dataesr/scientific-paragraphs-categorization huggingface dataset:
com/Preregistrations/gdispersal. html Requested revisions We revised our preregistration and associated files at http://corinalogan. com/Preregistrations/gdispersal. html , and we responded to your comments below. Note that the version-tracked version of this preregistration is in rmarkdown at GitHub: https://github. com/corinalogan/grackles/blob/master/Files/Preregistrations/gdis persal. Rmd. • Dependent variable: average relatedness between all pairs of individuals WITHIN ONE SEX Full cited reference: Coulon, A. , J. -F. Cosson, N. Morellet, J. -M. Angibault, B. Cargnelutti, M. Galan, S. Aulagnier, and A. J. M. Hewison. 2006. Dispersal is not female-biased in a resource-defence mating ungulate, the European roe deer. Proceedings of the Royal Society of London B 273:341-348. Reviewed by Sylvine Durand, 2019-08-28 11:22 Download the review (PDF file)Author's reply:Dear Dr.
Vegetative growth would result in large genets, while frequent colonization by sexual spores would result in many small genets  (Dahlberg & Stenlid, 1994). Practically, genet size is measured by genotyping sporocarps. Many genetically identical sporocarps scattered across a habitat characterize large genets; distances among genetically identical sporocarps define the size of the genet. Genets of other ECM species range from centimeters to hundreds of meters  (Gryta et al. , 1997; Redecker et al. , 2001; Sawyer et al. , 2001; Bergemann et al. , 2002; Dunham et al. , 2003; Lian et al. , 2006; Rubini et al. , 2011) , and like these fungi, A. phalloides may propagate using either fragments or spores, or both. By physically mapping and genotyping sporocarps from multiple populations in European and American forests, and from the same Californian sites over time, we generated data on genet size to ask 1) does the death cap establish across landscapes using vegetative hyphae or sexual spores, 2) can the same individuals persist in a population over time, and 3) does genet size differ between native and invasive ranges?.
Amplification and visualization of AFLP markers The AFLP protocol is a DNA fingerprinting technique that does not require prior DNA sequence information as it is based on a selective polymerase chain reaction (PCR) amplification of adaptor-ligated restriction fragments formed from digested genomic DNA  (Vos et al. , 1995). Mutations at restrictions sites result in the presence or absence of fragments of different sizes and enable individuals to be distinguished from one another. AFLP data were generated for sporocarps collected from 2004-2007, before genome data of A. phalloides were available, and at the time AFLP was a genetic fingerprinting method of choice. We adapted the AFLP protocol of the Applied Biosystems Microbial Fingerprinting Kit (Applied Biosystems [ABI], Foster City, CA, USA) for use with our samples.
Amplification products were denatured in formamide and visualized on a 3730 ABI capillary sequencer. Data were analyzed using GeneMapper version 4. 0 (ABI). Potential markers were scored using GeneMapper, and then inspected by eye. Clear presence/absence patterns were recorded as 1 or 0, respectively. A total of 102 loci across 221 specimens were recovered and converted to a GenAlEx v6. 5 format for downstream analyses  (Peakall & Smouse, 2012).
Mean sequencing depth of each sample ranged from 10. 56 to 150. 86 (Supporting Information Table  S1b ). Sequence data were filtered using Trim Galore! (v0. 4. 5) (https://github. com/FelixKrueger/TrimGalore). Adapter trimming was set to the highest stringency such that even a single nucleotide of overlap with the adapter sequence was trimmed from a given read. After trimming, reads reduced to 100 bp or shorter, and those with quality scores less than 30 were discarded. A sporocarp from Dunas de Mira, São Jacinto, Portugal (specimen number 10511) was also sequenced on four SMRTcells on a PacBio RS II Sequel platform, also at the University of Wisconsin-Madison Biotechnology Center (Supporting Information Table  S1b ). This resulted in raw coverage of 47x with N50 read length of 6,310 bp. After troubleshooting of genome assembly pipelines (summarized in Supporting Information Table  S2 ), the final assembly was performed using an in-house hybrid approach.
