dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION The two AMPs, HG2 and HG4, identified from a rumen metagenomic dataset further confirms the microbial community within the rumen is not only of great importance to the host animal but can also serve as an invaluable resource for urgently needed alternatives to currently available antibiotics. Furthermore, the study presented here emphasises the usefulness of complementing wet-lab and in silico techniques for the rapid identification of new AMP candidates from environmental sample datasets. The low similarity of the newly identified AMPs to previously known sequences suggests their novelty from an evolutionary point of view. Experimental evaluation and characterisation of the antimicrobial properties of HG2 and HG4, two of the identified AMP candidates, revealed their antimicrobial activity against Gram-positive bacteria.
In silico identification of AMPs using computational analysis Antimicrobial peptide prediction and similarity analysis was performed on the rumen metagenomic dataset from the study by ref. 17. The dataset was termed the Library 'Cow' dataset and contains 2,547,270 predicted protein sequences ('metagenemark_predictions. faa. gz') which were downloaded from the weblink (http://portal. nersc. gov/project/jgimg/CowRumenRawData/ submission/). All other datasets (libraries) used for similarity analysis prediction/identification of novel AMP candidates from the 'Cow' dataset and their respective sources are as follows: The Library 'AMP1', which contained a list of 2308 known antimicrobial peptides (AMPs) downloaded from APD2  45  (downloaded on November 10, 2013 and available at http:// aps. unmc.
edu/AP/main. php) and the Library 'AMP2', which contains a list of 48 synthetic AMPs (Hilpert Library) identified by ref. 46. The MATLAB toolbox Gait-CAD and its successor SciXMiner (http://sourceforge. net/projects/ scixminer/)  47  including the Peptides Extension Package  20  were used for the computational analysis unless otherwise stated. The 'fastread' function of the MATLAB Bioinformatics toolbox was used to import the 'Cow' dataset. For easier computational analysis, the imported dataset was then split into 26 parts with ~100,000 sequences each. Following recommendations that small antimicrobial proteins should have a length <200 amino acids (AAs), with most AMPs (>90%) on the APD2 database having a length of <60 AAs  45  , only protein sequences with a maximum length of 200 AAs  19  and not more than 5% unknown AAs (marked by X, *) were selected from the 'Cow' dataset predicted protein sequences (metagenemark_predictions.
To select only promising candidates for AMP predictions, only sequences from the Library 'Cow' with small AA distances AAD <0. 2 or a small AA and AA pair distances AAPD <1. 45 were saved. The distances were computed using the 1-norm (Manhattan norm)  20. The thresholds for AAD and AAPD were heuristically chosen to balance the trade-off between too many weak candidates (too high values of AAD and AAPD) vs. the loss of promising candidates (too low values of AAD and AAPD). All sequences that fulfilled the conditions in the preceding step were collected and some randomly selected hits were used to check similarity in the APD2 database (criteria: small values of AAD or AAPD, different neighbours to explore the variety of the candidates found, short peptide length). Finally, descriptors were computed following procedures described by refs.
Peptide synthesis and three-dimensional modelling of peptide structures Pure (≥95% purity) peptides were synthesised on resin using solid-phase Fmoc peptide chemistry  24  by GenScript Inc. USA. A de novo structural prediction method, PEP-FOLD  48  was used to model the 3D conformation of peptides HG2 and HG4. About 200 simulations were run for each peptide and resulting conformations were clustered and ranked using the sOPEP coarse-grained force field  49. In the case of HG2, the formation of the cysteine bond between Cys10 and Cys18 was imposed as a restraint to the simulation. The best 3D models for each peptide were manually analysed using PyMOL v1. 7. 6  50.
The optical density of re-solubilised biofilms was measured at 570 nm in a microplate spectrophotometer. The percentage biofilm reduction was calculated using the equation below: The plot showing anti-biofilm activity (% biofilm reduction) was generated using the 'ggplot2' package  53  in R (v 4. 1. 1)  54 with log-linear regression performed using lm() function. Percentage biofilm reductionð%Þ ¼ Positive Control À OD of Sample Positive Control À Negative Control.
The supernatant (100 μl) was carefully transferred to a new 96-well microplate and absorbance at 450 nm was measured using a microplate reader. Triton X-100 at 0. 1% (v/v) was used as positive control giving 100% haemolysis. The haemolysis caused by HG2 and HG4 was expressed as a percentage of total haemolysis. The HC 50 values for HG2 and HG4 (i. e. the concentration of peptide causing 50% of haemolysis) were calculated using GraphPad® Prism 7 software.
