dataesr/scientific-paragraphs-categorization huggingface dataset:
Heat shock proteins (HSPs) are molecular chaperones that protect the proteome by folding denatured polypeptides and promoting the degradation of severely damaged proteins. These molecules are regulated in mammalian and non-mammalian cells, responding to stimuli that enhance longevity and become impaired during aging  [34]. As a general result, the trends observed for these two genes (HLA-DPA1 showing overexpression with age, and DNAJB5 showing repression with age) were consistent across the 4 datasets analysed (Fig. 2 ). This emphasizes the coherence and replicability of the results achieved by our method with independent datasets. The dataset 2  [12]  was produced with a transcriptomic platform (Illumina HumanHT-12 V3. 0 Expression Beadchip arrays) that included a smaller number of human genes (approximately 12,000 human Uni-Gene IDs).
For this reason, a direct comparison or combination with the other 3 datasets was not possible. These were performed with Affymetrix high-density microarrays (HG U133 Plus 2. 0 and Human Exon 1. 0 ST), and included a common set of approximately 20,000 human ENSG IDs. Therefore, only the gamma correlations and p-values of these 3 datasets were mathematically combined, and dataset 2  [12]  was used to corroborate the results for the genes measured in common with the other platforms. We generated combined adjusted p-values using the Stouffer's Z-score method (a powerful method for combining probabilities in meta-analysis  [35] ), merging the p-values obtained within each of the 3 comparable datasets (1, 3 and 4 in Table  1 ). The combination of gamma correlations and p-values provided a robust evaluation of each gene, and allowed the selection of the best features to produce the gene signatures associated with the datasets of cortex, hippocampus and cerebellum.
The genes in each signature were ranked by significance, based on the combined p-values (setting up a threshold < 0. 01), and selecting only the genes that maintained the same sign of the gamma correlation (positive or negative) across datasets. Following these criteria, we proceed to the selection of the genes included in the aging signatures. The signature from cortex included 1148 genes (456 up-regulated and 692 down-regulated). The signature from hippocampus included 874 genes (546 up-regulated and 328 down-regulated). The signature from cerebellum included 657 genes (323 up-regulated and 334 down-regulated). These signatures and their respective statistical parameters, calculated for each gene, are included in 3 tables provided as Supplementary Tables 2S, 3S, 4S. To further test the existence of a consistent profile of brain genes that are up-regulated with aging and another profile of genes that are down-regulated with aging, we applied a different methodology, separated from the gamma correlation study.
This has been described in Materials and methods, and the results of this alternative analysis are shown in Fig. 3. A pattern of up-regulation is found in the four datasets Fig. 1. Analysis using t-SNE of the whole transcriptomic signal of the of human brain samples from two datasets. Analysis using t-distributed Stochastic Neighbour Embedding (t-SNE) method on two transcriptomic datasets that include three regions of the brain: cortex, hippocampus and cerebellum. (A) The dataset 1 (from Kang et al. [10] ), included 489 samples: 429 of cerebral cortex, 35 of hippocampus and 34 of cerebellum; and (B) the dataset 3 (from Trabzuni et al. [11] ), included 625 samples: 374 of cortex, 121 of hippocampus and 130 of cerebellum. and also a pattern of down-regulation, confirming the results obtained with the application of the gamma correlation to each dataset.
Finally, it is worth to indicate that the gene signature found associated with aging in cortex includes more down-regulated genes (692) than up-regulated genes (456), revealing that the gene or genomic effect of aging, at least in the brain cortex, seems more tilted towards a loss-of-function than to a gain-of-function. However, this cannot be taken as general observation because changes in the hippocampus do not follow this trend. To avoid an excessive focus only on the cerebral cortex, we performed a similar analysis of the data obtained from the hippocampus and the cerebellum. Fig. 4  presents the expression profiles produced for 6 genes from hippocampus that showed a high gamma correlation with age stages (using the hippocampus dataset with the largest number of samples: 121)  [11]. Again, the analysis of the expression profiles throughout 7 age stages showed some genes with a clear over-expression trend, from young to old ages, and other group of genes that showed significant repression with age.