First, Illumina data were subjected to a second round of filtering using Trimmomatic v0. 35  (Bolger et al. , 2014)  with the following parameters: ILLUMINACLIP:TruSeq3-PE-2. fa:2:30:10 CROP:245 LEADING:30 TRAILING:30 SLIDINGWINDOW:4:25 MINLEN:100. PacBio data were filtered to remove sequences shorter than 500 bp and error corrected with the Illumina data using FMLRC  (Wang et al. , 2018)  with default settings. Error-corrected PacBio reads were then used to simulate 20x coverage 3 kb insert size libraries with wgsim (https://github. com/lh3/wgsim) and parameter setting as follows: -e 0. 0 -d 3000 -s 500 -1 100 -2 100 -r 0. 0 -R 0. 0 -S 123 -N 5000000. Illumina data and simulated long-range libraries were then assembled using AllpathsLG v52400  (Gnerre et al. , 2011) , setting HAPLOIDIFY=True.
The resulting assemblies were subjected to further scaffolding using error-corrected PacBio data with the software LINKS v. 1. 8. 5  (Warren et al. , 2015 (Warren et al. , ) with -d 2000,5000,10000,15000,20000 -t 20,20,5 ,5000,10000,15000,20000 -t 20,20,5  and a kmer value of 29. Scaffolds were extended and gap-filled using PBJelly v. 15. 8. 24 and finally polished using Pilon v. 1. 2 (Walker et al. , 2014). Polished assemblies were evaluated with QUAST  (Gurevich et al. , 2013)  and BUSCO v. 2 with the Basidiomycota database v. 9  (Simão et al. , 2015). After genomes were assembled, single nucleotide polymorphisms (SNPs) and indels were identified using the Genome Analysis Toolkit (GATK) software v3. 8-0-ge9d806836  (McKenna et al. , 2010) , following GATK best practices (Broad Institute 2019).
Illumina reads from each of the 86 Illumina genome libraries were first mapped to the hybrid assembly using bwa-0. 7. 9 (Li. CC-BY-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted October 9, 2019. ; https://doi. org/10. 1101/799254 doi: bioRxiv preprint 10 2013) with the following parameters: mem -M -t 8 -v 2. Mapping rates for A. phalloides specimens ranged from 20. 0% -95. 3%, with a median mapping percentage of 86. 1% (only older specimens mapped at less than 50%). The mapping rate of 10511's Illumina reads to the 10511 hybrid assembly was 93. 8%. Duplicate reads were marked, and the GATK program Haplotypecaller was used to call variants simultaneously on all samples, using MODE=DISCOVERY and type=GVCF.
This procedure resulted in 212,119 indels and 1,580,133 SNPs. To eliminate any variants that may have been called as a result of sequencing errors, the raw VCF file was filtered using VCFtools-0. 1. 14 (Danecek et al. , 2011). We took a conservative approach: loci with quality scores greater than 30 (--minQ 30); with sequencing depths greater than the approximate mean depth across all loci and individuals (60X, or --min-meanDP 60); with a minor allele frequency (MAF) greater than 0. 006 (1/2N, where N=86 diploid individuals); and with no more than 50% missing data (--max-missing 0. 5), were kept in our dataset. To test how different filtering criteria affected the ability to distinguish genotypes, the raw VCF file was subjected to a variety of alternative filtering parameters (Supporting Information Table  S1b , Fig.
Intrapopulation and interpopulation patterns of polymorphism and divergence Statistical analyses were performed using R v3. 4. 4 and all scripts are available at https://github. com/jacobgolan/genets. To calculate the genetic relationships among sporocarps of both the AFLP and SNP datasets, we quantified the number of AFLP or SNP differences for every pair of sporocarps within a given population, and any sporocarp pair with a difference of zero was considered as belonging to the same genet (Fig. 2 , Fig. 3 , Fig. 4 ). Missing data are rare (no AFLP data are missing, and only 0. 73% of SNP data are missing), but a SNP locus at which two samples differed by missing data was counted as "not different" so as to facilitate. CC-BY-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under The copyright holder for this preprint (which was not this version posted October 9, 2019. ; https://doi. org/10. 1101/799254 doi: bioRxiv preprint 11 comparisons among pairs of samples: each pairwise comparison using SNP data was scaled to m(m-x) -1 , where m is the total number of loci and x is the number of missing sites  (Kamvar et al. , 2014). To understand how many markers are required to fully resolve genetic relationships among AFLP and SNP genotypes, we randomly sampled 100 loci 1,000 times without replacement, and calculated the number of unique genotypes for each random sample (Supporting Information Methods S2, Fig. S1 ). Curves of these data were constructed by adapting the poppr command from the package poppr v2. 8. 3  (Kamvar et al. , 2014). To understand genetic relationships among sporocarps collected across space and time, we first took an ordination approach.
