dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Genomic data offer new opportunities for genetic diversity management in composite breeds. Our results show that diversity management based on genomic data can be used in the first generations of a new line to build diversity even before starting selection. In the case of two step three-way crossbreeding, it was clearly better to start the selection after 4 generations post-crossbreeding to limit inbreeding, however it is difficult to extend this recommendation to all kinds of crossbreeding strategies. In any case, simulations based on real data collected from the base crossbred population should be carried out to design an appropriate breeding strategy that combines selection and diversity management. Among the breeding scenarios studied here, a strategy that includes a focus on allele frequencies, such as the BV + genomic data with relaxed ORI would be a good compromise between genetic progress and diversity conservation.
Introduction Identification of mosquitoes is a matter of public health. Numerous mosquitoes are proven vectors of human or zoonotic arboviruses, such as dengue (DENV), chikungunya (CHIKV), West Nile (WNV) or Usutu (USUV). Recently, Southern Europe suffered autochthonous dengue epidemics  [1]. These highlight the need for rapid vector identification, surveillance and control. Morphological methods, initially used for the description of original species and their comparisons, are the main means to quickly identify mosquitoes. They rely upon dichotomic/polytomous keys, illustrated simplified keys and interactive keys  [2]. The latter, with regard to the European fauna, were firstly developed in 2000  [3]  and were recently updated using the Xper2 software  [4] , leading to  MosKeyTool [5]. This interactive identification key for mosquitoes of the Mediterranean region requires updates on fauna composition and morphological data, but also well-preserved specimens analyzed by expert personnel.
While such morphological tools are very helpful, their routine use can turn out to be time-consuming. With the advent of molecular biology, molecular tools were developed in order to accurately identify mosquito species. Mostly based on barcoding techniques (analysis of the cytochrome oxidase I gene)  [6] , the sequencing and comparison of sequences with online databases (GenBank, BOLD) provide a reliable identification method  [7, 8]. However, some cryptic species like those of the Culex pipiens complex require further analysis of the ACE2 (acetylcholinesterase) gene and microsatellites to achieve accurate identification  [9, 10]. In addition to barcoding techniques, more precise molecular tools were developed in order to identify mosquitoes belonging to the same species complex. For instance, the multiplex allele-specific PCR technique was used to diagnose similar Aedes mosquitoes from the Stegomyia subgenus  [11]  and mosquitoes from the Anopheles gambiae and Anopheles barbirostris complexes  [12, 13].
Sequences were compared to existing GenBanK sequences with the BLAST algorithm  [43]  and identification was considered accurate above a 99% similarity. Pictures were taken using the Stream Essentials software version 1. 7 and a DP-26 video camera connected to a SZX10 stereomicroscope (Olympus, Tokyo, Japan). All specimens were captured with a X2 magnification. Pictures were saved in JPEG format, and the work files were built with TPS Util © version 1. 76. In total, 18 landmarks were manually digitized by one of the authors (JPM) with TPSDig © version 2. 31 [44], as shown in Figure  2. Error assessment: In order to evaluate the error in landmark digitization, we performed a Pearson correlation test on a subset of 76 randomly chosen pictures digitized twice by the same operator (JPM). Landmark analysis: Coordinates of the 18 landmarks were imported in RStudio software (version 1.
2. 5019)  [45]  and processed within the geomorph package (version 3. 2. 1) [46]. Coordinates were aligned by performing Procrustes superimposition (Figure  3 ). The mean positions of the landmarks per species are shown in Figure  4. Plots exported from R were made with the generic plot function. Coordinates in TPS format were imported in MorphoJ software version 1. 07a  [47]. Multivariate regression over the Procrustes coordinates was performed in order to evaluate the allometric influence of size over shape. Canonical Variate Analysis (CVA) was applied on the coordinates and Mahalanobis distances were computed to study the similarity between species. Pairwise cross-validated species reclassification tests with 1000 permutation runs were conducted. This test aims to quantify the rate of correct reclassification between samples.
Canonical Variate Analysis Canonical Variate Analysis performed on our dataset accounted for 86. 73% of the total variance on the first two canonical variates. The specimens from the six species here belong to four subgenera: Ae. albopictus belongs to the subgenus Stegomyia, Ae. japonicus to the subgenus Finlaya, Ae. cinereus s. l. to the subgenus Aedes, Ae. cantans, Ae. rusticus and Ae. sticticus to the subgenus Ochlerotatus. Figure  5  shows a relative clustering between the Stegomyia and Aedes subgenera. Species appear to be well segregated with low overlapping. The pairwise cross-validated species reclassification test shows an accuracy of 98%. The detailed pairwise cross-validated species reclassification test is available in Table  2. A neighbor-joining tree was performed on Mahalanobis distances between these species (Figure  6 ).
cantans  [19, 55]. Our results are in accordance with other studies performed in Europe. Due to all the morphometric literature, researchers are steadily building a database of wing patterns. It would be interesting if all this worldwide data could be merged in order to create a global catalog of mosquito wing patterns. As some authors have shown, the landmark disposition of two geographically isolated mosquito populations from the same species can show pattern variation  [28]. Nonetheless, such large databases could be of help to create worldwide tools for mosquito identification. GM is a valuable tool to prepare for the emergence of arboviruses. Exhaustive databases could be built and made available to that end. Integration of GM tools into identification software (such as MosKeyTool) could help ease the process of identification, allowing beginner field entomologists to make accurate identifications, and confirmed entomologists to save valuable time in the case of an epidemic event.
