dataesr/scientific-paragraphs-categorization huggingface dataset:
6% of orthologs with AD: paired t-test, t = -1. 01, p = 0. 33), filtering of false positives led to more frequent AD in NSF with respect to S. cerevisiae than MD (2. 8% of orthologs with MD and 4. 9% of orthologs with AD: paired t-test, t (20) = 2. 61, p = 0. 02). One reason for the higher rate of AD compared to MD may be the higher diversity of fungal genomes due to their diverse lifestyles and specific adaptations to natural or laboratory ecosystems  (Naranjo-Ortiz and Gabaldón, 2019). Finally, we investigated the position of the true MD and AD events within the protein sequence. For domain events where the reference protein and the ortholog had at least two annotated domains, we determined whether the MD or AD was found at the N/C-termini or in the middle of the protein. For both NHP and NSF datasets (Figure  7 ), the MD and AD events were less frequent in the middle of the protein and N-terminal events were slightly more frequent than C-terminal events, in line with previous findings  (Dohmen et al.
Characterization of true domain events in primates In this section, we perform an initial evolutionary and functional investigation of the 808 domain events (411 MD and 397 AD) that were retained as true events in the primate dataset. Our initial error detection protocol identified AD and MD at the level of Pfam protein families. However, many Pfam families are related and can be classified hierarchically into clans. Pfam defines a clan as a collection of families that have arisen from a single evolutionary origin. Although domain families in the same clan are considered to be homologous, they are so evolutionarily diverse that the family members cannot be identified with a single HMM. Of the 808 domain events, 52 events (44 MD, 8 AD) involved two domains in the same Pfam clan and were excluded from the subsequent analyses.
The distribution of the remaining 756 events is provided in Supplementary Table  S2. It has been proposed that novel combinations of preexisting domains had a major role in the evolution of protein networks and more complex cellular activities  (Peisajovich et al. , 2010). In agreement with this, we found that the most frequent Pfam-A domains implicated in MD or AD events in the primate dataset (Supplementary Table  S3 ) are mainly involved in signaling or regulatory functions, including (i) the Kruppel associated box (KRAB) domain and zinc finger domains (PF14634, PF00096, PF02023) found in diverse transcription factors, (ii) the Ankyrin repeat and SAM domain commonly involved in protein-protein interactions, and (iii) the Src homology-3 frequently having a role in signaling pathways. Other domains are present in immune system proteins (PF13895, PF07679), or cytoskeletal-associated proteins (PF12796, PF00307, PF00373, PF15974), or are found in proteins involved in diverse processes (PF14604, PF01462, PF13202, PF00100).
The human protein is N-terminally truncated compared to orthologs and lacks a region containing the Rieske-type iron-sulfur cluster domain (PF00355). It has been suggested that the inactive protein leads to the fact that humans differ from other primates because they completely lack Neu5Gc on their cell surfaces  (Suntsova, Buzdin, 2020). Frontiers in Bioinformatics frontiersin. org 3. 9 Characterization of true domain events in fungi In this section, we investigate the 3406 domain events (1243 MD and 2163 AD) that were retained as true events in the fungi dataset. As for primates, we first checked for MD/AD in the same Pfam clan. Of the 3406 domain events, 567 (240 MD, 314 AD) involved two domains in the same Pfam clan and were excluded from the subsequent analyses. The distribution of the remaining 2,852 events is provided in Supplementary Table  S4.
Supplementary Table  S5  shows the top 10 most frequent Pfam-A domains implicated in MD or AD in the fungi dataset. Several domains are also present in the primate top 10 list, including the promiscuous domains (C2H2, RING, EF hand, Ankyrin repeats) found in diverse proteins. Other promiscuous domains include the WD, RWD, F-box-like domains. For some NSF MD, the domain is missing in all orthologs and thus most likely correspond to a domain gain in the reference protein rather than independent domain loss events in multiple NSF. These domains include homing endonucleases (PF05203, PF05204) encoded by mobile DNA elements, the N-terminal domain of methionyl-tRNA synthetase (MetRS), or the C-terminal domain of the mitochondrial ribosomal L27 protein. Other examples of true NSF MD events include the galactose metabolic process.
