dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional rarity of North and Central American Mammals We used funrar to analyze a subset of the dataset of North and Central American Mammals from  Lawing et al. (2016a Lawing et al. ( , 2016b)). We selected 265 species out of 558 for which trait information was available. We used 6 traits relevant to mammal ecology (body mass, litter size, diet breadth, trophic level, habitat breadth and terrestriality, see  Jones et al. (2009)  for detailed trait explanation). The dataset comprises presence-absence information for the 265 species across 9699 50km x 50km cells. We asked whether there are 'hotspots' of Functional Uniqueness in North America and Central America for the six aforementioned traits; whether species that are functionally unique are geographically restricted; what the most functionally distinct and unique mammal species in the dataset are; and whether there are more functionally rare species in temperate, tropical or boreal areas.
For each species, we calculated Functional Uniqueness and Taxon Restrictedness indices, and averaged them across species by grid cell. Because those two indices are regional-level indices, each species had a unique value, and the variation in averaged indices among grid cells thus reflects change in species composition. We produced maps of the average values for indices in North and Central America (Figure  3 ). We also computed functional rarity -the average of Functional Uniqueness and Taxon Restrictedness -where each is scaled between zero and one. The most functionally unique cells were in Cuba (Figure  3A ), meaning that they hosted, on average, species that are quite functionally unique compared to the species pool of North and Central America. This pattern may be due to the tropical climate present in Cuba, which is less present across the rest of the dataset.
Figure2: Functions available in funrar to compute the different facets of functional rarity. Functions handle two formats of site composition, the default one assumes that the input dataset is a site-species matrix, while the _stack() versions use "tidy" format; _com() functions provided for Functional Distinctiveness and Scarcity take a single community as input. Note that regional-level indices -Restrictedness and Functional Uniqueness -are computed using the complete dataset, giving a single index per species. The site-level indices -Functional Distinctiveness and Scarcity -are computed for each site-species combination, giving one value per site-species combination.
Acknowledgements This research is supported by the  French Foundation for Research on Biodiversity  (FRB; www. fondationbiodiversite. fr) in the context of the  CESAB  project ' Causes and consequences of functional rarity from local to global scales ' (FREE). PD and CV were supported by the  European Research Council (ERC)   Starting Grant Project  " Ecophysiological and biophysical constraints on domestication of crop plants " (Grant  ERC-StG-2014-639706-CONSTRAINTS ). CMT acknowledges the  European Commission for the Marie Curie IIF  ( H2020-MSCA-IF-2014-657951 ).
Data Accessibility funrar stable version is available on CRAN (via install. package("funrar") or https://cran. rproject. org/package=funrar) and the development version is on Github at https://github. com/Rekyt/funrar. The code necessary to reproduce the analyses is on Github at https://github. com/Rekyt/mamm_funrar, an archived version is accessible on Zenodo DOI: 10. 5281/zenodo. 375605.
An analysis of imputation accuracy of missing genotypic data was conducted using the software Beagle, showing that two of the eleven populations were highly sensitive to increasing levels of missing data. The regression model produced, for each trait and each population, estimates of heritability (FW:0. 35, SC:0. 48, TA:0. 53, on average) and repeatability (FW:0. 56, SC:0. 63, TA:0. 62, on average). Predictive ability was estimated in a five-fold cross validation scheme within population as the correlation of true and predicted phenotypes. Results differed by populations and traits, but predictive abilities were in general high (FW:0. 60, SC:0. 72, TA:0. 65, on average). Conclusions: This study assessed the feasibility of Genomic Selection in peach for highly polygenic traits linked to yield and fruit quality.
73 x MB1. 73 (TxE) from Spain. The parental "SD40" originated from a cross between P. persica and P. davidiana. All plants were genotyped with the peach IPSC 9K SNP array  [14, 26] , with an average call-rate of 96. 7%. Of the initial 1 147 samples, 57 had a call-rate ≤ 0. 90 and were discarded. Of the initial 8 144 SNP markers, 2 068 SNP that were monomorphic in all populations or had a callrate ≤ 0. 90 were removed from the dataset, leaving 6 076 SNP for the analysis. The residual average missing rate was 3. 19% (1. 17% in BbxNl; 5. 79% in TxE). A summary of plant populations (i. e. progenies) and genotype data can be found in Table  1.
