dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of phylogenetic inference The accuracy of AF methods for data sets from three categories-gene tree inference, genome-based phylogeny, and horizontal gene transfer-was evaluated by a comparison of topology between the method's tree and the reference tree. The pairwise sequence distances obtained by the AF method were used as input for the neighbor-joining algorithm (fneighbor in the EMBOSS package  [87] , version: EMBOSS:6. 6. 0. 0 PHY-LIPNEW:3. 69. 650) to generate the corresponding method tree. To assess the degree of topological (dis) agreement between the inferred and reference trees, we calculated the normalized Robinson-Foulds (nRF) distance  [63]  using the Tree. compare function in the ETE3  [102]  toolkit for phylogenetic trees with the option unrooted = True. The Robinson-Foulds (RF) distance is a measure for the dissimilarity between two tree topologies with the same number of leaves and the same labels (species) at the leaves, i.
Acknowledgements We thank  Svenja Dörrer  for providing benchmarking data sets of unassembled sequencing reads. We also thank the tools' developers  B. Haubold ,  F. Klötzl ,  P. Kolekar ,  S. Mirarab ,  S. Sarmashghi ,  A. Phillippy , and  H. Yi  for recommending analysis parameters and helpful discussions. Computations were performed at  Poznan Supercomputing  and  Networking Center (PSNC).
Availability AAF  [38]  Availability of data and materials All data sets and results discussed in the paper are freely available from our website (http://afproject. org) through the download page (http://afproject. org/download/)  [66]. The specific reference data sets are available either through the dedicated pages (e. g. , http://afproject. org/app/benchmark/ genreg/crm/dataset/) or can be downloaded as a single package through: http://afproject. org/media/AF-reference_datasets190511. zip. The source code of the AFproject service is available under an open source license (Mozilla Public License Version 2. 0) at https://github. com/afproject-org/afproject [65].
Moreover the microcontrollers from  [6]  and  [4]  require to be initialized from an external device to obtain their program, which adds constraints to their integration in embedded devices. [1] ,  [5]  and  [3]  use an architecture and memory sizes similar to commercial products (Table  2 ), with non-volatile memory and a SRAM. Only  [2]  is based on a single non-volatile memory architecture, the other works presented in Table  1  separate application's program and data in different memories. Moreover,  [2]  uses only non-volatile memories. That solution helps to reach the lowest energy consumption in sleep mode without performing data transfers to save the content of volatile parts. [1],  [2]  and  [3]  use Ferroelectric memories when nonvolatility is needed. Here we use another kind of NVM technology: STT-MRAM.
A. MICROCONTROLLER ARCHITECTURE The specifications of an application determine which microcontroller to use. Manufacturers generally offer a large variety of microcontrollers to answer to the large number of actual embedded applications and their specific constraints, as it is not possible to create one microcontroller architecture that will fit all applications. There are microcontrollers with different packages, number of input/output pins, different processors, operating frequencies, peripherals, communication interfaces, analogic modules, low-power modes, memory technologies, memory capacities, and dedicated to different kinds of applications (automotive for example). However, there are some similarities between all these different devices. Typical microcontrollers include at least one processor, a non-volatile memory (usually Flash for code instructions and read-only data), a volatile memory (usually SRAM for application data), a power management unit, a clock management unit, input/output peripherals, communication modules (UART, SPI, I 2 C, USB, CAN…) and timers.
This is a 3-stage 32bit RISC processor that implements the ARMv6-M ISA, with a maximum frequency of 50 MHz. It includes a single AHB-Lite interface, 32 interrupt lines, 1 Non-Maskable Interrupt and a single-cycle multiplier. In this study, we focus on memory architectures for a single master system (which is the Cortex-M0, with a single bus interface). As STT-MRAM is a non-volatile random access memory, it could be used to replace both Flash for code memory and SRAM for dynamic data memory. To evaluate the possible gains by using STT-MRAM, we The memory architecture of commercial low power microcontrollers using an ARM Cortex-M0 or an ARM Cortex-M0+ is in many cases composed of at least a Flash memory and a SRAM. The microcontroller families presented in Table  2  have 4 kB to 256 kB of Flash and 2 kB to 36 kB of SRAM.
