dataesr/scientific-paragraphs-categorization huggingface dataset:
The x coordinate estimates the minimal number of cancer types k * for which a gene should have been predicted as a cancer-type-specific core fitness gene in order to be classified as a pan-cancer core fitness gene. All box-and-whisker plots show the interquartile ranges and 95th percentiles, with centres indicating medians. Extended Data Fig. 4  | Characterization of ADaM pan-cancer core fitness genes. a, The 553 pan-cancer core fitness genes in reference essential gene sets are shown  9, 10. Respective recall and enrichment significance P values from a hypergeometric test when considering the whole set of genes targeted in the CRISPR-Cas9 screen as the background population (n = 17,995). The 132 newly identified core fitness genes fall outside of these reference gene sets. b, c, Pathways (b) and gene families (c) enriched in the 132 newly identified pan-cancer core fitness genes (Benjamini-Hochberg-adjusted hypergeometric test P < 0.
sgRNAs targeting essential (sgEss) and non-essential (sgNon) genes were used as controls. Bars represent mean co-competition score; lines represent maximum and minimum values; individual data points overlaid. b, Selective WRN dependency in MSI versus MSS cell lines was confirmed using clonogenic assays in four cancer types (images are representative of two independent experiments). c, A reduction in WRN protein levels with all WRN sgRNAs was confirmed by western blot (images are representative of two independent experiments). d, An association between WRN dependency and MSI status was confirmed by mining data from an independent study that used RNA interference, project DRIVE 12 (Student's t-test, P = 0. 004; n = 214). Each circle represents the WRN RNA-interference dependency score in a cancer cell line.
Box-and-whisker plots represent median and 1. 5× interquartile range. e, siRNA depletion of WRN inhibited proliferation of HCT116 cells. Data are mean ± s. d. of three independent experiments. The P value was determined using a non-parametric Student's t-test. f, siRNA-mediated depletion of WRN was verified by western blot (images are representative of two independent experiments). For western blot source data, see Supplementary Fig. 1.
a, Differentially expressed genes in cell lines that are dependent on GPX4 (left) (n = 113, non-dependent versus dependent, moderated t-statistic FDR estimates). Epithelial-mesenchymal transition is the top differentially enriched cancer hallmark gene signature in GPX4-dependent cell lines (right). P values from single-sample gene set enrichment analyses were obtained by randomly permuting gene signatures 10,000 times and adjusted for multiple testing using the Benjamini-Hochberg FDR correction. b, Functional classification of priority targets in each tractability group using the PANTHER database. For clarity, kinases (a subset of transferases) and transcription factors are shown separately. Protein classes are indicated by colour. Statistical enrichment was calculated using a systematic hypergeometric test across protein families, following correction for multiple testing with the Benjamini-Hochberg method.
Acknowledgements We thank  D. Adams ,  G. Vassiliou  and  L. Parts  for comments on the manuscript, members of the M. J. G. laboratory and Sanger Institute facilities ( Wellcome Trust  grant  206194 ). Work was funded by  Open Targets  ( OTAR015 ) to M. J. G. , K. Y. and J. S. -R. The K. Y. laboratory is supported by  Wellcome Trust  ( 206194 ). The M. J. G. laboratory is supported by  SU2C  ( SU2C-AACR-DT1213 ) and  Wellcome Trust  ( 102696  and  206194 ). Support was also received from  AIRC 20697  (A. B. ) and 18532 (L. T. ); 5x1000 grant  21091  (A. B. and L. T. );  ERC Consolidator Grant   724748 -BEAT  (A. B. ); FPRC-ONLUS, 5x1000 Ministero della Salute 2011 and 2014 (L. T. ); and Transcan, TACTIC (L. T. ).
Wealth and wealth inequality Wealth data were collected in 2006-2007 and 2013. Here, we only included wealth data collected prior to a rare catastrophic flood in February 2014 that destroyed crops and household goods in the vast majority of Tsimane communities  (Trumble et al. , 2018). Figure  1 -figure supplement 1 summarizes how many individuals were included in the sample, out of all individuals ever sampled by the THLHP. Household wealth was assessed through an inventory of commonly owned items including traditional goods, that is, items manufactured from local organic materials (e. g. , canoes, bows and arrows), market goods, that is, industrially produced items obtained through trade or purchase (e. g. , bicycles, motorbikes), and livestock (e. g. , pigs, cows), which were subsequently converted into their local market value in Bolivianos and summed (Figure  1 ).