Most hemiascomycetes can grow on galactose as a sole carbon source, and in S. cerevisiae seven genes (the GAL genes) function exclusively in this pathway. Although orthologs of the GAL genes are present in many yeast genomes, they have been lost independently in several lineages  (Hittinger et al. , 2004). This specialty is also reflected at the protein domain level. The example shown in Figure  9A  concerns GAL10_YEAST (P04397), a bifunctional protein containing a GDP-mannose 4,6 dehydratase domain (PF16363) and an Aldose 1-epimerase domain (PF01263). The 17 identified NSF orthologs (no orthologs were found for Gonapodya prolifera, Nomascus cernae, V. culicis) all share the GDP-mannose 4,6 dehydratase domain. However, only Schizosaccharomyces pombe exhibits both domains, while 4 orthologs correspond to partially uncharacterized genome regions and 12 have confirmed losses of the Aldose 1-epimerase domain.
LDH1_ YEAST shares an alpha/beta hydrolase fold domain (PF00561) with Y. lipolytica and A. oligospora, but these two orthologs also contain a C-terminal dual specificity phosphatase, catalytic domain (PF00782). More complex MD/AD events are found in orthologs of the reference protein FOX2_YEAST (Q02207), a peroxisomal hydratasedehydrogenase-epimerase trifunctional protein, with 17 identified NSF orthologs (no orthologs were found for S. pombe and the two Microsporidia). The MaoC like domain (PF01575), with possible hydratase activity, is lost in the two Chytridiomycota, and the Basidiomycota W. ichthyophaga and Coprinopsis cinerea. In C. cinerea, the MaoC like domain is found in a separate downstream gene, suggesting a possible fusion/fission event.
After error filtering, true domain events are observed with a frequency of 0. 5% for NHP orthologs and 7. 7% for NSF orthologs. This difference highlights the different nature of the two datasets used in this work. The datasets were chosen to represent the main challenges for domain annotation pipelines. The first dataset includes closely related NHP species (sharing >89% protein sequence identity with human) with a common ancestor that appeared recently around 50-55 Mya  (Martin, 2012). True domain events are thus expected to occur at relatively low rates. Nevertheless, the annotation of NHP genomes poses significant challenges due to their large intron-rich and repeat-rich genomes and the variable genome sequence quality  (Wu D. D. et al. , 2022)  implying that errors are still common  (Meyer et al.
, 2020). In view of the clear physiological, cognitive and behavioral differences between NHP species, high quality data is crucial for comparative studies aimed at describing to what extent genetic differences drive phenotypic differences  (Rogers and Gibbs, 2014). In contrast, the NSF species of the second dataset generally have smaller genomes of high quality and few introns meaning that genes are theoretically easier to predict. However, the NSF have evolved over a longer time period (the fungal stem lineage emerged 1,000 Mya) and adapted to very diverse environments and lifestyles  (Berbee et al. , 2017). Again, error-free data are required to accurately identify different adaptive evolutionary trajectories. The fact-checking method used here confirms that errors or inconsistencies are widespread in the protein database entries corresponding to the two datasets.
For both NHP and NSF, the most frequent source of errors was heterogeneous domain annotations between orthologs. InterPro combines predictive models from several different databases, including Pfam into a single resource. The entire InterPro database is regularly reviewed for accuracy, and matches for sequences from UniprotKB are updated or removed where necessary  (Blum et al. , 2021). These efforts clearly improve the accuracy and coverage of protein domain databases, especially for the well-studied model species. Unfortunately, it also implies that domain annotations can be inconsistent between related organisms, thus hindering systematic large-scale comparative studies. Gene prediction failures were the second most frequent source of errors, particularly for NHP proteins. The relative difference between NHP and NSF is probably explained by the fact that the problems of gene prediction are less severe in the case of fungi that generally have less intron-rich genomes than primates.
Furthermore, in primates, some potential domain events also reflect the presence of alternative splicing isoforms, and the fact that the UniprotKB database may define different canonical isoforms for the orthologs of different species. Finally, 22% of domain events for NHP and 7% for NSF, were mapped to incomplete or uncharacterized genome sequences. In this case, the protocol could not distinguish between true and false events. Nevertheless, genome sequence quality should improve in the near future with the application of long read sequencing technologies, for example. Three important caveats should be kept in mind. First, we extracted ortholog information from the OrthoInspector database, which identifies pairwise orthologs based on protein sequence comparisons. This implies that orthologs with very different domain architectures may be missed by the algorithm, and thus our list of domain events may be incomplete.
