dataesr/scientific-paragraphs-categorization huggingface dataset:
We have previously shown in this model that IRF4 and MAFB are necessary for mo-DC and mo-Mac differentiation respectively  (Goudot et al, 2017). Their expression appeared largely exclusive, with IRF4 expression highest in cluster 3 while MAFB was mainly expressed in clusters 1, 4, 5, and 6 (Fig  1E ). These results suggest that clusters 1 and 2 correspond to undifferentiated monocytes, while cells from other clusters express either a DC-or a macrophage-oriented transcriptional program starting after 3-9 h. To study differentiation trajectories, we analyzed RNA velocity using scVelo  (Bergen et al, 2020). This method estimates the kinetics of mRNA splicing in single-cell transcriptomes, in order to reconstruct cellular dynamics. Inference of the velocity pseudotime using scVelo was able to reconstruct independently the temporal sequence of the dataset (Fig EV1I ), confirming the robustness of this approach.
Identification of candidate transcription factors involved in monocyte differentiation To identify transcriptional regulators of the early steps of monocyte differentiation, we used our human scRNA-seq dataset and analyzed differentially expressed genes between the clusters showing a commitment towards mo-DC versus mo-Mac, and extracted genes annotated as transcription factors. We focused on the comparison A CD45. 2 + monocytes were injected in CD45. 1 + recipient mice previously injected with thioglycolate. Peritoneal lavage was analyzed at 3, 9, 24, 48, and 72 h post transfer (n = 4 biological replicates). B UMAP displaying CD45. 2 + Lineage À Ly6G À Tim4 À peritoneal cells analyzed by flow cytometry and concatenated from all time points. C UMAP of cells from each time point. D Expression of phenotypic markers used for UMAP projection.
In addition, network analysis suggested that IRF1 controlled the expression of several ISG expressed in our dataset  (Fig 4F). Therefore, we sought to address whether IRF1 may impact mo-Mac differentiation through the regulation of ISG expression. To confirm the expression of ISG during monocyte differentiation in vitro, we measured by RT-qPCR the expression of the ISG MX1, IFIT3, and CXCL10 after 9 h of culture with different cytokine combinations  (Fig 4G). We found that ISG expression were significantly induced upon exposure to M-CSF, with TNF-a decreasing ISG expression. To directly address the effect of ISG on monocyte differentiation, we used a soluble type I interferon receptor (B18R) which acts as an inhibitor of interferon signaling  (Colamonici et al, 1995). We validated the inhibitory effect of B18R on the expression of MX1, IFIT3, and CXCL10 (Fig  4G ).
Single-cell RNA-sequencing analysis Initial processing was done using CellRanger (v3. 1. 0). Starting from the filtered gene-cell count matrix produced by CellRanger, we proceeded with the Seurat v4. 0 workflow  (Hao et al, 2021)  for demultiplex cells based on hashtag barcode and quality control (QC) processing. Briefly, we filtered out cells with mitochondrial genes < 20%, as well as genes expressed in less than 3 cells. Hashtag demultiplexing was performed with the function HTODemux() and positive. quantile = 0. 99. Integration of the 8 samples (2 donors, 4 time points) was subsequently performed with the STACAS workflow  (Andreatta & Carmona, 2021). Briefly, normalization and identification of variable features was performed with standard Seurat pipelines and then anchors were identified with FindAnchors.
STACAS (. , dims = 1:30, anchor. features = 5,000) and filter with FilterAnchors. STACAS (. , dist. thr = 0. 8). The order for integration was calculated with SampleTree. STACAS(). Finally, integration was performed with the filtered anchors using the function IntegrateData(). Downstream analysis, graph-based clustering, visualization and differential gene expression analyses of the scRNA-seq data were performed using Seurat v4. 0. For clustering analysis, FindNeighbors() and FindClusters() functions of the Seurat package were used with the first 50 significant PCs and a resolution of 1. 3, respectively. For signatures enrichment, we used the function AddModuleScore(). For identification of DEGs, we used the Find-Markers or FindAllMarkers function (test. use = 't', logfc. threshold = log[0.