A household's cooperation network was measured as the number of people from different households who helped in that household's fields in a given year. Cortisol was measured in first-morning urine using enzyme-linked immunosorbent assays and corrected for specific gravity (see von  Rueden et al. , 2014 for details). BMI Z-scores were calculated by GAMLSS using Tsimane-specific growth curves  (Blackwell et al. , 2016b)  (R package at: https:// github. com/ adblackwell/ localgrowth  (Blackwell, 2021 ) copy archived at swh:1:rev:81ce799bdc6da90d48e5ad8afd6ad0f3b19494d2) as well as the total distribution of Tsimane adult BMIs, representing deviations from the local population average for a given age and sex. Diastolic and systolic blood pressures were measured by THLHP physicians using an aneroid sphygmomanometer.
β 0 is the intercept, all other βs are slopes, us are random intercepts, and e is the residual error (not available for Bernoulli responses). Variance inflation factors (VIFs) indicated virtually no collinearity among predictors (all VIFs < 3). In order to test whether potential wealth-health associations were mediated by psychosocial stress, we reran all health models (blood pressure, self-rated health, total morbidity, infections, respiratory and gastrointestinal illness) with pertinent psychosocial variables as covariates and used the mediation function in the sjstats package  (Lüdecke, 2021)  to estimate direct and indirect effect. In addition, we also ran a series of exploratory analyses in which we added interaction terms. We used Bayesian multilevel models fit with the brms package v.
2. 13. 5  (Bürkner, 2017)  in R 4. 0. 2 for all analyses. All models used regularizing priors (fixed effects: normal, mean = 0, SD = 1; random effects: half-Cauchy, location = 0, scale = 2), which imposes conservatism on parameter estimates and reduces the risk of inferential errors  (Gelman et al. , 2013; McElreath, 2020). All models converged well as assessed by inspecting trace plots and standard diagnostics (all Rhat < 1. 01). All data and R code are available at https:// doi. org/ 10. 5281/ zenodo. 4567498 with any updates at https:// github. com/ adblackwell/ wealthinequality  (Jaeggi et al. , 2021 , copy archived at swh:1:rev:da16ac6b20732fe1939478450d81ac32fdcce202).
Acknowledgements We thank the  Tsimane  for their generous participation and years of collaboration, and THLHP personnel for their herculean efforts and dedication in data collection. We thank  Erik Ringen ,  Jordan Martin , participants in the Club EvMed series, as well as  Milagros Ruiz ,  Prabhat Jha , and one anonymous reviewer for comments and suggestions that greatly improved this article. The Tsimane Health and Life History Project has been funded by  NSF  ( BCS0136274 ,  BCS0422690 ) and  NIH/NIA  ( RF1AG054442 ,  R01AG024119 ,  R56AG024119 ). AJ was supported by the  Swiss NSF  ( PBZHP3-133443 ) and by the  SAGE Center for the Study of the Mind. JS acknowledges Institute for Advanced Study Toulouse  funding from the  French National Research Agency (ANR)  under grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ).
Data availability All data and R code are available at https:// doi. org/ 10. 5281/ zenodo. 4567498 with any updates at https:// github. com/ adblackwell/ wealthinequality (copy archived at https:// archive. softwareheritage. org/ swh: 1: rev: da16 ac6b 2073 2fe1 9394 7845 0d81 ac32 fdcce202). The following dataset was generated:.
Callithrix species distributions are based on 2012 IUCN Red List Spatial Data (http:// www. iucnr edlist. org/ techn ical-docum ents/ spati al-data) and the accompanying map was produced with the R 4. 1. 0 scripting language  18. The photographs on the right hand side show the phenotype of individual BJT022 as matching that of C. aurita, as well as the phenotypes of C. jacchus and C. penicillata for reference. mitogenomics work is the use of relatively time-consuming molecular techniques and lag time for sequencing data generation from several weeks to months (pers. obs. , Malukiewicz). As a subsequent step in developing genomic resources for C. aurita, we present here a rapid, PCR-free, genomic skimming technique for mitogenomic reconstruction with the hand-held, long-read Oxford Nanopore Technologies (ONT) minION sequencer.