A. SYSTEM DETAILS The system is kept as simple as possible to focus the evaluation on the memory architecture. The Cortex-M0 and the memories are organized around a single master AMBA3 AHB-Lite bus architecture. Here are the assumptions for this evaluation:  The maximum operating frequency is chosen to perform each memory operation in one cycle.  Both SRAM and STT-MRAM support 8-bit, 16-bit and 32-bit write operations.  Each read operation is 32-bit wide.  There is no interrupt, exception nor event.  There is no shadow memory operation (single master). The energy of the matrix bus and memory controllers are not included in this work. The software benchmark used is the ULPMark from EEMBC  [20]. CoreProfile (ULPMark-CP) is an application designed to reproduce a periodic behaviour with active and sleep phases (see Figure  1 (a) ).
The active phase of the CoreProfile is composed of math functions (linear approximation, filtering), conversion tables, string search, table copy, sorting, data permutations and output toggling. This application code is mostly used to evaluate and compare the energy efficiency of Ultra-Low-Power microcontrollers for Internet of Things applications. A comparison of the memories in terms of power consumption and energy per operation is summarized in Table  6. The energy consumption of the SRAM comes from a 128 kB memory implementation in 28-nm FD-SOI from STMicroelectronics, the read energy of the 28-nm Flash memory is extrapolated from the data of  [21]  and the data of the STT-MRAM are based on the results presented in Section III. If the STT-MRAM has a bigger writing energy cost (3. 0 pJ/bit) than SRAM (0.
B. RESULTS For each architecture scenario, the active phase of the application executes in 49767 cycles (32773 instructions executed). Table  7  shows the total count of the different memory operations in the code memory (that is, the location where read-only data and program code are stored) and the data memory (volatile data). Because the program code is located in the code memory, no instruction fetch occurs in the data memory, but writes operations only occur in the data memory. One instruction fetch corresponds to a 32-bit read. Figure  7  shows the estimated dynamic energy consumption of the memories, for the different architectures previously described in Figure  3. Each memory is represented by a different colour to distinguish its contribution. For scenarios ① and ②, where code and data are separated in two different memories, and where only read operations are performed into non-volatile memories, we observe that the dynamic energy consumption of the STT-MRAM is lower than the Flash's one (around 26%).
V. CONCLUSION We have designed a 128 kB (1 Mb) peSTT-MRAM, 28-nm FD-SOI CMOS with single 32-bit port SRAM-like interface for low-power embedded application. With 0. 9 pJ/bit read in 5 ns and 3 pJ/bit write in 10 ns, this embedded non-volatile memory is suitable for low-power devices that run at frequencies under 100 MHz. We presented the evaluation of STT-MRAM, SRAM and Flash solutions for different memory architectures (single and multiple memories architectures). STT-MRAM is a more interesting solution than Flash, thanks to a lower read energy (26% gain). Moreover, it has a faster, more flexible and more energy efficient write capability than traditional embedded Flash. When used as the sole memory of a system, the non-volatility of MRAM helps to reach the lowest power consumption in sleep mode, although this solution (one STT-MRAM for both code and data) is not the best for active mode.
The developmental potential of cells, termed pluripotency, is highly dynamic and progresses through a continuum of naive, formative and primed states. Pluripotency progression of mouse embryonic stem cells (ESCs) from naive to formative and primed state is governed by transcription factors (TFs) and their target genes. Genomic techniques have uncovered a multitude of TF binding sites in ESCs, yet a major challenge lies in identifying target genes from functional binding sites and reconstructing dynamic transcriptional networks underlying pluripotency progression. Here, we integrated time-resolved 'transomic' datasets together with TF binding profiles and chromatin conformation data to identify target genes of a panel of TFs. Our analyses revealed that naive TF target genes are more likely to be TFs themselves than those of formative TFs, suggesting denser hierarchies among naive TFs.
Trans-omic data pre-processing RNA-seq, ChIP-seq, and MS-based proteomics data were processed as previously described  (27). Briefly, sequence reads from RNA-seq experiments were aligned to the mouse reference genome (mm9 assembly) using STAR  (31)  version 2. 5. 2a, allowing up to three mismatches and acquiring unique reads only. Gene expression across eight time points of the differentiation were quantified using HTSeq (32) version 0. 6. 1 against Ensembl mouse gene annotation. Sequence reads from ChIP-seq experiments were aligned to mm9 using Bowtie (33) version 0. 12. 8 allowing at most two mismatches and accepting only reads mapped to unique genomic regions. Histone modifications and Pol2 ChIP-seq signals were quantified for each gene by computing reads per kilobase million (RPKM) of 1Kb region centred around the transcription start site of each gene, and this is repeated for each of the eight time points.