Critical pressure of insertion was calculated by plotting the variation of surface pressure caused by peptide insertion as a function of the initial surface pressure and corresponds to the theoretical value of initial pressure of lipid monolayer that does not allow the insertion of the peptide, i. e. a variation of pressure equal to 0 mN/m. All experiments were carried out in a controlled atmosphere at 20 ± 1 °C and data were analyzed using the Firmware 2. 5 programme (Kibron Inc. ). The accuracy of the system under our experimental conditions was determined to be ±0. 25 mN/m for surface pressure measurements.
Analysis of sequencing data was carried out using the Geneious Prime® (2021. 1. 1) software (Biomatters Ltd, New Zealand)  59. Firstly, all raw pairedend fastq reads passing quality check by FastQC v0. 11. 9 were mapped to the reference genome (Staphylococcus aureus subsp. aureus USA300_TCH1516 downloaded from the NCBI website), using Geneious RNA-Seq mapper. All generated data have been deposited in the European Nucleotide Archive under study number PRJEB41423. The 'calculate expression level tool' was used to calculate the raw, fragment per kilobase per million counts, transcript counts and their normalised versions Reads per kilobase of transcript per million reads mapped (RPKM), Fragments per kilobase of transcript per million fragments mapped (FPKM) and transcript per million reads (TPM) respectively.
The raw count matrix obtained from Geneious software was then loaded into the R environment  54  and data normalised and transformed to a log10 scale. All functions were run using the default parameters unless stated otherwise. The raw script file used for the analysis can be found at https://github. com/brandonyph/ SaureusRNASeq/. The Gene Ontology (GO) information for each gene was obtained from Uniprot Online Mapping Tools (https:// www. uniprot. org/uploadlists/). Each sample was run through Principal Component Analysis (PCA) using the 'FactoMineR' and 'factoextra' package in R. The analysis was conducted with the pca() function for both the individual genes and the plots were constructed with 'fviz_pca_ind()' and 'fviz_pca_var()'. After the removal of outlier samples, a hierarchical clustering based on normalised gene expression levels was performed on the rows and columns using the 'pheatmap' function in the pheatmap library and the heatmap was constructed on each sample using the same function.
Samples that appear to be outliers in both PCA and hierarchical clustering analysis were removed from the differentially expressed genes (DEGs) analysis step. DEGs for each sample was selected using the DESeq2 algorithm in the Geneious software, with a p value cut-off of <0. 05, and a log2 fold change of abs(log2FC)> 2. The total frequencies of each GO ID in the DEGs list were then summarised using the 'table()' function and visualised with the 'ggplot library' in R.
Statistical analysis All biological experiments were repeated at least three times and three biological replicates were used wherever applicable. Results are expressed as mean ± standard error. The MRSA USA300 LD 50 value was calculated by linear regression using software R v. 4. 11  54. The Kaplan-Meier method was used to plot the survival curves of G. mellonella. Differences in survival were calculated using the log-rank test with the software SigmaPlot (Systat Software Inc. , CA, USA). A p value of 0. 05 was considered to be statistically significant.
ACKNOWLEDGEMENTS This project was funded partly by the  Cross River State Government of Nigeria , the  Life Sciences Research Network Wales ,  RCUK Newton Institutional Link Fund  ( 172629373 ) and the  BBSRC UK  ( BB/L026716/1 ). HCM and DMSB thank the support from  FAPEMIG  and the  Coordination for the Improvement of Higher Education Personnel (CAPES)  for providing the Joint Institutional Links grant. KH thanks the  Institute of Infection and Immunity of St. George's University of London  for a start-up grant. We are also grateful to  Dr. Colin Greengrass , for his advice.
snoRNA 202 transcript was used for normalization. Quantitative PCR was performed according to the manufacturer's instructions and ran on a 7900 HT Fast Real-Time PCR system with 384-Well Block Module (Applied Biosystems, Villebon-sur-Yvette, France). Results were analyzed on SDS 2. 3 software (Applied Biosystems, Villebon-sur-Yvette, France) and expressed as 2 -∆∆Ct values with ∆Ct = Ct miRNA -Ct snoRNA 202.