RNA velocity Annotations of unspliced/spliced reads were obtained using velocyto CLI with default parameters (La  Manno et al, 2018). Next, unspliced counts were merged with the preprocessed, normalized, integrated and annotated spliced count matrix (Seurat v4. 0) and the RNA velocity analysis was performed using scVelo (v0. 2. 4) workflow  (Bergen et al, 2020). Briefly, we computed moments for velocity estimation and then the stochastic model to learn the transcriptional dynamics of splicing kinetics, transcriptional state, and velocity pseudo-time across the complete dataset.
1), anti-CD11c PeCy7 (BD Bioscience, clone N418), anti-MerTK PECy7 (Biolegend, clone 2B10C42), anti-ESAM APC (Biolegend, clone 1G8/ESAM), anti-CD115 APC (BD Bioscience, clone AFS98), anti-CD115 APC-Fire750 (BD Bioscience, clone AFS98), anti-TIM4 BB700 (Biolegend, clone RMT4-54), anti-CD43 BB700 (BD Bioscience, clone S7), anti-CD135 APC (Biolegend, clone A2F10), anti-Ly6C Alexa 700 (Biolegend, clone HK1. 4), anti-MHC II APC Cy7 (Biolegend, clone M5/ 114. 15. 2) and anti-ICAM2 FITC (Biolegend, clone 3C4). Data were analyzed with FlowJo (FlowJo LLC). Dimension reduction and clustering were performed in FlowJo using UMAP and Phenograph  (Levine et al, 2015)  modules, respectively, with default settings. For trajectory analysis, flow cytometry data was exported to R to perform Slingshot pseudo-time analysis  (Street et al, 2018).
Acknowledgements This work was funded by  INSERM, Institut Curie  ( CIC IGR-Curie 1428 ), the  Agence Nationale de la Recherche  ( ANR-10-LABX-0043 ,  ANR-10-IDEX-0001-02   PSL , and  ANR-17-CE15-0011-01 ). JV is a fellow of the  IC-3i PhD program  supported by the  ERC Horizon 2020-Marie Sklodowska-Curie Actions  (grant # 666003 ) and of the  Fondation pour la Recherche M edicale  ( FDT202106013025 ). The authors wish to thank the  Next Generation Sequencing Platform , the  In vivo experiments Platform , and the  Flow Cytometry Core of Institut Curie. The authors wish to thank  J. Waterfall  and L. P eri e for helpful advice, and  S. Heurtebise-Chr  etien for managing the CD45. 1 mouse colony.
Data availability The sequencing data from this publication have been deposited to the GEO database (GSM6746000; https://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSM6746000) and assigned the identifier GSE218483 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE218483). The flow cytometry data from this publication have been deposited to the Flow repository database (http://flowrepository. org/id/ FR-FCM-Z678 and assigned the identifier FR-FCM-Z678). Expanded View for this article is available online.
Data evaluation and statistical analyses All clinical and experimental patient data were implemented into a database program (FileMaker Pro) for further analysis. Information about all individual patients was used to compare all defined subgroups and to perform statistical analyses. Chi-Square distribution analyses were performed by using the following website: www. mathsisfun. com/data//chisquare-calculator. html. The database offers the possibility to analyze specific patient cohorts and we will share these data with requesting scientists.
The study cohort To analyze the recombinome of the human KMT2A gene, prescreened and not-prescreened acute leukemia samples were obtained between 2003 and 2022. As described in Methods, patient genomic DNA was either analyzed by PCR or targeted NGS to obtain the genetic information of rearranged KMT2A fusion alleles. For most of the investigated cases, we obtained all clinical information (gender, age at diagnosis, disease type and subtype or information about de novo or secondary leukemia), which is necessary for subsequent data processing. Patients for which we were unable to obtain the relevant clinical data, or which were KMT2A-r negative in our analyses were excluded from the present study. The results of the remaining 3401 patients are summarized in Fig. 1  and Table  1.
Breakpoint distribution according to clinical subtypes We also investigated also the breakpoint distribution of the KMT2A recombinome. The major breakpoint cluster region (BCR1) can be mapped between KMT2A intron 7 and KMT2A exon 13, and the minor BCR (BCR2) between intron 20 and exon 24. The majority of patients (n = 3336; 98%) showed breakpoints in BCR1 while a minority (n = 47; 1,4%) was found in BCR2. When restricting this distribution analysis only to our NGS data, then the distribution between BCR1 and BCR2 is 94% vs. 6%, respectively. The remaining breakpoints (n = 17; 0,6%) were found up-stream of BCR1 (n = 4), between BCR1 and BCR2 (n = 8), and downstream of BCR2 (n = 5) (see Fig. 4 ). We also analyzed the data according to the leukemia phenotype AML or ALL. While ALL breakpoints are found in BCR1 and BCR2, breakpoints in AML patients nearly exclusively occur in BCR1.
