dataesr/scientific-paragraphs-categorization huggingface dataset:
More specifically, each term is represented by a circle node, where its size is proportional to the number of input genes fall under that term, and its color represent its cluster identity (i. e. , nodes of the same color belong to the same cluster). Terms with a similarity score >0. 3 are linked by an edge (the thickness of the edge represents the similarity score). Proteomaps open-source tool was used to show an in-depth overview of the different abundant protein function using the polygon module based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways gene classification. Individual proteins are shown as a polygon, whereas polygon sizes reflect the fold of changes in abundances. Functionally related proteins are arranged in proximity and with a similar color.
Statistical analysis Myofibrillar fragmentation index, densitometry data of SDS-PAGE and immunoblotting protein bands, and the 2DE volume spots were analyzed using the mixed GLM procedure of the SAS statistical software 9. 3 (SAS Institute, 2013). The model included the dietary treatment (C: control and H: hazelnut skin) and the days of storage (0, 4 and 7 days) and their interaction (diet x storage) as fixed effects, while the individual lamb was included in the model as random effect. All effects were tested for statistical significance (P<0. 05). When significant effects were observed (P<0. 05), the Tukey test was used to locate significant differences between groups. The main tendencies in variation between the two dietary groups during storage time were determined with a Principal Component Analysis (PCA) performed using XLSTAT 2021, 1.
Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. In this study we explore the utility of neural networks to predict the correct model of sequence evolution and the correct topology for four taxon alignments. We trained neural networks with different architectures using simulated nucleotide and amino acid alignments for a wide range of models, model parameters and branch lengths. By comparing the model and topology prediction success of the trained neural networks with that of the Maximum Likelihood and Neighbour Joining methods, we show that for quartet trees, the neural network classifier performs better than the Neighbour Joining method and in most cases as good as the Maximum Likelihood method to infer the best model of sequence evolution and the best tree topology for nucleotide as well as for amino acid sequences.
e. the model of sequence evolution) that created the data is among the evolutionary processes considered in the phylogenetic tree reconstruction. The capacity of a statistical method to accurately estimate the correct tree, the model and its parameters when the amount of data approaches infinity is known as statistical consistency. Beyond consistency, it has been shown that the ML method is efficient and robust. A method is called efficient if the amount of data necessary to find the correct solution with a high probability is small and it is called robust if it finds the correct solution even if some of its underlying assumptions are slightly violated. For a range of possible model violations, the ML method was found to be robust  (Yang, 2014). Models of sequence evolution specify the probability that nucleotides or amino acids are substituted by other residues in given intervals of time.
Neural network architectures and implementation The training and prediction scripts and the neural networks were implemented in Python using the Tensorflow library  (Abadi et al. , 2016). Workflows for training the NNs and for using trained NNs for predictions are illustrated in Figs. 1 2. Distributions, such as site pattern frequencies, are best classified with a series of fully connected layers, called dense layers in tensorflow. We designed six different neural network architectures, which all consist of dense, normalisation and dropout layers, but with different numbers of branches leading from the input to the output layer and one network with interconnections. The neural networks we implemented are described in detail in the supplementary materials Section 1 and in Figures  S1-S2. We refer to the six neural network architectures as B1, B2, B3, B10, U, CU, where the n in Bn, stands for the number of independent branches in the network that lead from the input to the output layer, U stands for a U-shaped network and CU for a U-shaped network with internal connections.
Creating the training data sets Supervised training of neural networks requires extensive training datasets with many thousand alignments for which the correct model and topology are known. Alignments were simulated by starting from a random sequence with nucleotide or amino acid frequencies given by the substitution model. Sequences evolved under the stochastic process of a given model of sequence evolution along the given tree with branch lengths. In the present work we used the PolyMoSim-v1. 1. 4 software (https://github. com/cmayer/PolyMoSim) to simulate alignments of four sequences for a given tree and a specified model of sequence evolution for nucleotides or amino acids. PolyMoSim offers a wide range of output formats, including a format that directly outputs pattern frequencies of simulated alignments, which tremendously speeds up the process of creating training data sets.