Differentially regulated TFs Analysis of variance (ANOVA) tests were performed on time-course RNA-seq and MS-based proteomic data to identify differentially regulated genes on mRNA and protein levels, respectively. Benjamini-Hochberg correction that controls for FDR was applied to account for multiple testing. Subsequently, for each gene, an integrated -log 10 ( p) of mRNA and protein was derived as: min (-log 10 ( p G ) , -log 10 ( p P )) where p G and p P are adjusted p-values from ANOVA analysis of mRNA and protein for each gene, respectively. Similarly, an integrated log 2 fold change of mRNA and protein was calculated for each gene as: min max log 2 G i G 1 , max log 2 P j P 1 sign (. ) where G i (i = 1. 8) and P j ( j = 1. 9) are expression levels of mRNA and protein at a time point.
The sign(. ) function determine the sign of the selected fold change. Genes with a smaller than 0. 05 integrated P-value and a >1 integrated log 2 fold change, as well as being annotated as a TF in public databases  (35, 36)  were noted as differentially regulated TFs during the pluripotency progression. Those that have been profiled previously using ChIP-seq in ESCs (i. e. Sox2, Nanog, Esrrb, Klf4, Nr5a2, Otx2 and c-Myc) were selected for analysis.
Chromatin interaction and putative TF target genes Chromatin interaction between distal TF binding sites and promoters were determined based on published Pol2-ChIA-PET (20) dataset in ESCs. A TFBS was considered to interact with a promoter if the 1 kb region centred around the mid-point of the binding site and the 1 kb region centred around a transcription start site (gene promoter) has been observed to interact with Pol2-ChIA-PET interaction data. Genes that are involved in chromatin interaction with the distal binding sites of a TF were considered as putative distal target genes of that TF. Together with proximal target genes (i. e. genes with promoters that are bound by the same TF within 1kb), they were denoted as putative target genes of the TF under consideration.
Dynamic network reconstruction and analysis AdaEnsemble-identified TF target genes that are themselves TFs were included for visualizing the transition of transcriptional networks from naive to formative pluripotency using igraph R package with a layout calculated by using Kamada-Kawai force-directed algorithm  (38). The temporal mRNA expression of a gene x was first standardized across time points using z-score transformation: z x i = G x i -Ḡx σ x where for each gene G x i (i = 1. 8 ) is the expression level of mRNA at a time point i , and Ḡx and σ x are the average expression and variance across time points. After the standardization, the expression between a pair of genes λ i (x, y) on the mRNA level at a time point of i (i = 1. 8) was calculated as the average of their standardised temporal mRNA expression values at each time point: λ i (x, y) = z x i + z y i 2 To quantify overall expression change, the pairwise expression calculated above was averaged for all target genes of either naive or formative TFs.
Spatiotemporal mapping of differentiating ESCs To determine the in vivo counterparts of ESCs during pluripotency transition, we mapped cells from each time point to E5. 5-E7. 5 epiblasts of mouse embryos based on the TF target genes identified in this study. Briefly, Geo-seq samples of embryos were used for mapping  (41)  and the area under the recovery curve across the ranking of genes was calculated for each Geo-seq sample and ESCs from each time point using the AUCell R package (v1. 2. 4)  (42). The area under the recovery curve was then used as the activity score which measures the enrichment of in vitro ESCs at each time point and in vivo spatial epiblasts. Finally, the corn plots of differentiating ESCs at each time point were generated based on the enrichment on each Geo-seq sample.