Ligated miRNAs were reverse transcribed to cDNA using a reverse transcription (RT) primer with integrated Unique Molecular Indices (UMIs), which are molecular tags used to detect and quantify unique mRNA transcripts. After reverse transcription, a cleanup of the cDNA was performed using a streamlined magnetic bead-based method before and after library amplification. Next-generation Sequencing was performed in a single-end mode, 1 × 75 bp, on the NextSeq 500 Illumina System platform with approximately 32 million reads per sample, following the manufacturer's protocols. Reads quality was assessed using FastQC on the FASTQ file. Alignment was performed against the mouse genome (GRCh38/mm10). Raw miRNA counts and differential expression were analyzed using R/Bioconductor package DESeq2. An adjusted p-value < 0.
Statistical Analyses Data are means ± SEMs. t-test with Welch's correction or One-way ANOVA followed by Bonferroni's post-hoc test was used when appropriate to determine significant differences (GraphPad 6. 0 Prism software, San Diego, CA, United States). A p-value below 0. 05 was considered as statistically significant (* or § p < 0. 05; ** or § § p < 0. 01; *** or § § § p < 0. 001; **** or § § § § p < 0. 0001).
Hypertonicity Induces miRNAs Expression KC3AC1 cells were incubated for 6 h under an isotonic or hypertonic medium. Then, TLDA and miRNAs-Seq allowed us to establish a shortlist of five miRNA candidates, the expression of which was induced under hypertonicity with a fold-induction ≥1. 5: miR-135a-5p, miR-28a-3p, miR-30c-2-3p, miR-324-5p, miR-335-5p. We could locate binding sites for these miRNAs in murine Nr3c2 (MR) 3 -UTR using Targetscan/(http://www. targetscan. org/vert_72/, accessed on 19 March 2021)  [45]  and miRmap/(https://mirmap. ezlab. org/app/, accessed on 19 March 2021)  [46]  software. miR-335-5p and miR-324-5p present with one binding site in the Nr3c2 (MR) 3 -UTR, while miR-135a-5p, miR-28a-3p, and miR-30c-2-3p have two distinct binding sites (Figure  1A ). Of note, binding sites for miR-28a-3p are very close, while that of miR-30c-2-3p and miR-135a-5p are near the stop codon and the polyA tail.
Jaspar software  [52]  identified TonE consensus sequence TGGAAANNYNY in regulatory regions of murine mir324 and mir30c2 gene (Supplementary Figure  S4 ), suggesting that TonEBP may regulate the expression of these miRNAs (Figure  8 ). Moreover, we demonstrate that these miRNAs functionally interact with murine Nr3c2 (MR) 3 -UTR, which is conserved among species and presents with the highest number of putative binding sites for miRNAs compared to other transcripts of the renin-angiotensin-aldosterone system  [34]. Indeed, transfection of Mimics for both miRNAs decreased Nr3c2 (MR) 3 -UTR-driven luciferase activity, an effect that was blunted by the corresponding Inhibitors. Given that cooperativity between RBPs and miRNAs had previously been reported  [47] , we transfected 324-5p or 30c-2-3p Mimics together with Tis11b in HEK 293T cells and demonstrated that they cooperate to decrease Nr3c2 (MR) 3 -UTR-driven luciferase activity.
1. The availability of population genomic data through new sequencing technologies gives unprecedented opportunities for estimating important evolutionary forces such as genetic drift, selection, and mutation biases across organisms. Yet, analytical methods that can handle polymorphisms jointly with sequence divergence across species are rare and not easily accessible to empiricists. 2. We implemented polymorphism-aware phylogenetic models (PoMos), an alternative approach for species tree estimation, in the Bayesian phylogenetic software RevBayes. PoMos naturally account for incomplete lineage sorting, which is known to cause difficulties for phylogenetic inference in species radiations, and scale well with genome-wide data. Simultaneously, PoMos can estimate mutation and selection biases.
PoMos implemented in RevBayes Previously, PoMos were implemented in a Maximum Likelihood framework in HyPhy  (De Maio et al. , 2013)  and IQ-Tree  (Schrempf et al. , 2019). Here, we implemented PoMos in a Bayesian framework in the phylogenetic software package RevBayes  (Höhna et al. , 2016)  (Fig. 1 ). The main advantage of our new implementation are extensions of PoMos (described below) and the existing model toolbox of RevBayes which cannot be performed in the Maximum Likelihood framework. RevBayes includes several highly maintained and extensively used routines for phylogeny estimation and hypothesis testing  (Höhna et al. , 2014 (Höhna et al. , , 2016 (Höhna et al. , , 2018 (Höhna et al. , , 2021)). In addition, RevBayes can perform a range of inferencese. g. , phylogeny reconstruction, inference of selection, molecular dating, character evolution, ancestral state reconstruction -that can be easily married with PoMos, allowing the development of newly devised models for varied evolutionary applications.