Analyses were performed using vegan 2. 5-5 on each population and then for geographic regions (e. g. , California) and subpopulation clusters identified from our PCoA analyses (Supporting Information Table  S3 ;  Oksanen et al. , 2018). To estimate the physical area of each population we first drew a polygon around the perimeter of a population (as defined by sporocarps at the population's edges) and then estimated the smallest four-sided parallelogram that would completely enclose all sporocarps using in-house R scripts and alphahull v2. 1 (Pateiro-López & Rodríguez-Casal, 2010). Finally, to infer population differentiation between adjacent locations sampled in the same year, and between the same location sampled over time (e. g. , relationships between Drake 2 and Drake 3, both sampled in 2004, 2014, and 2015), we calculated FST for every pair of populations and subpopulations with hierfstat v0.
AFLP and SNP data While the 221 sporocarps of the AFLP dataset resolve into 160 unique genotypes, a plot of number of loci randomly sampled versus number of genotypes (Supporting Information Fig. S1 ) suggests 103 AFLP markers do not fully resolve sporocarp identity  (Grünwald et al. , 2003). SNP data provided greater resolution of genotypes, and both conventional and stringently conservative filtering criteria resolved each sporocarp as a unique genotype (Supporting Information Fig. S1 ). The different datasets result in different interpretations, for example, SNPs delineate each sporocarp from population Drake 3 2004 as a unique individual whereas AFLP markers resolve only two genets from the same sporocarps (Fig. 2 , Fig. 3 ).
It is made available under The copyright holder for this preprint (which was not this version posted October 9, 2019. ; https://doi. org/10. 1101/799254 doi: bioRxiv preprint 13 In fact, the majority of genets in both the AFLP and SNP datasets consist of a single sporocarp (Table  1 ). Genets encompassing more than one sporocarp were found only within the AFLP dataset. The 221 sporocarps genotyped by AFLP resolve into 160 unique genotypes, 72. 40% represent unique genotypes and resolve into small genets consisting of either a single sporocarp, or two to three mushrooms (Fig. 3 , Supporting Information Fig. S2a ,b, Fig. S3 ). The longest distance between two identical sporocarps was calculated from Jake's Landing (7. 51 m), the longest in California was found in Heart's Desire 2 (5. 14 m), and the longest in Europe was found in Serbia (3.
The physical area of a population did not influence whether or not there was a significant Mantel correlation (linear regression of area versus the calculated Mantel statistic: slope near zero and P >0. 05 for both within and between populations). Even across years, closely related genotypes often cluster in space (Fig. 6 ). For example, individuals of Drake 2 collected in 2004, 2014 and 2015 cluster together and away from individuals of Drake 3 collected in the same years. Subpopulations within single populations persist through time, for example, the subpopulations identified from Drake 2 in 2014 were found again in 2015 (Fig. 6 ). The data suggest spores fall and germinate within centimeters of their parent sporocarps; even after a decade, Drake 2 and Drake 3 (which are less than 100 m apart) remain genetically distinct.
Calculations of FST  (Weir & Cockerham, 1984)  confirm populations sustain a relative degree of genetic differentiation across years  (Hartl & Clark 2007; Branco et al. , 2015; 2017). FST calculated using the 2004, 2014, and 2015 data of Drake 2 ranges from 0. 0054-0. 0138, and for Drake 3 ranges from 0. 0003-0. 0270. The FST statistic comparing the adjacent populations, Drake 2 and Drake 3, in 2004 is 0. 0376 (0. 021 with AFLP data), in 2014 is 0. 0523, and in 2015 is 0. 0398. FST comparing Drake 3 to either Drake 2 subpopulation (Cluster 1 or 2) reveals. CC-BY-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted October 9, 2019.