05 from the integrated transcriptome and proteome data (see more details in 'Materials and Methods' section). This threshold captures both the magnitude of change as well as reproducibility within biological replicates across the time points to ensure that selected TFs are significantly altered during the ESC to EpiLC transition. Using this approach, we found 110 TFs that are dynamically regulated during the differentiation process (Figure  1C ; Supplementary Table  S1 ). Lastly, we filtered the dynamically regulated TFs, selecting a panel of seven that have been profiled in a compendium of ChIP-seq datasets in ESCs  (14). The seven selected TFs are known to promote ESC selfrenewal or differentiation through previous genetic and/or functional studies (Figure  1C ). Five out of this seven are down-regulated during the pluripotency transition, Sox2  (44) , Nanog  (45, 46) , Esrrb  (47) , Klf4  (48) , and Nr5a2  (49)  and two are up-regulated, Otx2 (26) and c-Myc  (50).
After calling the binding sites for each of these TFs from their respective ChIP-seq datasets using SISSRs (37), we categorized them as promoter-proximal (binds within 1 kb of a TSS) and distal binding sites (binds 1 kb away from TSSs) either without or with chromatin loop support (interacts with one or more TSSs through chromatin looping, as supported by the Pol2-ChIA-PET data) (Supplementary Figure  S1A ). We next identified putative proximal and distal target genes for each TF (see 'Materials and Methods' section) (Figure  1D ). Assessing the relative ratio of putative proximal versus distal target genes for each TF, we found that while c-Myc and Otx2 have more putative proximal target genes (72% and 57%, respectively), other TFs have relatively more putative distal target genes (57-78%).
Prediction of TF target genes during pluripotency progression using AdaEnsemble Identification of genes that are regulated by master TFs is an essential step for reconstructing transcriptional networks during the pluripotency progression. Here, we integrated time-resolved trans-omic data with publicly available Pol2-ChIA-PET  (20)  and ChIP-seq datasets from ESCs, and formulated the task of identifying TF target genes as learning with the presence of class label noise from all putative proximal and distal target genes. We extended our AdaEnsemble model  (28, 29)  for TF target gene prediction (Supplementary Figure  S1B ). To achieve highly timesensitive and context-specific predictions, we processed the trans-omic dataset into time-resolved gene features from the transcriptome, proteome and epigenome that can be utilized by AdaEnsemble (see more details in 'Materials and Methods' section).
Characterisation of transcriptional networks governed by naive and formative TFs Using the AdaEnsemble-identified TF target genes, we next quantified the degree of overlap in transcriptional networks regulated by the seven TFs using the Jaccard index (Figure  4A ). Consistent with the clustering of epigenomic features (Figure  3B ), transcriptional networks controlled by multiple TFs are segregated into the naive module (i. e. Nanog, Sox2, Esrrb, Nr5a2 and Klf4) and the formative module (c-Myc and Otx2) (Figure  4A ). Within the naive module, 197 of the 824 (∼24%) target genes are regulated by two or more naive TFs (Supplementary Figure  S5A ). For the formative module, 539 target genes are shared between Otx2 and c-Myc, and account for ∼65% and ∼35% of all formative TF target genes, respectively (Figure  4B ).
Precise timing in transcriptional network rewiring during pluripotency progression To reveal the dynamics of core transcriptional networks during the transition between the two pluripotent states, we filtered the AdaEnsemble-identified target genes for those that have a prediction confidence >0. 95 and are themselves TFs (Figure  6A ). As expected, the target genes are largely clustered into two modules with respect to their expression patterns across the timepoints (Supplementary Figure  S7A ). We subsequently reconstructed the dynamics of the time-resolved networks by taking the mean of the expression of each TF-gene pair in the networks (Figure  6B  and Supplementary Table  S4 ). We observe that, during the early time points after induction of differentiation, transcriptional regulation mediated by the naive module dominates the transcriptional networks.
To account for the difference in predictiveness of each epigenomic mark and Pol2, we subsequently incorporated this information as feature weights in a weighted knearest neighbour (kNN) classifier in the augmented learning phase for predicting the final TF target genes. Using public functional datasets, we demonstrated the robustness of our approach by showing that the expression of many predicted target genes is significantly altered with knockdown/knockout or overexpression of their respective TFs (Figure  2E ). While these functional datasets provide additional evidence for predicted TF target genes, they may not be useful for precise validation of all target genes because of secondary effects such as compensation by other TFs as well as the difficulty in determining the optimal time for transcriptome profiling following TF knockdown, knockout, or overexpression.