Phenotypic data Fruit weight, sugar content and acidity measurements were available for the 1 090 peach trees left after editing for call-rate from the 11 P. persica populations. Fruit weight (FW) in grams was measured as the average weight of 10 random peaches sampled from each tree. Sugar content (SC) and titratable acidity (TA) were measured, respectively, as average Brix degrees (soluble solid content) and meq/100 ml in the juice of at least five ripe fruits. FW was available for all 11 peach crosses; SC and TA only for 9 crosses (all except BoxBo and WxBy). In most cases, phenotypic records for multiple years were available (most commonly two or three years; only one for FW in BoxBo, as many as 6 for FW in PxF), collected between 1995 and 2013. The number of records spanned from 16 (TA, TxE, 2011) to 127 (FW, BC2, 2005).
Imputation of missing genotypes After genotyping and editing for call-rate, residual missing genotypes were imputed using the localized haplotype clustering imputation (LHCI) method implemented in the software "Beagle"  [27]. Originally developed for human genetics, LHCI has since found wide application also in animal and plant genetics (e. g. [11, 17, 20, 28] ). Imputation was carried out in each cross separately, to avoid potential problems due to population heterogeneity. The accuracy of imputation was measured. For each cross, a subset with no missing data was extracted from the total dataset, and increasing proportions of missing genotypes were then artificially introduced: 1, 2. 5, 5, 7. 5, 10, 12. 5, 15, 17. 5, 20, 22. 5, 25, 27. 5 and 30%. Missing genotypes were imputed using the LHCI method in Beagle.
Discussion The joint effort between this paper and the paper by Hernandez-Mora et al. [37]  constitutes the first large work to investigate the applicability of genomics-assisted breeding for complex quantitative traits (QTL mapping and genome-enabled predictions) in P. persica. The genetic materials and phenotypic records in the two works largely overlap; however, Hernandez-Mora et al. focussed on QTL detection, and looked at genome-enabled predictions as a collateral result. In this study we provide further insights into genomic predictions in peach trees, considering also the variability of estimates in specific cross-trait combinations. We selected three quantitative continuous traits (fruit weight, sugar content, titratable acidity) based both on their commercial importance and on the availability of complete datasets spanning several crosses and years.
Such variable results suggest a strong influence of the genetic background of each population on genotype imputation in peach. Putting together the average missing rates in the original datasets (Table  1 ) with the corresponding estimated imputation accuracies (Additional file 2), the amount of imputation errors in the data used for genomic predictions can be estimated in the range 0. 88% (BbxNl) -5. 33% (TxE), with an average of 2. 71%. Given the low initial missing rates, and the generally good imputation accuracy, there are therefore few residual imputation errors, which are expected to have negligible impact on genomic predictions.
g. trying to understand the relative influence of genetics and environment on the phenotype) are different problems, and a model may yield good predictions even when the underlying biological mechanisms are poorly understood or estimated (and vice-versa:  [62] ). In some cases, PA showed very low variability, like in BxO for FW or in BbxNl for TA; in other cases, PA was so variable to become practically unreliable, like FW in BoxBo, where predictive ability went from -0. 5 to 0. 99. The performance of genomic predictions can be influenced by the size of the analysed dataset, the heritability and repeatability of the trait, and by the phenotypic variability. Additional files 3 and 4 show the coefficient of variation of PA as a function of sample size and phenotypic variability (x-axis). In both figures, the general trend is that the variability of estimates tends to be larger with smaller sample size and smaller phenotypic variability.