In contrast, we assumed that the identified ortholog pairs are accurate, since OrthoInspector achieved a precision >99% in independent benchmarking tests  (Altenhoff et al. , 2020). Although the OrthoInspector database includes one-to-many and many-to-many orthologs, we restricted our analyses to orthologs with one-to-one relationships, thus excluding complex gene families that may have undergone copy number variations and subsequent sequence divergence through neo-or sub-functionalization. Second, our more relaxed definition of the 'domain content' of proteins considers only the set of domains present in a protein irrespective of domain order or domain duplications. This definition focuses on the important evolutionary events involving loss or gain of unique domains and has been used previously to study evolution of multidomain proteins  (Przytycka et al.
We trained a multinomial logistic regression classifier  30  , a probabilistic model that classifies data belonging to multiple classes (see "Methods"). This classifier received the firing rate of neurons in a sliding time window (100 ms) and learned, for each neuron, a coefficient that best predicts the class (i. e. , the generative parameter θ, B θ or θ × B θ ) of the stimulus. This decoder was first used to probe for representation of the stimuli's orientations θ in the population activity. For this purpose, the dataset of trials was separated for each variance, such that eight independent, B θ -specific, orientation decoders were learned, with optimal parametrization (Supplementary Fig. 6 ). These orientation decoders were able to retrieve the correct stimulus' θ well above the chance level (1 out of 12 orientations, max.
For the range of values of B θ considered here, the orientation envelope approximates a Gaussian distribution and B θ is thus a measure of the variance of the orientation content of the stimuli. All stimuli were generated using open-source Python code (see Additional information) and displayed using Psychopy  59. Monocular stimuli were projected with a ProPixx projector (VPixx Technologies Inc. ) onto an isoluminant screen (Da-Liteⓒ) covering 104°× 79°of visual angle. All stimuli were displayed for 300 ms, interleaved with a mean luminance screen (25 cd/m 2 ) shown for 150 ms between each trial. Trials were fully randomized, and each stimulus (a unique combination of θ × B θ × drift direction) was presented 15 times. Stimuli were shown at 100% contrast, meaning that as B θ increased, the amount of orientation energy at median orientation θ decreased, and conversely for off-median orientations (as illustrated in Fig.
35 to 7, in 200 even steps, and the input was not convolved with a receptive field. Statistics and reproducibility. All data were analyzed using custom Python code. Statistical analysis was performed using non-parametric tests. Wilcoxon signedrank test with discarding of zero-differences was used for paired samples and Mann-Whitney U-test with exact computation of the U distribution was used for independent samples. Due to the impracticality of using error bars when plotting time series, colored contours are used to represent standard deviation values (unless specified otherwise), with a solid line representing mean values. For boxplots, the box extends from the lower to upper quartile values, with a solid white line at the median value. The upper and lower whiskers extend to respectively Q1 -1.
Acknowledgements The authors would like to thank  Genevieve Cyr  for her technical assistance,  Bruno Oliveira Ferreira de Souza  and  Visou Ady  for experimental advice,  Louis Eparvier ,  Jean-Nicolas Jérémie  and  Salvatore Giancani  for their comments on the manuscript, and  Jonathan Vacher  for fruitful exchanges on the formalization of the generation of synthetic images and for his contributions to related analysis of other neurophysiological recordings. This work was supported by the  French government  under the France 2030 investment plan, as part of the  Initiative d'Excellence d'Aix-Marseille Université -A*MIDEX  ( AMX-21-RID-025 ), as well as by an  ANR  project  " AgileNeuRobot "  ANR-20-CE23-0021  to L. U. P, by a  CIHR  grant to C. C. ( PJT-148959 ) and an  École Doctorale  62 PhD grant to H.