For the five nucleotide models 5,000 alignments of length 30,000 bp and for the four amino acid models 4,000 alignments of length 10,000 aa were generated. The prediction successes of the neural networks were compared to the model prediction of the ModelFinder software implemented in IQ-Tree version 2. 1. 3. To conduct a fair comparisons, the set of models among which ModelFinder chooses the best model was restricted to the models available in the neural network prediction by specifying the command line parameter "-mset JC,K2P,F81,HKY,GTR -mrate I,G,I+G" for nucleotide and "-mset JTT,LG,WAG,Dayhoff -mrate I,G,I+G" for amino acid datasets. In ModelFinder the BIC was used to select the best model. A binomial test with the standard Wilson score interval and with continuity correction  (Wilson, 1927; Crawley, 2014)  has been carried out to determine whether or not the ModelFinder or the neural network predictions were significantly better.
To verify the topology prediction we simulated 1,000 alignments of length 1,000 and 10,000 nucleotide bases and amino acids for each of the three topologies. Two separate verification data sets of the given size were created for normal and short internal branch lengths. The topology predictions have been compared with the ML method as implemented in IQ-Tree and BioNJ  (Gascuel, 1997) , a Neighbour Joining algorithm implemented in IQ-Tree that is invoked by specifying the -fast option. For the BioNJ tree, corrected distances were determined by conducting a model prediction followed by estimating ML distances. For the ML tree inference, IQ-Tree was not restricted to a set of substitution models but was allowed to select the most appropriate model on its own. Again the same binomial test was used to determine whether or not the neural network method was significantly better or worse than the ML and/or the BioNJ methods.
Alternative machine learning algorithms Besides neural networks, a number of machine learning classifiers exist that are well suited for frequency distribution classifications (see e. g. Hastie et al. , 2009). To evaluate the performance of different machine learning algorithms, including the Gaussian process classifier, several implementations of the Random Forest classifier and the support vector machine classifier, the Lazypredict Python library was utilised  (Pandala, 2020). A detailed description of how the alternative machine learning algorithms were trained is given in the supplementary methods section 1. 6.
Alternative machine learning algorithms for topology prediction classifiers trained and tested on nucleotide alignments of length 100,000 bp that evolved under the K2P+I+G evolutionary model on trees with normal and short internal branchen lengths we obtained the highest prediction accuracy of 96% with the support vector machine classifier which is marginally better than the best neural network (B2) with 95. 4%. Detailed results are provided in the supplementary materials. For amino acid alignments of length 1,000,000 aa that evolved under the Dayhoff+I+G evolutionary model the highest prediction accuracy of 98% was obtained with the LightGBM classifier which is a bit lower than the prediction accuracy of the best neural network (B10) with 99. 1%. We found that nine out of 25 machine learning algorithms achieved a topology prediction accuracy rate of 90% or higher in predicting the correct tree topology for nucleotide data, whereas 12 out of 26 machine learning algorithms achieved a comparable accuracy for amino acid data sets.
During the course of the project we have already increased the size of the amino acid training data from a total of 60,000 300,000 training alignments, which increased the model prediction accuracy from 88. 0% (data not shown) to 99. 8%. We expect that an even larger training data set yields a neural network that is not significantly worse than ModelFinder. It should be noted that for amino acid alignments of four sequences, we have 160,000 different site patterns. Even for a length of the training sequences of 1,000,000 aa the average number of patterns found per pattern class is only 1,000,000/160,000=6. 25. Clearly, this alignment length does not allow us to simulate highly precise pattern frequencies. In order to sample the pattern frequencies sufficiently for all model parameter and branch length combinations, it is expected that (i) longer sequence lengths and (ii) a larger number of training alignments is required.
For alignment lengths of 1,000 bp to 100,000 bp they obtained model prediction accuracies that are comparable to the ModelFinder. Since they used branch lengths and model parameters from empirical data sets, the results are difficult to compare with our results since the variance of empirical distributions are smaller than those of the uniform distributions used in this project. Our results also show that neural networks are highly suitable to predict the best tree topology for nucleotide and amino acid alignments. Topology prediction accuracies of NNs for nucleotide alignments were as good or almost as good as those of the ML method. The ML method was marginally but significantly better only for alignments of length 10,000 bp that evolved on a tree with short internal branch lengths (0. 001 to 0.