Functions show increase of immune response and decrease of neural signaling To identify particular biological pathways altered during aging, we performed a concurrent functional enrichment analysis in multiple annotation spaces (GO, KEGG, InterPro) using the software tool GeneTerm Linker  [27] , described in Materials and methods. This was done using the top 300 genes up-regulated with best positive gamma correlations and the top 300 genes down-regulated with best negative gamma correlations from the signature of cortex (since it included the largest number of genes and comprises the largest part of the brain). We selected the same number of genes (top 300 up-regulated and top 300 down-regulated), to provide a fair comparison in the capture of enriched biological functions. The results of these analyses are presented in Table  2 , which shows, for the up-regulated genes, a set of functional meta-groups with a clear biological signal concerning immune system activation, response to stress (in particular oxidative stress), response to damage (in particular DNA damage) and response to pathogens.
In this way, we believe that the aging gene signature described here will be very valuable for additional comparative studies with the Fig. 3. Main coexpression trends with aging detected in human cerebral cortex. Identification of the most significant coexpression clusters of genes found in the human brain cortex samples of four different independent datasets. The datasets are described in Table  1. The clusters were obtained using the fuzzy c-means algorithm and show a significant correlation along age, presenting two main trends: up-regulation with age (plots A, B, C and D) and down-regulation with age (plots E, F, G and H). The samples of each independent dataset were grouped by decades with increasing age (as in Fig. 2 ). transcriptomic profile of the brain of individuals with a neurodegenerative disease (in particular Alzheimer's disease, which do not have a clear molecular cause, neither any direct molecular therapy).
Loss of neurons with aging and increase in astrocytes and microglia activity As we have four independent datasets with full transcriptomic profiles of the genes corresponding to brain cortex samples from individuals of many different ages, from young to old people; we could investigate in these samples the status and evolution of specific cell types throughout the lifespan of the individuals. To do this, we need to find subsets of specific genes that can mark specific cell types in a selective and distinguishable way. Using single cell RNA sequencing, Darmanis et al. [30]  identified a set of molecular markers that allow classifying cell types in the human brain. Indeed, these authors proposed a set of 21 specific genes to identify neurons, other set of 21 genes to identify astrocytes and other set to identify microglia (Fig.
6 ). We used these gene sets to identify their change in expression levels between the studied age periods; calculating the average fold change of all the 21 specific genes on a cell type in young individuals in the initial decades of life (from 1 to 39 years old) (Fig. 6A ) and the average fold change in old individuals in the late decades of life (from 50 to 100 years old) (Fig. 6B ). The standard error of the mean (SEM) was calculated in the same way for the mentioned analysis (Fig. 6A, B ). It is important to note that the approach taken, dividing the data into a youth age frame (1 to < 40 years) versus an elderly age frame (> 50 to 100 years), is done because we want to make a solid comparison between young ages and advanced ages; not taken only into account just one stage change, but as many as we can include within these time frames for each dataset.
The size of each gene nodes is proportional to the absolute value of their correlation with bio-age. Up-regulated genes have a red border color and down-regulated genes a dark-blue border color. show a neural decline and deterioration observed in the last periods of life, mainly after 60-65 years. The same analyses were performed for the hippocampus using the 3 datasets from this region of the brain, revealing a very similar evolution of the cells signal through age stages, both across the young ages and across the old ages (shown in Supplementary Fig. 7S ). This confirms the initial results, suggesting that the expression changes in cortex and hippocampus with aging follow a similar pattern and affect to similar sets of genes and biological functions.
Deep-learning neural network provides reliable calculation of the biological age A DLNN was built to calculate individual age based on the information provided by the aging gene expression signature that we obtained in this work. The performance of the neural network was compared with two other machine learning algorithms (random-forest, RF, and support vector machine, SVM) applying exactly the same input data for training and validation (Fig. 7A, B, C ). The regression parameters provided by each of the three predictors (DLNN, RF and SVM) indicate that the neural network (with an adjusted R 2 = 0. 909) is the one that shows the lowest error in the calculation of the biological age with respect to the chronological age (i. e. , the nominal age that is known for each person included in the validation set in this study).