This probability may be interpreted as the worth of any given genotype. Farmers' assessments were converted to pairwise comparisons, in which genotypes with a higher value (from 1 to 5) got a "win" when compared to another genotype with a lower value. To optimize model convergence, we did not consider ties in the pairwise comparisons. Data from each individual farmer were then aggregated by RIL families. We considered two main effects that could potentially drive farmers' choice. The first were farmers' individual differences, such as different gender and locations influencing how farmers appreciate the genotypes. The second factor were the individual characteristics of genotypes, reported by BLUPs (57) Details on the procedure are given in the SI Appendix. GS Model. Allele calls were filtered for failure rate (<20%) and heterozygosity (<50%) in both DP and EtNAM data.
R/rrBLUP (57) was used to conduct GS with the GBLUP model. SNPs were imputed with the mean method in the A. mat() function in rrBLUP. The set of SNPs overlapping among the DP and EtNAM was used to run the GS. The selection model was run according to different scenarios. To test whether farmers' knowledge could accurately predict wheat performance, we focused our GS on OA and GY. The DP was used to predict EtNAM performance by training the model on 100 random subsets of 4/5 of the DP data. All traits measured on the DP were used as predictors over EtNAM traits. In a second scenario, a cross validation was performed across locations predicting the EtNAM over EtNAM data, iteratively using data collected in one of the locations to predict performances in the remaining two locations. In a third scenario, EtNAM data were used to predict DP data by training the model on 100 random subsets of 4/5 of the EtNAM data, using all partitions of computed BLUPs.
QTL confidence intervals were defined with a peak drop of LOD=1 and Bayes credible intervals at 0. 9. Data, Materials, and Software Availability. Data management relied on R/ tidyverse  (65)  and R/rgdal  (66). Plotting made use of R/ggplot2 (67), R/raster  (68) , and R/patchwork  (69). All scripts are available on the GitHub page of the corresponding author at https://github. com/mdellh2o/EtNAM. GS. Raw data are available through Dryad at https://doi. org/10. 5061/dryad. w6m905qrv.
ACKNOWLEDGMENTS. We thank Dejene Kassahun Mengistu and Mulugeta Tilahun for their contribution in the development of the EtNAM and in the coordination of the fieldwork. We are grateful to Mercy Macharia Wairimu and Leonardo Caproni for the useful discussions. We thank the farmers who took part in the participatory evaluation of the EtNAM. In Adet: Balew Dessie, Tewachew.
Raw acoustic data were collected in East Antarctica from the RSV Aurora Australis during two surveys: the Krill Availability, Community Trophodynamics and AMISOR Surveys (KACTAS) and the Krill Acoustics and Oceanography Survey (KAOS) in the East Antarctic (centre coordinate 66. 5° S, 63° E). The KACTAS survey was conducted between 14th to 21st January and 2001, and the KAOS survey was conducted between 16 January and 1 February 2003. We examine the Antarctic krill (Euphausia superba) component of these surveys and provide scientific echosounder (EK500 and EK60) data collected at 38, 120 and 200 kHz, cold water (-1 °C) echosounder calibration parameters and accompanying krill length frequency distributions obtained from trawl data. We processed the acoustic data to apply calibration values and remove noise.
Background & Summary Management advice for fisheries targeting pelagic species often includes fishery-independent data gathered during active acoustic surveys with scientific echosounders  1. Acoustic surveys of Antarctic krill (Euphausia superba) are often carried out from research vessels in order to estimate krill biomass  2  , which is used in conjunction with a population model to set catch limits  3, 4. In 2001 the Krill Availability, Community Trophodynamics and AMISOR Surveys (KACTAS) voyage was conducted between 66°S and 67°S and 62°E to 64. 5°E between 14th January and 21st January 2001. In 2003 the Krill Acoustics and Oceanography Survey (KAOS) survey was also conducted in the same area of the East Antarctic between 16 January to 1 February 2003. Both surveys aimed to measure krill distribution and abundance in an on-shelf region of the East Antarctic, and to examine the acoustic survey results in the context of oceanographic conditions  5.
Methods The KACTAS and KAOS voyages, each consisting of 13 transects, the positions of which were defined formally pre survey, were conducted over the same area (Fig. 1 ). During KAOS two surveys were carried out, denoted as krill box 1 and krill box 2. The transects had a designed length of 90 km with a north-south orientation. Within each survey transects some transects were sampled multiple times: during the first survey 21 transects were sampled, and 18 transects in the second survey (Fig. 2 ). Sampling took place during day and night (Table  1 ). The EK500 and EK60 scientific echosounders ran continuously during the surveys, operating at 38, 120 and 200 kHz. All echosounder transducers were hull-mounted (depth 5. 5 m) split-beam transducers with 7° beam widths. Acoustic data were recorded to a range of 250 m at a 1 Hz ping repetition rate during the KACTAS voyage and 2 Hz pulse repetition rate during the KAOS voyage.