The high number of misclassifications of the ML and NN method for short internal branch lengths are most likely attributed to cases in which site pattern frequencies of alignments favour the wrong topology by chance. In this case only longer sequences can help to reconstruct the correct topology. Suvorov (2020)  showed that CNNs which analyse images of alignments can be as good as ML and other methods for alignments that evolved on trees in the so-called Farris-and Felsenstein zones. For alignments of length 1,000 bp that evolved on trees with internal branches in the range 0-0. 5, their CNNs showed an accuracy of 82%, which is as accurate as the ML method (82%) for the same dataset. In our study, NNs showed accuracies above 99% for internal branches in the range 0. 1-0. 5 for the same alignment length.
For amino acid alignments the ML method was always significantly better than neural networks for alignments of the length 1,000 aa and for alignments generated with short internal branch lengths. For alignment lengths of 10,000 aa and normal internal branch lengths, the differences between ML and neural networks would not be significant if the level of significance would be corrected for multiple tests according to Bonferoni. As in the case of the amino acid model selection, the size of the training data set was a compromise limited by the available computational resources. Our initial training data set was even smaller with only 6,000 training alignments of length 1,000,000 aa. For the final amino acid NNs we used 60,000 training alignments of length 1,000,000 aa. When testing the trained NNs with alignments of length 1,000 aa and normal internal branch lengths the prediction accuracy for the four amino acid models improved considerably from 94.
For some of the classification tasks the best neural network architecture is significantly better than all other architectures, see supplementary Table  S1  for details. Altogether, the B10 architecture was the best for both substitution model prediction tasks. For most topology predictions, the B3 architecture is preferred and for the few cases for which B3 is not the best architecture, it is not significantly worse. Therefore it is always a good choice. The most complex architecture CU was the best architecture for several topology prediction tasks, but it could not outperform the other architectures significantly. While we have a significant difference in the number of features used in the nucleotide and amino acid data sets, we see no general trend that the more simple architectures are better suited for nucleotide data or more complex architectures are better suited for the amino acid data.
We have mentioned that an ever faster prediction would be possible if multiple predictions are combined or if a GPU is used. Despite the fact that neural network classifiers are currently limited to four taxa, there are a number of potential applications. From the fact that they are super fast, they could be useful in the following cases: (i) The PartitionFinder program  (Lanfear et al. , 2012; Lanfear et al. , 2017 ) spends most of its computing time on determining the best model of sequence evolution for a large number of subsets of a data set. Using neural networks could tremendously speed up the process of finding meta-partitions that best evolve under the same substitution model. (ii) Massive computations of quartet trees are used e. g. in approaches such as Quartet Puzzling  (Strimmer & von Haeseler, 1996) , Likelihood mapping  (Strimmer & von Haeseler, 1997) , and Sliding window phylogenetic analyses to detect introgression (see e.
Conclusion In this study we have shown that neural networks trained on site pattern frequencies of four taxon alignments are as accurate or close to being as accurate as the ML method to select the best fitting nucleotide or amino acid evolutionary model as well as the tree topology on which the sequences in the alignment evolved. In cases neural networks are not as accurate as the ML method, we provide evidence that a larger training data set should be able to close the accuracy gap. The main advantage of neural networks is that they much faster than the maximum likelihood method. The speed difference ranges from a factor of 3-4 on short nucleotide alignments to more than 1000 for long amino acid alignments. Furthermore, we have shown that other machine learning classifiers such as support vector machine and the LGBM classifier are about as accurate as the neural networks.
Data availability The software for the neural networks implementation as well as scripts that have been used in this study can be found at GitHub (https://github. com/cmayer/DeepNNPhylogeny). The PolyMoSim-v1. 1. 4 program that is needed for training data generation can be obtained at (https://github. com/cmayer/PolyMoSim). The trained machine learning models can be downloaded from the DryAd repository with the DOI https://doi. org/10. 5061/dryad. ksn02v783.