, 2001; Carriconde et al. , 2008). Genet sizes across the genus Amanita appear variable. The species A. franchetii reaches a maximum size of 4. 7m, but populations house many singleton genets as well; populations of A. manginiana are entirely composed of singleton genets, with no genotype recovered between consecutive years  (Sawyer et al. , 2003; Liang et al. , 2005). By contrast, A. conicoverrucosa, A. punctata, A. pyramidifera, and A. muscaria appear to form genets made up of multiple sporocarps and may reach tens of meters in size  (Sawyer et al. , 2001; 2003). However fingerprinting techniques with low resolution may bias inferences by binning unique genetic individuals together, as we discovered by comparing our AFLP and SNP datasets. CC-BY-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
Mapping At CESAC  (in 2002 and 2006)  and Drake 2-3 (in 2014 and 2015) transects were measured from an arbitrary center point within each population (at CESAC, a planted Cedrus libani) to each sporocarp. The transect angle with respect to north and its distance from the center point were recorded and data later converted to Cartesian coordinates. Populations from Portugal  (Vilarinho, Agraria, and Mira [collected in 2015] ) were mapped by measuring the distance between all pairs of sporocarps. Hand drawings of each population were used to arbitrarily choose a single sporocarp as the population center. Cartesian coordinates were then calculated for each sporocarp using the spatial distances between all pairs. Populations from Drake 1-4 (in 2004), Heart's Desire 1-3 (in 2004), Serbia (in 2007), Round Valley (in 2006), Jake's Landing (in 2006), and Rochester 1-3 (in 2007) were mapped using transect lengths measured from each of two poles placed within each population to each sporocarp.
The distance between the two poles was also measured, allowing the length of each transect to be considered as a radius in the mathematical equation of a circle. Using the transect lengths as two intersecting radii, equations for each of two circles were algebraically solved to obtain the Cartesian coordinates of each sporocarp. Using a rough sketch of each population, we identified which point was the correct physical location of any given sporocarp. After mapping, entire sporocarps were extracted from soil and stored individually in labelled bags. Each sporocarp was assigned a unique five digit specimen number (Table  S1a ,b) tied to an in-house database named AmanitaBASE. Within 24 hours of sampling, fresh sporocarps were stored using a variety of protocols including drying on a dehydrator, air drying at approximately 35ºC, flash freezing in liquid nitrogen and then lyophilizing, and/or cutting apart and placing in CTAB (Table  S1a.
The curve plateaus and variance is minimized at about 90 AFLP markers. Using VCFtools  (Danecek et al. , 2011)  we tested a variety of different VCF filters to gauge how each filter discriminated among unique genotypes. Loci with a minor allele frequency lower than 0. 006 ([2*N] -1 , where N = 86 diploid sporocarps), or 0. 49 (as an arbitrary and overly conservative filter), with a sequencing depth below 60 (the approximate mean depth across all loci and individuals), and with missing data thresholds per locus less than 0%, 50%, and 100%, were used in different combinations to filter the raw VCF file. Dotted red lines mark the maximum number of genotypes recovered with either AFLP or SNP data; dotted grey lines mark the total number of sporocarps used to generate either AFLP or SNP datasets.
Sequencing resulted in 103 AFLP bands from 221 A. phalloides genomes and 1. 2 raw Illumina reads (an average of 3. 6 M per sporocarp) from 86 A. phalloides genomes. The assembled Illumina-PacBio hybrid genome of specimen 10511 (Mira, São Jacinto, Portugal) resulted in a 35. 5 Mb assembly covering approximately 77% of the estimated 43 Mb genome. The assembly encompasses 605 scaffolds with an N50 of 320 kb, and gene space completeness is estimated at 94. 4%. The 86 full genomes mapped to the 10511 assembly provided 297,133 high quality SNPs. Sporocarps with identical genotypes were found exclusively in the AFLP dataset (Supporting Information Fig S2a,b, Fig. S3). The two most closely related SNP genotypes were collected from Dunas de Mira, São Jacinto, Portugal (specimens 10511 and 10512), and they differed by approximately 24,250 SNPs (scaling for missing data; Fig.
