and local organizations that have partnered with him to conduct this and other research projects since 2015. JPP also thanks the research participants included in all stages of this research as well as anyone who has contributed directly and indirectly to this research.
We washed the beads twice with 1. 5 mL of 70% ethanol, and finally eluted the DNA samples in 30 µL of Qiagen buffer EB. Sequencing libraries were prepared and sequenced at the University of Delaware Sequencing & Genotyping Center on a PacBio RSII. We also supplemented our PacBio dataset with Oxford Nanopore sequencing data. We isolated DNA from a single T. rathkei female and two separate males using a Qiagen DNEasy Kit as described above. We then performed sequencing on Oxford Nanopore Minion flowcells (R9. 4) with the Rapid Sequencing Kit (SQK-RAD004) following the manufacturer's instructions.
Genome assembly We performed a hybrid assembly combining the short-and long-read sequence data, closely mirroring the bioinformatics pipeline used to generate previously published isopod genome assemblies  (Chebbi et al. , 2019; Becking et al. , 2019). We first removed adapters and trimmed the Illumina sequencing data using Trimmomatic v. 0. 36  (Bolger et al. , 2014) ; we removed leading and trailing bases, as well as internal windows of at least 4 bp, with a mean quality score of 5 or lower, and discarded any reads shorter than 36 bp after trimming. We then used SparseAssembler  (Ye et al. , 2012)  to assemble the cleaned Illumina data from sample Mpool, This dataset was chosen because it had decent coverage (~38x), was generated from a PCRfree sequencing library, and came from male samples (so that Y chromosome sequences would be present); additional male sequencing samples were excluded from this initial assembly to minimize the number of sequence polymorphisms that would be present in the data with additional samples.
5 -L 6 for mapping to an error-prone assembly;  (Li, 2013) ). Finally, we assessed the quality of each of the three candidate assemblies using BUSCO v. 3. 0. 2  (Simão et al. , 2015) , with the arthropod reference gene set, and selected the assembly with the greatest number of BUSCO reference genes present for further analysis. To remove contaminants from the final assembly, we generated blob plots using Blobtools v. 1. 0 (Laetsch and Blaxter, 2017). To accomplish this, we BLASTed all contigs against the NCBI nucleotide (nt) database using megablast  (Morgulis et al. , 2008)  and against Uniprot reference proteomes using diamond blastx  (Buchfink et al. , 2015). We then removed any contigs that were identified as coming from plants, fungi, viruses, or bacteria, except for those matching Wolbachia.
Genome annotation We used RepeatModeler v. 1. 0. 10, which uses  RECON (Bao and Eddy, 2002) , RepeatScout  (Price et al. , 2005), and Tandem Repeat Finder (Benson, 1999) , to construct a custom repeat library for T. rathkei. Because we were unable to run RepeatModeler successfully using the full assembly, we ran RepeatModeler on a random subset 40% of the contigs; this should still successfully identify most repetitive elements in the genome as long as all repeat families are well represented in the subset. We then masked the assembly using RepeatMasker 4. 0. 7, and used the data generated by RepeatMasker on divergence between each individual repeat and the consensus sequence for that family to examine the history of transposable element activity in T. rathkei (Tarailo-Graovac and Chen, 2009).
We annotated coding sequences using the MAKER pipeline  (Cantarel et al. , 2008). We initially ran MAKER v2. 31. 9 using assembled transcript sequences (est2genome=1) from previously available data from one wild-caught male and one wild-caught female T. rathkei from the same population  (Becking et al. , 2017). This transcriptome was generated by assembling the male and female samples separately, each with two assemblers, Trinity v2. 8. 4  (Grabherr et al. , 2011) and TransLiG v1. 3 (Liu et al. , 2019) , and then merging and filtering the transcriptome assemblies with EvidentialGene v4  (Gilbert, 2019). This initial annotation also incorporated protein alignments against Uniprot-Swissprot (version March 2020),and the resulting output was used to train SNAP  (Korf, 2004). To train AUGUSTUS  (Stanke et al.
Development of sex-linked PCR markers We used multiple approaches to develop male-specific, putatively Y-linked PCR markers. Initial attempts to perform a hybrid Illumina-PacBio genomic assembly with only male samples and then identify contigs with zero coverage in females were unsuccessful. We therefore developed a complementary approach by looking for male-specific k-mers using just the raw Illumina sequencing reads. We chose a value of k=21 because it should be large enough that most kmers will not occur more than once in the genome sequence, yet small enough to minimize the impact of sequencing errors  (Vurture et al. , 2017). We used kmc v. 3. 1. 0 (Kokot et al. , 2017)  to count all the canonical 21-mers in each of the Illumina sequencing datasets (in other words, each 21-mer and its reverse complement were considered to be the same k-mer during counting).
We then searched for k-mers that occurred at least 8 times in the Mpool Illumina sequencing dataset and a total of at least 3 times combined across the lower coverage M2, M5, M6, and wildM samples, but which were completely absent from all female samples. We then extracted all Illumina sequence reads containing these candidate male-specific k-mers using mirabait v. 4. 0. 2  (Chevreux et al. , 1999) , and assembled them using Spades v. 3. 11. 1 (Bankevich et al. , 2012). We also performed a reciprocal analysis looking for female-specific k-mers (which are not expected in an XX/XY system), searching for k-mers that occurred at least 8 times in the Fpool Illumina sequencing dataset, a total of at least 3 times combined across the F3, F4, and wildF samples, but which were completely absent from all male samples.