Finally, river discharge (Qms) data were obtained from the Hydrometeorology Service of the Regional Council of Bizkaia (see https://www. bizkaia. eus/Ingurugiroa_Lurraldea/Hidrologia_Ac/Datos_ meteo. asp?Idioma=CA&Tem_Codigo=2679), concretely from Muxika station. For the bacterioplankton analysis, additional 10 L of water were collected with a Niskin metal-free bottle and stored in opaque plastic jerrycans in the field. Once in the laboratory, water was prefiltered through 20-μm nylon net filters (Millipore, 90-mm diameter) and next through 0. 22-μm Durapore membrane filters (Millipore, 47-mm diameter) for bacterial counts and DNA extraction (half filter each). This filtration procedure is not able to discern free-living bacteria from the attached because they are pooled together in the filter.
Sequence processing Raw sequences were trimmed with Sickle tool (ver. 1. 33, N. A. Joshi and J. N. Fass, see https://github. com/najoshi/ sickle/releases/tag/v1. 33) in a paired-end mode and default quality values were used (Phred score ≥ 20). Subsequently, forward and reverse reads were merged with PEAR (ver. 0. 9. 6, see https://www. h-its. org/software/pear-paired-endread-merger/;  Zhang et al. 2014 ) by using a cut-off of P-value = 0. 01 for the observed expected alignment score. Then, to remove non-existent barcodes from the fastq file achieved by PEAR, fastq-barcode. pl (D. Smith, see https:// gist. github. com/dansmith01/3920169) was used. De novo method of USEARCH (ver. 7. 0. 1090, see https://www. drive5. com/usearch/; Edgar 2010;  Edgar et al. 2011)  was used in QIIME (ver. 1. 9, see http://qiime.
org/index-qiime1. html;  Caporaso et al. 2010a ) for chimera removal. The final length of V4 sequences was ~253 bp, length that was used in subsequent analyses to avoid background noise. UCLUST (ver. 1. 2. 22q, see https://drive5. com/usearch/manual/uclust_ algo. html; Edgar 2010) was used to assign similar sequences to operational taxonomic units (OTUs) by clustering sequences based on a 97% similarity threshold and SILVA reference database  (Quast et al. 2013 ) was used for taxonomic assignment. In this process, sequences that failed in PYNAST alignment  (Caporaso et al. 2010b)  were removed from the OTU table. Taxonomic assignment of blanks and samples was verified for cross-contamination. All sequences assigned to chloroplast and OTUs with <10 reads were removed. Similarly, samples with <5000 reads were excluded from the study, which included both blanks and a sample collected in July in U35.
Analysis So as to calculate diversity differences of OTUs among samples, alpha diversity (Chao1 and Shannon-Weaver index) was calculated using phyloseq (ver. 1. 14, see https://joey711. github. io/phyloseq/; McMurdie and Holmes 2013) in R (ver. 4. 1. 1, R Foundation for Statistical Computing, Vienna, Austria, see https://www. r-project. org/). To identify dissimilarities among and within water masses (30, 33 and 35 PSU) and variations across the annual cycle, Bray-Curtis dissimilarity networks were inferred and visualised with plot_network function in phyloseq. In brief, Bray-Curtis dissimilarities were computed pairwise between all samples, and an edge was drawn for every pair of samples with a Bray-Curtis value of <0. 6. Networks were created both for all bacteria and the rare (<5% abundance) groups.
Analysis of similarity (ANOSIM, 999 permutations) was used to verify that the bacterial community composition was significantly different between samples from different water masses, whereas Kruskal-Wallis analysis depicted the main OTUs responsible for the community dissimilarities observed (based on Bonferroni-corrected P < 0. 05). Correlations within physical-chemical variables were checked using the FactoMineR package (ver. 1. 31. 5, see https://CRAN. R-project. org/package=FactoMineR;  Lê et al. 2008). The resulting principal-component analysis (PCA) enables removing the variables that have a low statistical weight, or are shown to be correlated with other variables. In addition, the PCA contributes to the depiction of the physico-chemical dynamics and it eases finding putative drivers of changes in microbial community among water masses.