The benefits of genomic selection stem not only from accurate genome-enabled predictions and shorter generation intervals, but also from potentially lower phenotyping costs. Collecting phenotypes in fruit trees is costly  [67] , and restricting phenotypic evaluation to smaller subsets of progenies for shorter times may be beneficial. Coupling genomics, high-throughput phenotyping  [68, 69] , databases and tools for breeding has the potential of creating effective platforms for genomics-assisted breeding in all plant species (e. g. rice,  [70] ), particularly in fruit trees, including peach (see  Iwata et al. 2016  for a review  [42] ). Genome-enabled predictions are mainly used to select breeding candidates in genetic improvement schemes. However, in species with a long life-cycle, accurate genome-enabled predictions may be particularly useful to predict future phenotypes of the plant: e.
Acknowledgements This work was supported by the  European Union -funded project " FruitBreedomics: Integrated approach for increasing breeding efficiency in fruit tree crops"  (Grant # FP7-265582 ; http://fruitbreedomics. com/; http://ec. europa. eu/research/fp7/index_en. cfm). Work in Italy was partly supported also by  "Ministero delle Politiche Agricole Alimentari e Forestali" (MiPAAF , http://www. politicheagricole. it)through the project " DRUPOMICS: Sequenziamento del genoma del pesco ed utilizzo della sequenza in programmi di miglioramento della qualità del frutto del pesco e della resistenza alle malattie " (Grant #  DM14999/7303/08 ). DB received funds also from Italian private and public agencies within the project " MAS. PES: apricot and peach breeding by molecular-assisted selection".
Beyond this scheme, there is significant scope for reducing the time required to perform US tracking. For instance, transmissions from tracking transducer elements could be performed with orthogonal codes, so that they could be performed simultaneously and subsequently decoded from a single FOH signal. With the current system, tracking images were acquired and saved at 1 to 2 Hz to allow for sufficient time to transfer data from the digital acquisition card, which included both bipolar and Golay coded tracking transmissions. Challenges with identifying the needle tip that are routinely encountered during US-guided interventions in current clinical practice can be overcome with ultrasonic tracking. Here, tracking images provide uncluttered views of the needle tip in which only this object is present.
Incorporation of anatomical information and corresponding variations in the SoS will improve tracking accuracy. When the needle tip is in the imaging plane, tracking data from the central array of transducer elements, which are naturally co-registered to the US images  12, 13  , could supplement tracking data from the side arrays. Tracking data from previous needle positions could be used to estimate the needle tip trajectory. Requirements for tracking accuracy in the depth and out-of-plane dimensions are likely to depend on the clinical procedure being performed, and on the current depth and proximity to the image plane of the needle tip. With in-plane insertions, the primary role of out-of-plane tracking may be to determine on which side of the imaging plane the needle tip resides, in order to reorient the imaging probe to bring the needle in-plane.
The FOH sensor console provided a signal proportional to the time-varying reflectivity of the cavity with a wavelength-tuneable laser. With the wavelength tuned to optimise sensitivity of the cavity to impinging US waves, the reflectivity signal was proportional to the US pressure. Three US transmission sequences were used for tracking, and one was used for B-mode US imaging. The first tracking sequence comprised bipolar pulses; the second and third were 32-bit Golay code pairs generated from bipolar pulses  15. Tracking sequences were performed from individual transducer elements, sequentially across rows (Fig. 1b ). All bipolar pulses used for tracking had a central frequency of 4 MHz. B-mode sequences were performed with bipolar pulses (9 MHz) from an aperture of 64 elements and an electronic focusing depth of 30 mm.
Acquisition FOH sensor data was synchronised with US transmissions using two trigger signals: a frame trigger (FT) for the start of all 128 transmissions, and a line trigger (LT) for each transmission. For each FT, FOH signals and the LT were digitised at 100 MS/s (USB-5133, National Instruments, Austin, USA)  12, 13. The US imaging/tracking probe was controlled by a custom program operating on the US imaging system written in LabVIEW (National Instruments, Austin, USA).
Coded excitation. Coded excitation of the US transmissions was performed with a 32-bit Golay code pair to increase the SNRs of the FOH signals. This scheme is described in detail in ref. 13. Encoding was performed by convolving a bipolar base transmission sequence with each Golay code pair sequence; the output was used to excite individual transducer elements for US transmissions. The received US signals from the FOH were band-pass filtered (Chebyshev Type I; 5th order; 2-6 MHz). Decoding was performed with two steps. First, the filtered FOH sensor data was convolved with each of the time-reversed versions of the oversampled Golay code pair sequences to generate two decoded sequences. Second, these two decoded sequences were summed, which mitigated the range side lobes  13. The summed data were concatenated to form 4 tracking images, with each image corresponding to transmissions from one side array.