The mean vessel speed was 7. 8 knots giving a mean inter-ping spacing of 4 m for KACTAS and 2 m for KAOS. In addition to the raw data that we describe here, we provide data on metrics of the krill swarms that were identified (e. g. Cox et al. 6  ; Table  2 ). Krill swarms were extracted from data collected when the vessel was  surveying along line transects, and not engaged in other activities, i. e. net sampling and CTD casts, or steaming between transects. Acoustic data were cleaned before krill swarm identification: seabed returns and surface noise were removed, as was background noise  10. Seabed aliased returns were also removed manually. Potential krill swarms were delineated using the Shoal Analysis and Patch Estimation System (SHAPES) algorithm  11  (implemented in Echoview v12. 1 Echoview, Hobart Australia) was applied to the clean 120 kHz data convolved by a uniform 7 × 7 filter.
Minimum school length (m) 15 Minimum school height (m) 2 Minimum data threshold (dB re 1 m -1 ) -70 Table  5. Krill identification and scaling parameters. Krill identification parameters are from the 'dB-difference' method and are presented as the minimum (Min. dB diff) and maximum (Max. dB diff) values for the 120 kHz -38 kHz frequency difference that were considered to be krill. Acoustic echoes from krill swarms were scaled from mean volume backscattering strength (S v , units: dB re 1 m -1 ) to volumetric density using the krill target strength (TS) of 1 kg (wetmass) of krill (units dB re 1 kg). Krill target strength data. The realization of the krill acoustic target strengths used in this study are available here: https://github. com/ccamlr/2019Area48Survey/blob/09a26bb38d8eb231f9740367dd65413c05b5d781/ results/SDWBA-TS-38-120-200.
Technical Validation Echosounder performance is strongly dependent on water temperature (see for example  27, 28  ) making it important to calibrate echosounders in water at a similar temperature to that of the survey area. Here, the echosounders were calibrated in cold water (temperature -1 °C) for both KACTAS and KAOS and the calibration parameters (Table  6 ) were applied during data processing. The calibration was carried out using the standard methods of 29 with a 38. 1 mm diameter tungsten carbide sphere as the reference target. The calibration of the EK500 used during the KACTAS survey and the EK60 used during the KAOS survey were carried out in Horseshoe Harbour, Mawson on 24-Jan-2001 and 4-Feb-2003 respectively.
Usage Notes The overall objective of the KACTAS and KAOS voyages was to clarify the relationship between the distribution of krill, predators of krill, and the surrounding oceanography. For example, the acoustic data from KAOS have also been used to compare krill predator (Adélie penguin, Pygoscelis adeliae)  5, 30  distribution to krill. Since the main objective of the acoustic component of KACTAS and KAOS was to survey krill, the collection of high resolution acoustic data was facilitated by utilizing, a 1 Hz (KACTAS) and 2 Hz KAOS) pulse repetition rate, which was achieved by limiting the echosounder sampling range to 250 m. This was considerably less than the typical operational range achieved by 38 kHz echosounders of 600 to 1,000 m, e. g. Haris et al. 31. Additionally, due to increased scattering at higher frequencies, 200 kHz returns were limited to 100 m depth.
By using ping-based resampling (7 × 7 uniform convolution) during data processing, we have attempted to conduct schools-based analyses at a common scale. Nevertheless, given the different ping rates between the two surveys, researchers should be mindful of the inherent difference in the underlying along-transect (horizontal) spatial resolution of the two data sets. Also, as krill were observed to 250 m, we investigated the number of swarms that may have resided below 250 m and remained undetected. Using log-normal statistical distributions fitted to the vertical distribution of krill for each survey, we estimate only 0. 2% of krill swarms were found below 250 m during the KACTAS survey and 0. 5% during the KAOS survey. Due to rapidly changing bathymetry in some parts of the survey area, there were numerous instances of seabed aliased returns (false bottom) in the 38 kHz (Fig.