Since a given KMT2A fusion allele is genetically stable and a monoallelic marker for each tumor cell, a more reliable quantification and tracing of residual tumor cells becomes feasible. For each of the 3401 acute leukemia patients at least one KMT2A fusion allele was identified and characterized by sequencing. Several prospective studies were already initiated and first published data verified the reliability of these genomic markers for MRD monitoring  [4] [5] [6] [7]. Therefore, the use of these MRD markers will in the future contribute to a better stratification of leukemia patients which in turn will help to further improve their outcome. In particular, in infant ALL MRD monitoring has a high impact in the outcome of the patients. For the majority of KMT2A TPGs identified so far, a systematic classification about their function(s) has recently been described in comprehensive detail  [76].
ACKNOWLEDGEMENTS We thank all local physicians and biologists who provided clinical information and material. This work was made possible by and conducted within the framework of the  International BFM Study Group. RM was supported by grants from the  DFG  ( Ma 1876/12-1 ) and  Wilhelm Sander  foundation ( 2018. 070. 2 ). BAL received a sponsorship of renewed research stay in Germany from the  Alexander von Humboldt Foundation. TB was supported by  Deutsche José Carreras Leukämie-Stiftung  grant  13 R/2016. JT and JZ are supported by grant  LX22NPO5102  from the  Next Generation EU NICR.
A finger length perception task, a continuous version of body size estimation  [16, 59, 60] , was used to measure body image. The task was performed using a graphical user interface displaying a picture of the participant's right hand and a slider (Fig  1 ). This GUI was presented on a laptop (Lenovo ideapad 100S; screen size: 14"). A picture of the participant's left hand was taken at the start of the experiment against a white backdrop with ambient light from above (no directional light to prevent hard shadows) using a web camera (F/#2. 0; f: 4. 8mm -1). The GUI was developed by the first author using python and the OpenCV library (available upon request). The "target" window (Fig 1A ) separating the index finger from the rest of the hand was positioned and sized by the experimenter at the beginning of the experiment, just after the picture of the hand was taken.
Participants were compensated 16€ for their time. Analyses. In each task, we quantified finger elongation by subtracting the baseline score from the mean score in the respective condition. A single multivariate outlier (>M+3 SDs in 3 conditions) was identified and removed from the dataset. We tested the effect of each condition on each task relative to baseline with a repeated measures ANOVA followed by post hoc analyses, employing two-tailed paired-samples t-tests (both frequentist and Bayesian). In post hoc tests, the Holm-Bonferroni correction was applied to p values. We also computed and compared the correlations (Pearson's r) between perceptual and pointing effects in the different conditions using the cocor library  [71]. In Bayesian post hoc tests, we used default priors corresponding to a Cauchy distribution centred on zero with a width of.
for a Bayes factor B = 10, we report the smallest and largest prior widths for which B>3). Hence, no RR are reported for B between 1/3 and 3. Prior width were varied from 0 to 2, which covers the reasonable range of prior distributions. We use p-values, effect sizes, and Bs for interpreting significant effects but only effect sizes and Bs for interpreting non-significant effects. Figures were generated using Python 3, and the matplotlib and seaborn libraries  [74]. Data are publicly available on the Open Science Framework (https://osf. io/jxek3/). No part of the study procedures or analyses was pre-registered prior to the research being conducted and thus the analyses can be considered exploratory.
This research was supported by  ARCHE  formation (scholarship of A. C. and experimental expenses, no grant number, https://www. archehypnose. com/), FrontCog ( ANR-17-EURE-0017  FrontCog, https://www. psl. eu/en/front-cog) and PSL ( ANR-10-IDEX-0001-02  PSL, https://psl. eu/ en). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
| INTRODUC TI ON Aquatic ecosystems are experiencing a rapid decline in fish biodiversity and fisheries stocks due to escalating anthropogenic pressures, such as habitat degradation, invasive species, overfishing, and climate change  (Lotze et al. , 2019; Reid et al. , 2019; Su et al. , 2021). As secondary consumers and top predators, fishes are among the most ecologically important taxa in aquatic ecosystems, exerting strong influences on the population dynamics of both their predators and prey. Accurate and timely assessments of fish diversity and population dynamics are therefore essential to support databases needed for conservation and management decisions  (Pereira et al. , 2013). Over the past two decades, environmental DNA (eDNA) monitoring approaches have expanded rapidly and are now frequently used worldwide to study fish populations  (Deiner et al.