The R 2 , coefficient of determination, is a statistical measure of how well the regression predictions approximate the real data points. Thus, for the three methods compared using the validation dataset, the regression parameters (adjusted R 2 , adjusted p-values of the regression model and RMS Error) allowed us to measure the correctness of each method. This showed that the biological age predictor provided by the Deep Learning Fig. 6. Evolution of cell type specific gene expression with aging in cerebral cortex. Evolution, in 4 independent datasets, of the expression level of 3 cell type specific gene signatures derived from single-cell analysis. These signatures are used to identify the presence of 3 cell types (neurons, astrocytes and microglia) in the human cerebral cortex throughout different age stages across young ages or across old ages.
Comparison of DLNN predictor with other predictors of bio-age As far as we know, there are not many studies that use deep neural networks technique for predicting human age. A related and relevant study conducted by Putin and co-workers  [44]  used multiple DLNNs, stacked into an ensemble, to build a predictor of human chronological age, taking the data from common clinical blood tests. These authors trained their DLNNs using tens of thousands of samples from common blood biochemistry and cell count tests of patients undergoing routine physical examinations. The results of this work showed that the best performing DLNN, applied to the test dataset, achieved 81. 5% epsilonaccuracy within a 10-year frame, r = 0. 90, R 2 = 0. 80 and a Mean Absolute Error (MAE) of 6. 07 years in predicting chronological age.
98 to as low as 0. 46  [46] ; that showed a great variability of the Fig. 7. Deep Learning Neural Network applied to build a reliable predictor of biological age based on the aging gene signature of cortex. Construction of predictors of biological age with three machine learning methods (Deep Learning Neural Networks, DLNN, Support Vector Machine, SVM and Random-Forest, RF) using the transcriptomic profile of 1077 protein coding genes that are included in the aging gene signature from cortex: regression model obtained with DLNN (A); with SVM (B); and with RF (C). The predictors were built using data from 652 samples of brain cortex coming from 3 different datasets for the training, and the plots present the predictive regression models obtained with an independent validation set of 280 samples.
The chronological age of the samples is used as the observed variable for the prediction models. An independent calculation was performed to test how the expression profile of each single gene along the three datasets (1, 3, and 4) correlated with the biological age calculated for each sample by the DLNN predictor. This analysis was done using Spearman correlation and the tables included in (D) and (E), present the top 20 genes found to have the best mean positive r coefficient and the best mean negative r coefficient, respectively. prediction power and that would result, if transformed to adjusted R 2 values, in worse predictions than those provided by our DLNN based predictor of age. Other authors also used genome-wide methylation profiles to build a predictor of biological age  [47]. Again, in that work the metric used to assess the accuracy of the model differs from the one we used in our work.
Acknowledgements We acknowledge the funding provided to JDLR research group by the  Spanish Ministry of Economy, Industry and Competitiveness (MINECO)  with grants of the ISCiii co-funded by  FEDER  (references  AC14/00024  and  PI18/00591 ). We also acknowledge the funding received from the  European Commission EU   Horizon 2020 FET OPEN RIA Programme  (call  H2020-FETOPEN-1-2016-2017 ) with grant number  737390 :  Project ArrestAD. A PhD  research grant to OGV (from the  Program: Ayudas a la Contratación de Personal Investigador ) provided by the  Junta de Castilla y Leon (JCyL)  with the support of the  Fondo Social Europeo (FSE)  is also acknowledged. The funding boards had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.
Funding information The publication costs for this article were funded by the research grant  PI18/00591 , from the  Instituto de Salud Carlos III (ISCiii)  cofunded by the  Fondo Europeo de Desarrollo Regional (FEDER). This project has also received direct funding from the  European Commission EU   Horizon 2020 FET OPEN RIA Programme  (call  H2020-FETOPEN-1-2016-2017 ) under grant number  737390  ( Project ArrestAD ).
Availability of data and material The original raw data series used in this work can be found in GEO database (https://www. ncbi. nlm. nih. gov/geo/) searching for the register IDs indicated in Table  1. The code used for the deep learning NN bio-age predictor is included in a GitHub public repository available at: https://github. com/jdelasrivas-lab/RdeeplearningNN-bioage.