Complete mitogenome reconstruction. After direct ONT sequencing of genomic DNA (gDNA) from individual BJT022, we successfully determined a full mitogenome using the genomic skimming/minION approach. ONT sequence reads were first filtered for quality and to remove sequencing adapters. Then ONT mtDNA reads were identified with the BLAST-based mtBlaster script  5  , with the C. aurita (GenBank Accession MN787075) mitogenome as the reference genome. Table  1  shows overall characteristics for gDNA ONT reads as well as ONT mtDNA reads. The final length of the reconstructed ONT-derived mitogenome was 16,181 base pairs (bps) with an average coverage of 9x. Annotation of the ONT-derived mitogenome with the CHLOROBOX web server  19  showed that the mitogenome contained 37 mitochondrial genes (13 protein-coding genes, 22 tRNAs, and two rRNAs), as shown in Supplementary Figure  1.
Discussion Feasibility of genomic skimming on the ONT minION sequencer. The term 'genomic skimming' was first coined by in 2012 by Straub et al. (2012)   20  as a way to utilize shallow sequencing of gDNA to obtain relatively deeper coverage of high-copy portions of the genome, including mitogenomes. In combining genomic skimming with ONT long-read sequencing, we successfully reconstructed a complete marmoset mitogenome without the need for prior PCR enrichment, using standard molecular biology equipment, and a compact portable sequencer that connects to a laptop computer. Preparation of genetic material for ONT sequencing in this study took less than a full day, and sequencing reads were available within 48 hours. Although the coverage of our reconstructed ONT mitogenome was low-medium (9x) and one of the largest sources of error for the ONT reads were missing reads in long homopolymer runs, the ONT data showed a high degree of concordance with gold standard mtDNA Sanger sequencing reads for the same individual.
Hence, this work along with a number of previous studies (e. g. ,  5, 21  ), highlights ONT-based genomic skimming as holding great potential for enhancing mitogenomic and diversity studies of data-deficient and/or non-model organisms. A major challenge in ONT sequencing is the relatively high sequencing error (5%-15%), but the application of computational 'polishing' significantly reduces errors of raw ONT data (e. g. 22 ). Another challenge with ONT methodologies is the large amount of input DNA needed for sequencing relative to other types of methods, particularly PCR and Sanger sequencing. Multiplexing samples onto the same flow cell is one way to reduce the required amount of per sample DNA, and currently ONT chemistry allows for up to 24 individual gDNA samples to be multiplex per flow cell.
Additionally, surveys should be conducted of the standing genetic variation levels of the captive C. aurita population. These data are crucial for understanding anthropogenic impacts on the species as well for making appropriate decisions for species conservation. The application of genomic skimming based on portable ONT long-read technology can be applied to address several of these knowledge gaps for C. aurita. First, with large-scale sampling of wild and captive C. aurita, genetic diversity estimates, demographic history, and other evolutionary analyses can be calculated relatively easily from mitogenomic data. Given the relatively fast turnaround time to obtain sequencing data from the minION, such data could be quickly obtained for a primate as highly endangered as C. aurita, without weeks or months long wait times for sequencing data.
Conclusion. Brazilian legal instruments that protect C. aurita consider hybridization a major threat to the survival of this species  8, 12. In this report, we have uncovered the first known case of cryptic hybridization between C. aurita and C. penicillata, which may represent a larger trend of genetic introgression from exotic into native marmosets in southeastern Brazil. Our findings are based on the combination of two recent innovations in the field of genomics, that of genomic skimming and portable long-read sequencing on the ONT minION. Given that C. aurita is still very deficient for genetic data, our approach provides a substantial advance in making more genomic data available for one of the world's most endangered primates. Genomic skimming based on ONT sequencing can be integrated easily with phenotypic and other genetic data to quickly make new information accessible on species biodiversity and hybridization.