Implementation, availability and validation The RevBayes source code is freely available at https://github. com/revbayes/revbayes. A detailed tutorial about Bayesian phylogenetic inference with PoMos is available at https://revbayes. github. io/tutorials/pomos/. As critical part of a robust Bayesian workflow, we validated our implementations of PoMos in RevBayes using a simulation-based calibration approach  (Talts et al. , 2018). This method assesses the soundness of a posterior sampler by testing the expectation that if data is generated according to a model, then the Bayesian posterior inferences with respect to that same model are calibrated by construction. In practical terms, if one draws S parameter values from their prior distributions and subsequently simulates S data sets using these parameter values, the inferred α% credible intervals will contain the true parameter value in Sα of times.
Conclusion We implemented PoMos into the widely-used phylogenetic Bayesian software RevBayes. Our implementation allows researchers to use the latest species tree inference models and combine state-of-theart methods in phylogenetics to build robust inferences or hypotheses testing. PoMos are able to perform accurate species tree inference while jointly estimating parameters of the molecular evolutionary dynamic. Together, PoMos offer more complete species tree inference methods that can be useful to characterize both species and molecular evolution. We anticipate that our methods will be particularly interesting to researchers studying genomic data sets including multiple species, populations, and individuals, which are increasingly being produced but still lack appropriate analytical methods.
We confirmed that traction forces exhibited by both cell types increased significantly with increasing substrate stiffness within the investigated stiffness range (Fig. 2d , Fig. S2 ). Similarly, net forces, which are obtained by integrating traction forces across neuronal growth cones, and which provide an approximation for the tension exerted by a growth cone on its axon  (22, 26) , significantly increased on stiffer substrates (Fig  2h ). These data indi-cated that traction forces and net forces assessed by 2D TFM and tether forces measured by OT are not tightly coupled. In order to further examine the interplay between cortical tension and effective membrane tension, we measured tether forces in fibroblasts cultured on 10 kPa hydrogels, where we would expect the highest cytoskeletal tension  (23) , following treatment with the myosin II (and thus contractility) inhibitor blebbistatin.
Traction force microscopy Twenty-four hours after plating, fibroblasts were imaged with an inverted microscope (Leica DMi8) at 37 • C and 5% CO 2 , equipped with a digital sCMOS camera (ORCA-Flash4. 0, Hamamatsu Photonics), an EL6000 illuminator (Leica) and a ×63 oil objective (NA = 1. 4, Leica). Images were acquired with Leica LAS X software. Fluorescence images of beads and wide-field images of cells were taken every 2 min for 10 min. After image acquisition, the culture media was exchanged with Trypsin-EDTA (Gibco) to detach cells from the gel. Reference images of fluorescent beads were taken 15 min after trypsinization. Sixteen to twenty-four hour after plating, Xenopus eye primordia explants were imaged using an inverted Nikon microscope (Ti-E at RT) equipped with an sCMOS camera (Prime BSI Scientific, Photometrics), a CoolLED pE-4000, and ×60 oil objective (NA = 1.
4, Nikon). Fluorescence images of beads and wide-field images of cells were taken every 5 min for 1 h. After image acquisition, the culture media was exchanged with Trypsin-EDTA (Gibco) or 20% sodium dodecyl sulfate solution (Severn Biotech Ltd. , 2 0-4002-01) to detach the explants from the gel. Reference images of fluorescent beads were taken 15 min after trypsinization or immediately after sodium dodecyl sulfate treatment. Traction stress maps were calculated for each frame. Traction stresses were averaged over time (12 frames for neurons, 5 frames for fibroblasts) for each cell. Postprocessing of the data and statistical analyses were done with a custom script in Python 3. 8 and is available on GitHub https://github. com/rg314/pytraction.