10 GSE36192 10 Dataset 3 10103 ) D2 (44) D3 (46) D4 (77) D5 (56) D6 (34) D7 (35) D8 (52) D9 (29) ) D2 (44) D3 (47) D4 (78) D5 (55) D6 (34) D7 (35) D8 (52) D9 (29) Trabzuni et al. stages in consecutive decades (D) (number: samples in each stage) Cortex samples correspond to: a Frontal, parietal, temporal & occipital cerebral wall. b Frontal cortex. c Frontal, temporal & occipital cortex. d Neocortex frontal, postcentral & entorhinal cortex.
Variation in Holobiont PalA Levels Across Ascidian Colonies and Collection Sites Typical procedures for natural product chemistry samples utilize bulk specimen collections for chemistry extraction (~30 individual ascidian lobes per lipophilic extraction in the case of S. adareanum). Prior to this study, variation in PalA content at the individual lobe or colony level (inset, Figure  1 ) was unknown. Our sampling design addressed within-and between-colony variation at a given sampling site, as well as between site variation. The sites were constrained to the region that was logistically accessible by Zodiac boat in the Anvers Island Archipelago off-shore of the United States Antarctic Program (USAP)-operated Palmer Station. S. adareanum colonies were sampled across seven dive sites (Figure  1 ), in which three lobes per multi-lobed colony were sampled from three colonies per dive site, totaling 63 lobes for PalA comparison.
The ASVs present in 50%-79% of samples represented the Dynamic50 category and contained 14 ASVs, which represented only 3. 3% of the data set. The remaining ASVs fell into the Variable fraction, which included 426 ASVs, representing 1. 7% of the iTag sequences, yet the majority of phylogenetic richness (Table  1 ). Comparative statistical analyses were conducted with the complete sample set, which was subsampled to the lowest number (9987) of iTags per sample. This procedure was limited by one sample (Bon1b) that underperformed in terms of iTag sequence yield. After the elimination of this sample from the analysis, the 62-sample set had 19,003 iTags per sample with a total of 493 ASVs, the same 21 sequences in the Core80 (with seven common to all 62 samples), the same 14 Dynamic50 sequences, and a total of 458 Variable ASVs.
There were also two additional phyla, an ASV related to a sponge-affiliated Verrucomicrobium isolate, and a Planktomycetes-related ASV (Table  1 , Figure  S2 ). Several of these ASVs were most closely related to isolates from marine sediments. Interestingly, five sequences identified from earlier cloning and sequencing efforts with this host-associated microbiome (Figure  S2 ;  [13] ) matched sequences in the Core80 and Dynamic50 data sets. Phylogenetic comparisons also revealed that the SaM isolates were distinct from the Core80 and Dynamic50 except for the Pseudovibrio sp. TunPSC04-5. I4 isolate, which was present in both the Core80, and the clone and sequencing study. The hierarchical clustering of Core80 ASVs (based on Bray-Curtis similarity) across all 63 samples did not reveal strong trends in site or colony specific patterns (Figure  3 ).
Culture Collection: Microbiome and Bacterioplankton Comparisons To address whether the composition of the Synoicum adareanum-associated bacterial cultivars was also present in the SaM or in the free-living bacterioplankton (< 2. 5-µm fraction), we considered the overlap in membership between the isolate 16S rRNA gene sequences and these other two data sets. Representation of isolates in the 16S rRNA gene ASV data set was estimated by comparing the two sequence data sets, albeit different ascidian samples were used for the culture collection and the S. adareanum survey. Excepting Pseudovibrio sp. TunPSC04-5I4 that was present in the Core80 with a 100% sequence match, two other isolates also had 100% matches to sequences in the Variable SaM (BONS. 1. 10. 24 and BOMB. 9. 10. 19). Three other isolate 16S rRNA sequences (BOMB.