In addition, the influence of environmental factors on the bacterial community structure was explored by calculating the Spearman rank correlation (rho) among environmental variables (Euclidean similarity matrix built from normalised values of salinity, temperature, pH, DO, water turbidity, precipitation and Chl-a) and the community composition based on Bray-Curtis index matrix, by using bio-env method from the R package vegan (ver. 2. 3-4, J. Oksanen, F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P. R. Minchin, R. B. O'Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens, E. Szoecs and H. Wagner, see https://cran. r-project. org/package=vegan). This analysis was performed in a cumulative format, thus taking into account the cumulative effect of the environmental variables on the bacterial community composition.
Acknowledgements. Technical and human support provided by the  General Genomics Service-Sequencing and Genotyping Unit-SGIker of the University of the Basque Country (UPV/EHU)  is gratefully acknowledged. Authors thank  A. Laza ,  I. Uriarte  and  E. Bachiller  ( UPV/EHU ) for their technical assistance in sample collection. We are also grateful to  V. Joglar  from  Vigo University  for her writing and formatting corrections on the manuscript.
All animals were immunocastrated by applying three doses of antigonadotropin-releasing factor (GnRF) vaccine  (Bopriva®, Zoetis, Brazil). The first dose was applied at weaning and the second and third doses 30 and 90 days later, respectively. After weaning at 210 days of age, the animals were transferred to the feedlot and housed in individual covered pens with concrete floors that were equipped with individual feeders and drinkers. The two groups of animals were fed a diet formulated using RLM software v. 3. 3. 57 (Andrade et al. , 2023; Lanna et al. , 2011) -Table  S1 , The finishing diet consisted of 12. 7% roughage (sugarcane bagasse) and 87. 3% concentrates feeds (ground corn, soybean meal, cottonseed, potassium chloride and mineral-vitamin supplement). Mineral-vitamin mix had 1231. 10 IU/kg of vitamin A.
Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and image analysis For 2D-PAGE, approximately 375 μg of protein was loaded onto cm isoelectric linear strips containing pre-fabricated gel and ampholytes (pH gradient of 3 to 10). After separation of the proteins in the first dimension, the strips were transferred to a polyacrylamide gel (12. 5%) for separation in the second dimension as previously described  (Baldassini et al. , 2015). The gels of the experimental groups were scanned and analyzed using the Image Master 2D Platinum software v. 7. 0 (GE Healthcare, Illinois, United States) to obtain the following parameters: number of spots, matching, isoelectric point (pI), and molecular weight (MW). To ensure confidence in the detection and subsequent validation of the spot, the gels were run in triplicate.
Electrospray ionization tandem mass spectrometry (ESI-MS/MS) After image analysis, the differentially abundant protein spots (between treatments) were extracted from the gels with a scalpel, cut into segments of approximately 1 mm, transferred to microtubes containing 1 mL of 5% acetic acid (v/v), and subjected to the following steps: dye removal, protein reduction and alkylation, and tryptic digestion with ng mL -1 trypsin solution. Tryptic digestion of the spots was performed using a specific commercial kit (In-Gel DigestZP). The peptide sequences obtained by tryptic digestion were characterized by ESI-MS/MS as previously described  (Shevchenko et al. , 2007). The proteins were identified in the UniProt database (www. uniprot. org) based on homology.
genenames. org/tools/hcop/). Hierarchical heatmap clustering comparing the significantly enriched GO terms was performed. The overlap of the two proteomes was also investigated with a Circos plot, illustrating de the degree of overlap between protein lists of control versus vitamin A. The cut-off criteria were the default values: degree cut-off = 2, node score cut-off = 0. 2, Max depth = 100, and K-score = 2. Further bioinformatics analysis between the proteins identified in the LT of animals was performed using the STRING database (Search Tool for Retrieval of Interacting Genes, v. 11. 0 -https://string-db. org). A protein-protein interaction network relating the differential proteins according to the pathways to which they belong was then generated.