The w  (k)  values ensured that tracking images with higher signal amplitudes contributed more prominently to the estimates of  x and  z than others. Analysis of the signals was performed in Matlab (Natick, New Hampshire, USA). Tracking accuracy: measurements in water. Tracking accuracy was evaluated with the US imaging/ tracking probe and needle in deionised water. The needle tip position was held constant with a mechanical mount; the probe position was varied relative to that of the needle tip with a 3-axis translation stage (nominal location accuracy ±4 µm; MTS50/M-Z8, Thorlabs, UK). To evaluate accuracy in the out-of-plane dimension (X), the X coordinate of the needle tip (measured relative to the probe) was varied from 0 to 20 mm in steps of 1 mm. These variations were performed at three depths (Z = 20, 40 and 60 mm), with the needle tip always laterally centred with respect to the probe.
Similar variations were performed to evaluate accuracy in the lateral dimension (Y), at the same three depths (Z = 20, 40 and 60 mm), and with the needle tip always in the imaging plane (X = 0). For accuracy in the depth (Z) dimension, the Z coordinate of the needle tip was varied from 20 to 60 mm in steps of 1 mm, with the needle tip at three out-of-plane locations (X = 0, 10 and 20 mm) and always centred laterally with respect to the probe. At each probe position, FOH sensor data were acquired for needle tip tracking. The spatial coordinate of each needle tip position in the translation dimension was compared with a corresponding reference position obtained from the translation stage. The relative tracking accuracy was defined as the Euclidean distance between these two quantities. Tracking accuracy: simulations with speed of sound heterogeneity.
Acknowledgements This work was supported by an  Innovative Engineering for Health  award by the  Wellcome Trust  (No. WT101957 ) and the  Engineering and Physical Sciences Research Council (EPSRC)  (No. NS/A000027/1 ), by a Starting Grant from the  European Research Council  (Grant No. ERC-2012-StG, Proposal 310970 MOPHIM), and by an  EPSRC   First Grant  (No. EP/J010952/1 ). A. L. D. is supported by the  UCL/UCLH NIHR Comprehensive Biomedical Research Centre. The authors are grateful to the staff of the  Northwick Park Institute for Medical Research and the Royal Veterinary College  for their valuable assistance with in vivo experiments.
Some of these tools incorporated traditional machine learning techniques such as support vector machine (SVM), neural networks and k-nearest neighbor (KNN). In  [8] , the authors developed a Python package called "repDNA" to produce the features representing physicochemical properties and the sequence-order effects of DNAs and nucleotides. Fifteen types of features built from DNA sequences can be calculated using the repDNA package. These computed features can be categorized into three different categories: nucleic acid composition, autocorrelation and pseudo-nucleotide composition. The repDNA internal algorithms were evaluated by generating many test DNA sequences, then the generated output was compared with the ground truths of these sequences to ensure having an error-free implementation.
The package incorporates several traditional machine learning algorithms to be used with the generated features. In  [9] , the authors developed a web server called "repRNA", which can produce eleven modes of feature vectors. It also enables users to choose any set of features out of 22 built-in physicochemical properties. Moreover, repRNA gives the option to visualize the generated feature vectors through a built-in visualizer incorporated in the web server. repRNA makes it easy for users to perform any in-depth genome analysis as it can easily incorporate machine learning algorithms such as SVM and KNN, which were not capable of handling anything except vectors back then. In  [10] , the authors developed a Python package called "Pse-Analysis", which can automatically perform feature extraction and selection, parameter tuning, model training, cross-validation and evaluation for any biological sequences.