Taxonomy is shown according to genome taxonomy database (GTDB) classification and NCBI taxonomy is included (GTDB/NCBI) where they differ. Biosynthetic potential only calculated for ASVs with genus-level taxonomic assignments was based on representative genome biosynthetic gene cluster content in the same genus (See Figure  S3  for list of genomes). ASVs in bold ranked in the top 10. Where more than one ASV was found per genus, the average relative abundance and standard deviations were summed. n = 63 individuals.
ASV identities between the SaM, the S. adareanum bacterial isolates and the bacterioplankton data sets were compared using CD-HIT (cd-hit-est-2d; http://cd-hit. org). The larger SaM data set which included 19,003 sequences per sample was used for these comparisons to maximize the ability to identify matches; note that this set does exclude one sample, Bon1b, which had half as many ASVs, though overall this larger data set includes nearly 200 additional sequences in the Variable fraction for comparison. ASVs with 100% and 97% identity between the pairwise comparisons were summarized in terms of their membership in the Core, Dynamic or Variable fractions of the SaM. Likewise, CD-HIT was used to dereplicate the isolate sequences at a level of 99% sequence identity, and then the dereplicated set was compared against the bacterioplankton iTag data set.
Phylogenetic analysis of the SaM ASVs, S. adareanum bacterial isolates, and 16S rRNA gene cloned sequences from Riesenfeld et al. [13]  was conducted with respect to neighboring sequences identified in the Ribosomal Database Project and SILVA and an archaea outgroup using MEGA v. 7  [63]. Two maximum likelihood trees were constructed-the first with the Core80 ASVs, and the second with both Core80 and Dynamic50 ASVs. A total of 369 aligned positions were used in both trees. A total of 1000 bootstrap replicates were run in both instances, in which the percentage (≥ 50%) of trees in which the associated taxa clustered together are shown next to the branches.
Statistical Analyses T-tests were run in Statistica (v. 13, Tibco Software, Palo Alto, CA, USA) to determine significance (p < 0. 05) of site-to-site, within-and between-colony variation in PalA concentrations. Similarity matrix and hierarchical clustering analyses were performed using PRIMER v. 7 and PERMANOVA+ (PRIMER-e, Auckland, New Zealand). Analyses were performed on the complete microbiome as well as the three microbiome fractions in most cases. The ASV occurrence data were square root transformed for all analyses. A heat map based on the Core80 ASV occurrence was generated with the transformed data, and hierarchical clustering with group average parameter was employed, which was integrated with SIMPROF confidence using 9,999 permutations and a 5% significance level. Bray-Curtis resemblance matrixes were created using ASV occurrences without the use of a dummy variable.
Co-occurrence networks were constructed using filtered ASV occurrence data sets in which the ASV were filtered to only those that were present in at least five samples resulting in a 102 ASV data set. The 102 × 63 matrix was provided as input to FlashWeave v1. 0  [64]  using default parameters, and visualized in Gephi v. 0. 9. 2  [65]. Then to consider whether the ASVs in the Core80, Dynamic50, or Variable fractions of the SaM were affiliated with particular levels of PalA in the ascidian lobes, PalA niche robust optimum and range were computed using the occurrence and dry weight-normalized contextual data  [66]. Weighted gene correlation network analysis (WGCNA package in R  [67] ) was used to identify modules and their correlation with PalA levels. The matrix was total-sum normalized  [68] , and WGCNA was used in signed mode.
Data Availability Synoicum adareanum microbiome Illumina sequence information and associated metadata are described under NCBI BioProject PRJNA597083 (https://www. ncbi. nlm. nih. gov/bioproject/?term= PRJNA597083), and the S. adareanum culture collection 16S rRNA gene sequences were deposited in GenBank under MN960541-MN960556. The bacterioplankton sequence information and associated metadata are described under NCBI BioProject PRJNA602715 (https://www. ncbi. nlm. nih. gov/bioproject/?term=PRJNA602715). Antarctic project metadata [71] and environmental metadata (data set identifiers: Synoicum_adareanum_microbiome_part1, Synoicum_adareanum_microbiome_part2, Synoicum_adareanum_microbiome_part3) are available at: POLA 3 R (2020)-POLA 3 R Polar Links to Antarctic, Arctic and Alpine Research. Scientific Committee on Antarctic Research, Antarctic biodiversity portal, (www.