DATA AVAILABILITY AdaEnsemble is implemented in the AdaSampling R package and is publicly available from [https://CRAN. R-project. org/package=AdaSampling]. DyTN, a shiny application summarising the reconstructed transcriptional networks and visualising their dynamics during the pluripotency progression, can be explored at (http://shiny. maths. usyd. edu. au/DyTN/). Mouse ESC to EpiLC time-course RNAseq, ChIP-seq, and MS-proteomics data were downloaded from Gene Expression Omnibus (GEO) with accession number GSE117896 and PRIDE with accession number PXD010621  (27). Mouse ESC TF ChIP-seq data were downloaded from GEO with accession number GSE44288 for Nanog and Sox2 (83); GSE11431 for Esrrb, Klf4 and Myc (50); GSE19019 for Nr5a2  (49)  and GSE56138 for Otx2  (26). Mouse ESC Pol2-ChIA-PET data were downloaded from GEO with accession number GSE44067  (20).
Mouse ESC Microarray data were downloaded from GEO with accession number GSE4679 for Sox2 KD and Esrrb KD (84); GSE26520 for Nanog KD and Nr5a2 KD (85); GSE9775 for Klf2,4,5 KD (86); GSE56138 for Otx2 KD  (26)  and GSE60344 for Myc overexpression  (87). Mouse.
Motivation: Indoles have been shown to play a significant role in cardiometabolic disorders. While some individual bacterial species are known to produce indole-adducts, to our best knowledge no studies have made use of publicly available genome data to identify prokaryotes, specifically those associated with the human gut microbiota, contributing to the indole metabolic network. Results: Here, we propose a computational strategy, comprising the integration of KEGG and BLAST, to identify prokaryote-specific metabolic reactions relevant for the production of indoles, as well as to predict new members of the human gut microbiota potentially involved in these reactions. By identifying relevant prokaryotic species for further validation studies in vitro, this strategy represents a useful approach for those interrogating the metabolism of other gut-derived microbial metabolites relevant to human health.
In this study, we have developed a computational workflow to predict the contribution of the gut microbiota to the production of disease-related metabolites, here exemplified for indoles. In particular, we used the Kyoto Encyclopaedia of Genes and Genomes (KEGG)  (Kanehisa and Goto 2000) , a popular reference database comprising biological pathways and cellular processes, as a starting point to identify the metabolic reactions relevant for the production of indoles and the bacterial species involved in these reactions. We then used a BLASTP-based strategy  (Altschul, Gish et al. 1990 ) to predict novel key gut microbial players involved in this metabolic network, offering new insights into the prokaryotic metabolism of tryptophan.
Indole metabolic network The MetaboSignal R/Bioconductor package (v. 3. 8) was used to build a KEGG-based tryptophan metabolic network  (Rodriguez-Martinez, Ayala et al. 2017). This network has a bipartite structure, where nodes represent two different biological entities: metabolites and reactions. A reaction-to-metabolite distance matrix was generated to identify reactions directly connected to indole-containing compounds. The tryptophan network was then filtered to include only reactions involved in indole production from tryptophan. The reactions from the indole synthesis network were linked to their corresponding organism-specific genes using a two-step procedure: 1) from reaction to KEGG ortholog (KO); 2) from KO to organism-specific genes. The indole synthesis network was further filtered, by excluding reactions which are not undertaken by human or prokaryotic organisms.
Gut microbial species A dataset of gut microbial organisms was created by merging the list of gastrointestinal isolates reported at the Human Microbiome Project (HMP) Catalog  (Turnbaugh, Ley et al. 2007 ) with additional species identified in cultivation studies (Rajilic-Stojanovic and de Vos 2014,  Browne, Forster et al. 2016 , Lagier, Khelaifia et al. 2016). Data from HMP were downloaded on 12 November 2018; and only isolates with complete sequences and annotation were included. The taxize R/CRAN package (v. 0. 9. 6)  (Chamberlain, Szoecs et al. 2018 ) was used to retrieve the taxon identifier (ID) as well as the phylum of each organism.
BLAST analysis For each KO involved in indole metabolism, a FASTA file of concatenated prokaryotic protein sequences was generated. These FASTA files were used to run BLASTP searches against the non-redundant (rn) protein sequence database on 30 November 2018. BLASTP results were filtered using an E value cut-off of 1x10 -3 and 90 % sequence coverage; only hits with ≥ 95 % identity were retained.