Methods Sampling A skin biopsy was sampled from a single adult male marmoset with a Callithrix aurita phenotype at the Guarulhos Municipal Zoo, Guarulhos, Sao Paulo, Brazil following the procedure described in Malukiewicz et al. (2017, 2021)  7, 16. Tissues were collected under the approval of the ASU Institutional Animal Care and Use Committee Animals (ASU IACUC, protocols #11-1150R, 15-144R) and Brazilian Environmental Ministry (SISBIO protocols #47964-2 and #28075-2). Biological tissue sampling complied with all institutional, national, and international guidelines. The sample is registered in the Brazilian SISGEN database under number A18C1CE. This study is reported in accordance to ARRIVE guidelines (https:// arriv eguid elines. org/ resou rces/ quest ionna ire).
Then another 200 ul of priming mix were loaded into the flow cell priming port. Sequencing libraries for both runs were prepared by adding 35 ul RBF buffer, 25. 5 ul of Library Loading Beads from the Oxford Nanopore Libary Loading Bead kit (EXP-LLB001), and 2. 5 ul NFW to 12 ul of the presequencing mix. A volume of 75 ul of library was loaded drop by drop onto the SpotON sample port and capillary action pulled the library into the flow cell. MinKNOW software 2. 2 was used to set up two 48 hours MinION sequencing runs on a MacBook Pro laptop computer and read base calling of resulting reads in FAST5 format and conversion to FASTQ format was done with Guppy 2. 1. 2. Guppy also generated a series of quality control files which were used to assess the performance of both sequencing runs. Both MinKNOW and Guppy are only available to ONT customers via their community site (https:// commu nity.
ONT sequencing data analysis and mitogenome reconstruction and annotation To first filter FASTQ reads output by Guppy, we first used PORECHOP 0. 2. 4  32  to remove sequencing adapters and with NANOFILT 2. 7. 1  33  remove low quality reads (Q<7). Then we identified minION reads representing the mitogenome with the BLAST-based mtBlaster script  5  , using the mitogenome of C. aurita (GenBank Accession MN787075) as the reference genome. MtBlaster output was assembled de novo into a full mitogenome with the FLYE 2. 8. 2  34, 35  assembler and polisher for single molecule sequencing reads such as those produced by the min-ION. The command line script for the above steps is available at https:// github. com/ Calli thrix-omics/ aur_ nano. The assembled minION mitogenome was aligned to the Sanger sequencing results with the MAFFT 7.
Mitogenome phylogenetic and diversity analyses To determine the lineage of the reconstructed mitogenome, we combined this sequence with other Callithrix mitogenomes  7  , and used other New World primate GenBank sequences as outgroups. Accession numbers of all sequences used are listed in Table  S1. We kept mitochondrial genomes in their entirety, but trimmed part of tRNA-Phe, 12s rRNA and the control region to accommodate the length of all utilized sequences. All mitogenomes were aligned using the online MAAFT server with default settings. A maximum likelihood phylogeny of all sequences was generated with the IQTREE 1. 6. 12 37 online server http:// iqtree. cibiv. univie. ac. at with default setting which equated to the command "iqtree -s input_alignment. fasta -m TEST -bb 1000 -alrt 1000.
Acknowledgements This work was supported by a  Brazilian CNPq Jovens Talentos Postdoctoral Fellowship  ( 302044/2014-0 ), a  Brazilian CNPq DCR  grant ( 300264/2018-6 ), an  American Society of Primatologists Conservation Small Grant , a  Marie-Curie Individual Fellowship  ( AMD-793641-4 ) and an  International Primatological Society Research Grant  for JM. This work was also supported by an  Arizona State University SOLS/OKED Research Investment  grant to RAC, AV,  MAW , and JM, as well as an  Arizona State University   RTI Postdoctoral Grant  to JM. Additional internal funding from the  German Primate Center  to LW and JM also supported this work. The funding agencies had no roles in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank the  Golden Lion Tamarin Association, CEMAFAUNA , the  Guarulhos Municipal Zoo , the  Beagle Lab at UFV , and many biologists, field technicians, veterinarians, and other individuals that made this research possible.
One way to compute this function is to statistically approximate it: run the software from Section 7 many times, inspect the multiplication counter built into the software, and take the average over many experiments. A more efficient way to compute the same function with the same accuracy is with a simulator that skips the multiplications but still counts how many there are. Our simulator, despite being written in Python, is about 50 times faster than the software from Section 7. However, using a statistical approximation raises concerns about the impact of statistical variations. So, instead of using the software or the simulator, we directly compute the average cost of the first AB, the average cost of the second AB, etc. , stopping when the probability of needing any further AB is below 10 -9.