The training in Pse-Analysis uses the SVM machine learning model; also, the package provides a powerful multi-core CPU implementation, which efficiently decreases the running time compared to the one-core CPU implantation. In  [11] , the authors developed a web server called "Pse-in-One", which can reproduce almost every possible feature vector based on a set of properties defined by the user for DNA, RNA and protein sequences. The web server provides an easy way to combine the vectors with different machine learning techniques such as SVM and neural networks. Pse-in-One, the first developed platform of its kind, includes 148, 22 and 547 built-in physicochemical properties, which can be defined by users before generating the DNA, RNA and protein sequences. The ensemble learning approach is a common method used to acquire better performance through using multiple learning techniques.
They used the CNN technique to perfume the classification. The concept "one-hot vector", which saves the position records of each nucleotide in DNA sequences, was used. By applying the same representation technique of text, they obtained a two-dimensional numerical matrix, which contained information about the specific position of each nucleotide in the sequence. Then, this matrix was finally used as input to the CNN model to perform the classification of the DNA sequences into different classes. To show the efficiency of their classification method, they selected the 10 datasets that were used in  [25] , then they applied the proposed algorithm on them to compare the results obtained by their algorithm with the results obtained by other methods such as using n-mer features as the representation of sequences and SVM as the classifier.
The proposed method illustrated a good performance for multi-class prediction with 589 protein family classes. Their results showed that neural network classifiers give better performance than the tuned SVM baseline on the same UniProt dataset; particularly Gated Recurrent neural networks (GRU) outperformed their SVM baseline by almost 7%. In comparison to the SVM in  [27] , which trained many single-class classifiers, the neural network (NN) architectures of the multi-class protein performed well relatively, even if being more complicated. In  [28] , the authors investigated the reliability of Frequency Chaos Game sequence Representation (FCGR) for the classification of genomic sequences. They chose a CNN to solve the classification problem, and the obtained results were very good, especially for the full-length sequences, which were recognized with a very high accuracy.
In  [29] , the authors presented a new method for the prediction of protein disorders based on sequence information. Their approach used deep networks to make predictions. In a 10-fold cross-validation procedure on a dataset composed of 723 proteins, the method achieved an accuracy of 82% and an area under the receiver operating characteristic (ROC) curve of 90%. In  [30] , the authors used Bidirectional Long Short-Term Memory (BLSTM) to look for powerful features from pseudo-proteins; moreover, they proposed the "ProDec-BLSTM" classifier, which contains an input layer, BLSTM, a time distributed layer and, finally, an output layer. This designed architecture could automatically extract features through using the BLSTM and the time distributed dense layer. The ProDec-BLSTM classifier outperformed methods reported in  [31] [32] [33] [34] [35] [36] [37]  in terms of mean ROC and mean ROC50 scores, which were 96.
9% and 84. 9%, respectively. In  [38] , the authors proposed a classifier based on deep learning for the estimation of human protein subcellular localization. For the implementation of the proposed algorithm, the authors utilized GPUs and CUDA software (nVidia Corp. , Santa Clara, CA, USA) to tune the designed network parameters and effectively train them. Moreover, it was reported that the used architecture could automatically learn the high-level features that represent the human proteins by going over the polynomial relations between the sparse subcellular locations. The dataset used in their study contained 13,978 samples belonging to 10 different classes representing the subcellular locations. The algorithm acquired a total accuracy of 37. 4% of correct predictions for the 10 classes, which means that it could correctly predict 5226 samples out of the total number of samples available in the dataset.
Implementation and Experimentation The Torch7 platform was used to implement the CNN model for this work. The reason for selecting Torch7 is because it provides the maximum possible flexibility and speed in building the scientific algorithms including the different machine learning techniques  [40]. Crepe is the name of the code that was implemented using Torch7 for text classification from the character-level dataset using CNNs  [41] , and it has been used to produce the results in  [20]. Crepe contains the following components: Originally, this code processed text and classified it into different classes representing the topic to which the text belongs; however, for the DNA taxonomic classification, we modified it to process DNA sequences. The reason for pursuing a nearly similar approach for processing both The model's main component is the temporal convolutional module, which computes the 1D convolution between the input g(x) and the kernel f (x) to generate the output h(y), as is shown in Equation (  1 ); where c represents the offset constant.