Statistics OT data are shown as violin plots, TFM data as box plots. Violin shapes are kernel density estimations, boxes indicate the range of data that falls between the first and third quartile, whiskers show the range of data within 1. 5 times the interquartile range. Density estimator width is based on the number of samples at a given value. Median values for each dataset are marked by a white circle. Lines between violins indicate significant differences in the samples. The boxplot diagrams were generated using python seaborn. The borders of the box indicate the quartiles of the dataset, the median is indicated by a line within the box, and the whiskers include 1. 5×interquartile range. Points that were determined to be outliers were plotted individually. Kruskal-Wallis one-way analysis of variance tests followed by a Tukey post hoc test were used for statistical analysis when comparing multiple groups.
Programming Models & Toolchains Vitis based HLS flow. Vitis  [52]  is the HLS flow designed by Xilinx to cover all the steps required to translate an application, described through a C/C++ program, into a working bitstream running on an FPGA card and communicating with a program running on the host node. The Vitis environment allows emulating the whole system's behavior (host program and FPGA code) by running and debugging the C/C++ code in a standard IDE. Once the functional correctness has been achieved, i. e. , the program produces the expected results, the HLS engine translates the pieces of C/C++ code mapped on the FPGA into equivalent, optimized, hardware implementation. The communication with the external world (host node, memory banks) is achieved through a presynthesized layer implementing the interfaces (PCIe, DDR and HBM memory banks) and the FPGA resources are accessed from the host node through the runtime and APIs developed by Xilinx.
Vitis is largely customizable thanks to the opensource LLVM-based front-end  6  and many open-source accelerated libraries (e. g. , math, video processing, AI, signal processing)  7. In the TEXTAROSSA project, the Vitis environment will be extended with multi-precision arithmetic, allowing the usage of new data types based on the Posit format, and with a communication library used to perform inter-FPGA direct communications. The inter-node communication layer, namely MPI, will also be updated to transfer data without requiring memory copies between FPGAs and hosts. Furthermore, the APIs to access the FPGA will be used to build the TEXTAROSSA APIs that will be defined to access homogeneously the accelerators. Such extensions will require the integration into the Vitis flow of the new IPs defined in the project, thus we will develop the hardware modules, their C++ functional models and we will encode all the necessary info needed by the HLS to properly manage (i.
e. , schedule and connect) the new functionalities. StarPU, OmpSs. Task-based programming models (like OmpSs 8   [43]  and StarPU 9   [15] ) address the challenges to program on heterogeneous computing nodes while achieving high productivity by providing higher-level abstractions that could help the programmer to generate high-performance code. It has been demonstrated  [23]  that task-based programming models can simplify FPGA programming by making the memory allocation and data copies automatic and automating the code generation of the CPU and FPGA binaries, provided the C/C++ implementation, by transparently running open or vendor tools. Performance can be subsequently improved by using high-level tools like tracing directly from inside the FPGA  [23]. In addition, these programming models allow the use of parallelism based on tasking (instead of kernel invocations) and can even provide support for data-dependent tasks and manage the execution based on such data dependencies  [66].
We plan to move part of the runtime work to a fast hardware task scheduler  [66] ,  [40]  to further enhance the performance of these programming models. It has already been shown  [40]  that these improvements can lead to obtain top performance of some applications in FPGAs directly from high-level language programs. In addition, we plan to extend the range of programs that demonstrate this optimum results with the tools used by further adapting the environment to the TEXTAROSSA platforms. New features obtained from these platforms will then be ported back to framework in order to benefit the whole range of systems targeted by the tools. Training Deep Neural Network (DNN) is a memory-intensive operation. Indeed, the training algorithms of most DNNs require to store both the model weights and the forward activations in order to perform back-propagation.
e. , parallel patterns) modeling recurrent schemas of parallel computations (e. g. , pipeline, task-farm, parallelfor, etc. ). FastFlow efficiency stems from the optimized implementation of the base communication and synchronization mechanisms and its layered software design. Besides, stream processing is the natural paradigm for event-driven distributed applications that need to communicate with each other via message passing. Finally, some data streaming paradigms are naturally suited for implementation on reconfigurable platforms  [56] , e. g. the dataflow/actor paradigm. In TEXTAROSSA, we aim at exploiting reconfigurable platforms to accelerate HPDA tasks leveraging the FastFlow framework  [7]. Streamflow. The StreamFlow framework  [36, 37]  is a container-native Workflow Management System (WMS) written in Python 3 and based on the Common Workflow Language (CWL) Standard  [14].