, 2021). However, this approach increases non-specific target amplification. For example, DNA metabarcoding of sedDNA using universal fish primers (e. g. , tele01,  Valentini et al. , 2016)  and an annealing temperature of 56°C reported dominance of non-target sequences from plants and bacteria  (Huang et al. , 2021). Overall, DNA metabarcoding can provide data on the taxonomic composition of a target community. However, metabarcoding of sedDNA can be | 1463 HUSTON et al. sensitive to PCR-related errors, pseudogene contamination, primer bias, and universality issues  (Lopez et al. , 2021). Last, the lack of a comprehensive and accurate reference database needed for taxonomic assignment also limits the data derived from this approach  (Keck, Blackman, et al. , 2022; Keck, Couton, & Altermatt, 2022).
| Metagenomics (shotgun sequencing) Metagenomics is a promising method for detecting fish sedDNA, particularly when an enrichment step is employed (e. g. ,  Armbrecht et al. , 2021)  Vertebrates Genome Project have the potential to fill these gaps, enhancing metagenomic databases and boosting the possibility of genomic approaches in the next future  (Theissinger et al. , 2023). Hybridization capture is a technique in metagenomics that relies on the creation of in silico RNA baits -short oligonucleotides designed to be complementary to nuclear, mitochondrial, or chloroplast genomes or gene regions of any target species. When mixed with eDNA extracts, baits hybridize with matching DNA fragments, and the resulting complexes are purified, enriched, and sequenced to generate more targeted biological insights.
Combining short-and long-strand sequencing of environmental metagenomes may also be an alternative, allowing thorough investigation of fragmented and thus poorly preserved DNA from the sediments, but can be costly and time-consuming  (Pedersen et al. , 2016). Importantly, all these methods are dependent upon adequate DNA sequence resources and reference databases  (Monchamp et al. , 2023). Overall, further research is required to meet ecological and methodological challenges, but in the meantime, it is essential to consolidate best practices from existing workflows to enable higher success in detecting fish DNA from sediment samples.
| FUTURE DIREC TIONS In the coming years, we expect major improvements in various as-  (5) expansion of efforts to improve the accuracy and augment fish DNA reference sequence database resources; (6) integration of fish sedDNA studies with indigenous and local ecological knowledge; (7) expansion of fish sedDNA studies to more diverse regions and communities to help identify the best methods for particular settings and contexts; (8) development of analytical approaches to better statistically account for incomplete detection and time scale biases; and (9) standardization of sampling, metadata processing, and reporting to increase the confidence and repeatability of fish sedDNA-inferred biological data. Knowledge of fish sedDNA dynamics in natural ecosystems is in its infancy. The present review lays the groundwork for the successful integration of fish sedDNA in ecological and conservation studies by providing an overview of the critical ecological and methodological factors affecting fish sedDNA analysis.
Cawthron Report XXX. In preparation. b Duxbury, L. C. , Pérez Godoy, V. , Cadd, H. , Tyler, J. J. , Francke, A. , Law, W. B. , & Armbrecht, L. (in prep). Lake sedimentary ancient DNA reveals ecosystem response to fire and climate on Kangaroo Island (Karti), Australia. c Kurte, L. , Pérez, V. , Quezada-Romegialli, C. , Yichen, L. , Wei, W. W. , Kessler, A. , … Sintern, A. (in prep). Ancient DNA analysis from Gippsland Lakes sediment cores: impact on publish database biases. d King, L. , Brothers, S. , & Brahney, J. Fish sedDNA from Goshen Bay, Utah Lake. Unpublished. TA B L E 1 (Continued) 26374943, 2023, 6, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/edn3. 467 by Paul Scherrer Institut PSI, Wiley Online Library on [06/02/2024]. See the Terms and Conditions (https://onlinelibrary.