Acknowledgements We thank the  University of Wisconsin-Madison ,  Human Frontier Science Program  and  National Science Foundation  for funding (and in particular an  NSF   Graduate Research Fellowship   DGE-1747503  to  Jacob Golan ). Deep thanks to  Benjamin Becker ,  Lynne Boddy ,  Tom Bruns ,  Bryn Dentinger ,  Jeffrey Grass ,  Hervé Gryta ,  Thomas R. Horton ,  David Johnson ,  Todd Mitchell ,  Sam Morris  and his  Trigonometry class at Cary Academy ,  Sophie Manzi ,  Zachary Muscavitch , the  Mycologists' Association of Serbia (Branislav Uzelac ,  Goran Milošević  and  Krstajic Damjan ),  Karla Sartor ,  Rod Tulloss , and  Natalia Vargas-Estupiñan , for collections, advice, technical assistance, and support in the field. We collected at  Tomales Bay State Park  and  Point Reyes  under permits granted to  Tom Bruns ,  Sydney Glassman , and  Anne Pringle.
e. , with large volumes of un-segmented, non-prototypical and non-preselected data of wholly naturalistic behaviour. This is precisely the type of data that the new generation of affectoriented multimedia and human-machine/human-robot communication interfaces have to face in the real world. Major novelties are introduced for the AVEC 2019 with three separated Sub-challenges focusing on health and emotion analysis: (i) State-of-Mind Sub-challenge (SoMS), (ii) Detecting Depression with AI Sub-challenge (DDS), and (iii) Cross-cultural Emotion Sub-challenge (CES). In the following, we describe the novelties introduced in the Challenge and the guidelines for participating. The State-of-Mind Sub-challenge (SoMS) is a new task focusing on the continuous adaptation of human state-of-mind (SOM), which is pivotal for mental functioning and behaviour regulation  [25].
The DAIC data set contains new recordings of the same population with the virtual agent being, this time, wholly driven by AI, i. e. , without any human intervention. Those new recordings are used as a test partition for the DDS, and will help to understand how the absence of a human conducting the virtual agent impacts on automatic depression severity assessment. The Cross-cultural Emotion Sub-challenge (CES) is a large extension of the AVEC 2018 CES  [47] , where dimensions of emotion were inferred from audiovisual recordings collected "in-thewild", i. e. , with standard webcams and at home/work place. A cross-cultural setup was further exploited for inferring emotion: knowledge of German culture was leveraged to infer emotion on the Hungarian culture, using the SEWA corpus  [31]. This dataset now includes data collected from new participants with Chinese culture, which is used as a test set for the CES, whose aim is, therefore, to investigate how emotion knowledge of Western European cultures (German, Hungarian) can be transferred to the Chinese culture.
Whereas many other metrics of performance could be exploited for ranking the contributions, such as the Spearman's CC, or the coefficient of determination (r 2 ), we believe that the index of reproducibility CCC is the most suitable metric to use, as it is not biased by changes in scale and location, and elegantly includes information on both precision and accuracy in a single statistical measure  [36]. Moreover, its theoretical definition and properties are well rooted in the literature  [41] , and it can be easily exploited as a loss function for training neural networks  [77]. To be eligible to participate in the Challenge, every entry has to be accompanied by a paper submitted to the AVEC 2019 Data Challenge and Workshop, describing the results and the methods that created them. These papers undergo peer-review by the technical program committee.
Ulm State-of-Mind Corpus The Ulm state of mind database was collected to assess the association between personal story telling and current SOM, operationalised by affective state according to Russel's theory  [46, 52, 61]. Parts of this dataset have been released for the Interspeech 2018 Computational Paralinguistics (ComParE) challenge  [61]. Participants of the USoM corpus were instructed to first tell two negative personal narratives N N 1, 2 and subsequently two positive personal narratives PN 1, 2 , each for five minutes in front of a camera. They were also asked to rate their current affect (CA) on a 10point likert scale for the dimensions arousal and valence before and after telling each narrative, resulting in the following protocol: (t 0 ), CA 0 , N N 1 , (t 1 ), CA 1 , N N 2 , (t 2 ), CA 2 , PN 1 , (t 3 ), CA 3 , PN 2 , and (t 4 ), CA 4.
For the purpose of the Challenge, the USoM dataset was partitioned into training, development, and test sets while preserving the overall speaker diversity -in terms of age, gender distribution, and core affect evaluations -within the partitions. Table  1  shows the number of subjects and duration for each partition. As the interest of the SoMS is on the change in mood, rather than just its static observation, the initial self-reports made before the storytelling are included in the data package given to participants for all partitions, including the test set. Exploiting such contextual information in an automatic system predicting the level of mood is a realistic scenario in the real-world, because a therapist would always ask a person's baseline emotion at the start of a session. It is thus essential to provide machine learning algorithms with the same prior information as a therapist would have.