Constant-time software for the action We have built a self-contained high-performance software package, high-ctidh, that includes implementations of all of the operations needed by CSIDH users for whichever parameter set is selected: constant-time key generation, constant-time computation of the CSIDH action, and validation of (claimed) public keys. The package uses the CTIDH key space and CTIDH algorithms to set new cycle-count records for constant-time CSIDH. The high-ctidh source code is in C, with assembly language for field arithmetic. Beyond the performance benefits, using low-level languages is helpful for ensuring constanttime behavior, as explained below. Measuring the performance of a full C implementation also resolves the concerns raised by using multiplications as a predictor of performance, such as concerns that some subroutines could be difficult to handle in constant time and that improved multiplication counts could be outweighed by overhead.
Processor selection and field arithmetic The original CSIDH paper reported clock cycles for variable-time CSIDH-512 software on an Intel Skylake CPU core. Skylake is also the most common CPU choice in followup papers on CSIDH software speed. We similarly focus on Skylake to maximize comparability. The original csidh-20180826 software from  [10]  included a small assembly-language library for Intel chips (Broadwell and newer) to perform arithmetic modulo the CSIDH-512 prime. The same library has been copied, with minor tweaks and generalizations to other primes, into various subsequent software packages, including high-ctidh. Code above the field-arithmetic level, decomposing isogenies into multiplications etc. , are written in C, so porting the software to another CPU is mainly a matter of writing an efficient Montgomery multiplier for that CPU.
The software permutes the b ≤ B batches in the AB to use primes b-1 , b-3 , b-4 ,. , 1 , b-2 , b in that order. Each AB selects one prime from each batch in the block and tries to compute an isogeny of total degree D, the product of the selected primes; D = r in Algorithm 4. Each point is multiplied by 4 and then by all primes outside D immediately after the point is generated by Elligator, so that the order of the point divides D. There are two types of primes outside D (compare Steps 2 and 3 of Algorithm 4): • The batches in the AB are public. Primes outside these batches are publicly outside D. • Primes that are inside the batches in the AB, but that are not the secretly selected prime per batch, are secretly outside D. For scalar multiplication by a product of secret primes,  [5]  uses a Montgomery ladder, with the number of ladder steps determined by the maximum possible product.
Automated constant-time verification We designed and analyzed every step of the CTIDH algorithm to be constant time, leaking nothing about the input through timing; this is the basis for our claim that the algorithm is in fact constant time. We also designed and reviewed every new line of code in high-ctidh to be constant time, and reviewed every line of reused code for the same property; this is the basis for our claim that the software is in fact constant time. These analyses are complete-but, as in most papers on constant-time algorithms, are entirely done by hand, raising the question of what protections there are against human error. For extra assurance, we designated an internal auditor to use automated tools to verify the constant-time claims. This subsection is an audit report to support external auditing.
An automated test using valgrind. There is a standard tool, valgrind  [21] , that runs a specified binary, watching each instruction for memory errors-in particular, branches and array indices derived from undefined data. If secret data in cryptographic software is marked as undefined then simply running valgrind will automatically check whether there is any data flow from secrets to branches and array indices; see, e. g. ,  [17]. See also  [16]  for a survey of related tools. Because valgrind works at the binary level, this analysis includes any optimizations that might have been introduced by the compiler. A compiler change could generate a different binary with timing leaks, but valgrind is fast enough to be systematically run on all compiled cryptographic software before the software is deployed.
The auditor wrote a simple checkct program using high-ctidh to perform a full CSIDH key exchange; this program is included in the high-ctidh package. For example, running valgrind. /checkct512default takes under 30 seconds on a 3GHz Skylake core, where checkct512default performs a full CSIDH-512 key exchange. The underlying randombytes function marks all of its output as undefined, so valgrind is checking for any possible data flow from randomness to branches or to array indices. For each size, valgrind completes successfully, indicating that there is no such data flow. Limitations of the automated test, and steps to address the limitations. The following paragraphs ask, from the auditor's perspective, what could have been missed by this automated test-for example, the auditor asks what would happen if private keys were actually generated by OpenSSL's RAND_bytes rather than randombytes.