Definition of terms used in the review. Also called exact sequence variants (ESVs). 'True' DNA sequences (reads) are identified and separated from false reads (chimeras) by an algorithm during the bioinformatic pipeline of a sequencing run. As opposed to Operational Taxonomic Units (OTUs), ASVs are not study-specific groupings, and a clustering algorithm based on sequence similarity is not employed. If the same bioinformatics parameters are used, ASVs can be compared and merged across sequencing runs, studies, and databases Digital PCR (dPCR) Method of amplifying and quantifying DNA concentration. Unlike qPCR, in dPCR the PCR reaction is split into thousands of individual reactions prior to amplification, allowing for more precise quantification. Quantification of the PCR product is measured at the end of the reaction (end-point PCR) Also called next-generation sequencing (NGS) -the term is used to describe technologies that sequence large numbers of DNA fragments in parallel, encompassing both second and third-generation sequencing to target short and long-read lengths Metabarcoding Large-scale taxonomic detection of multiple species from an environmental sample via high-throughput sequencing of a targeted gene fragment (amplicon).
GPH, MTK, and JES were supported by the NSF EPSCoR RII Track-1 Maine-eDNA program (#OIA-1849227) and the Maine Agricultural and Forest Experiment Station. MLL is supported by the Liber Ero postdoctoral fellowship program and Genome Canada, Genome British Columbia, and Genome Québec large-scale applied research project #312ITD awarded to CCH. EC was supported by the Severo Ochoa Excellence Program postdoctoral fellowship (CEX2019-000928-S). MP, GTL, and SAW were supported by the New Zealand Ministry of Business, Innovation, and Employment research programme -Our lakes' health: past, present, future (C05X1707). LA is funded by an Australian Research Council Discovery Early Career Researcher Award (ARC DECRA DE210100929). LCD is supported by a research internship from the ARC Centre of Excellence for Australian Biodiversity and Heritage (CABAH).
LKP is supported by the Australian Research Council (ARC), Centre of Excellence for Australian Biodiversity and Heritage (EpicAustralia,org. au), and by a 2018 Monash University Interdisciplinary Research Seed Funding Scheme and Núcleo Milenio INVASAL grant NCN2021-056 funded by Milenio program at Ministerio de Ciencia, Technología, Conocimiento & Innovación. MKS was supported by Grants-in-Aid for.
Adsorption-based techniques for gas separation using nanoporous materials are widely used and hold a promising future, but systematic identification of the bestperforming materials for a given application is still an open problem. For that task, we need to estimate selectivity at different operating conditions (temperature, pressure) on a large set of nanoporous structures. To this aim, we have developed a machine learning-assisted screening process based on a fast grid calculation of interaction energies, in addition to newly designed geometrical descriptors to predict ambient-pressure selectivity. As a proof of concept, we tested our methodology for the separation of a 20:80 xenon/krypton mixture at 298 K and 1 atm in the nanoporous materials of the CoRE MOF 2019 database. Based on a train/test split of the dataset, our model is promising with an RMSE of 2.
, a synergistic workflow combining computational discovery and experimental validation can push material discovery to the next stage. 7, 8  But to efficiently guide experimental discoveries, computational chemists are facing two major challenges: generating reliably more structures and evaluating them with fast and accurate models. The number of nanoporous materials is potentially unlimited; for the metal-organic frameworks (MOFs) alone, over 90 000 structures have been synthesized  9  and 500 000 computationally constructed  [10] [11] [12]. This ever-increasing amount of structures requires more efficient screening procedures as well as faster evaluation tools. To go beyond the time-consuming calculations over the whole dataset, computational chemists developed funnel-like screening procedures to reduce the need for expensive simulations and introduced machine learning (ML) models.
The comparison between the low and high pressure cases provides clarifications on the origin of the differences in selectivity values. For some materials, selectivity could drop when increasing the pressure in the Xe/Kr separation application. And, this was mainly attributed to the presence of different pore sizes and potential reorganizations due to adsorbate-adsorbate interactions. 18  In this article, we develop a new adsorption energy sampling technique using a gridbased approach. Moreover, we perform a statistical characterization of the pore size and energy distributions to inform the model on a potential selectivity drop. By combining these two approaches, we introduce a set of useful ML descriptors for fast and accurate ambientpressure selectivity prediction, and we highlight its performance in the case of xenon/krypton separation for the CoRE MOF 2019 database  19.