faecalis and 1981 of E. faecium from the NCBI database. We used the ncbi-genome-download command with default parameters to download all available genomes (in fasta format) of E. faecalis and E. faecium, including their metadata. Only released genome sequences available for downloading were retrieved. Then, we thoroughly checked the metadata and excluded all the known contaminated sequences. Generally, the E. faecalis and E. faecium draft genome size is between 2. 2 and 3. 5 Mb (including plasmid sequences). In our analysis, whole genome sequences above 4 Mb with a percentage of aligned sequences (mauve_Aligner) < 50% were also excluded from our analysis. We re-annotated all 3572 genomes of the two species, including genomes sequenced from Marseille (strains G823, G824, G881, G882, G883 and G884 with assembly accession numbers FPDY01, FPDW01, FPEB01, FPDZ01, FPEC01 and FPEA01, respectively), with Prokka  [27]  using Pfam and the Swissport database and default parameters.
Genome Phylogenetic Tree Reconstruction of Both E. faecalis and E. faecium We performed a whole genome SNP alignment of the 1591 whole genomes of E. faecalis and the 1981 genomes of E. faecium using Scapper (https://github. com/tseemann/scapper, accessed on 10 January 2021), with default settings, to reconstruct the whole genome phylogenetic tree. The strains of the E. faecalis ATCC 29212 (NZ_CP008816. 1) and E. faecium DO (NC_017960. 1) genomes were used as references, respectively. Aligned multi-fasta were shrunk using SNP-sites  [28]  to remove the monomorphic sites, and so reduce the phylogenetic inference computing time. We inferred approximately maximum likelihood phylogenetic trees from shrunken alignments of nucleotide sequences using RaxML  [29] [30] [31]  and used them to adjust the seed parameter to 2000 for a reproducible and consistent tree topology.
Detection of Recombination Hotspots inside the Genomes of E. faecalis and E. faecium We putatively identified base substitution and recombination loci containing elevated densities of base substitutions suggestive of horizontal transferring sequences. We constructed a maximum likelihood phylogenetic tree based on the putative point mutations outside these regions of high sequence diversity using ClonalFrameML (version v1. 0-19)  [32]. We reconstructed the evolutionary maximum likelihood phylogeny by determining genetic genealogy and considering points of variation and genome plasticity and then generated a genome recombination heatmap against phylogenetic trees. 2. 4. Detection of Clustered, Regularly Interspaced, Short Palindromic Repeat (CRISPR) Spacers inside the E. faecalis and E. faecium Genomes Detection of a CRISPR-Cas system (spacers, repeats) was conducted using MinCED software (v0.
Orthologous Gene Detection and Pan-Genome Analysis of Both Species The pan-genome analysis was performed using Roary  [34]  (version 3. 6. 8) with default parameters for orthologous gene similarity (BLAST: 95% minimum identity; E-value = 1e -5 ), and genes had to be present in >99% of all isolates to be included in the hard-core genome. We determined core (hard-core) (genes present in 99-100% taxa), softcore (genes present in 95-99% taxa), shell (genes present in 15-95%) and cloud genes (genes present in 0-15% genomes) as described by Kaas  [35]. We generated a binomial pan-genome profile for the presence, indicated as (1), and absence of genes, indicated as (0), inside the genome of both species. Orthologous genes related to antimicrobial, endonuclease and anti-endonuclease gene distribution were extracted using homemade scripts and plotted against maximum likelihood phylogenetic trees in both species.
Statistical Analysis All statistical analyses conducted in this study were performed using R statistical software (v. 3. 4. 4) [36]. We used the Student's t-test for means' comparison, and the Pearson chi-squared test was used for proportion comparison inside and between the two species. The Pearson correlation test was used to show a statistical association between two genomic features. A logistic regression analysis was used to compute the association between qualitative genomic variables (presence and absence of vancomycin genes and the number of recombination and CRISPR spacers detected inside and across the genomes of both species). The odds ratio was calculated to interpret the association. We set the CI level to 95%. The statistical test was significant at a p-value of < 0. 05. All p-values below 0.
Mass spectrometry and GO analysis Half of the native protein extract was pre-treated with 0. 625 U/µL Benzonase (Millipore, E1014) at 37°C for 30 minutes. The reaction was stopped with 5 mM EDTA pH 8. 0 and Protein A immunoprecipitation was performed in the same manner as for RNA immunoprecipitations. After washes with lysis buffer, beads were washed with 0. 1 M NH4OAc and 0. 1 mM MgCl2 and eluted with 0. 5 M NH4OH for 20 minutes at room temperature. Eluates were lyophilized and subject to in-solution tryptic digestion. LC-MS/MS analysis was performed by the IRCM Proteomics Discovery Platform on a Q Exactive HF. Raw data was processed and analyzed using the MaxQuant software package 1. 5. 1. 2  60 and the fission yeast UP000002485 reference proteome (24/11/2019). Settings used for MaxQuant analysis were as reported  61.
Proteins present in immunoprecipitations from both biological replicates and absent from control immunoprecipitations were considered genuine Bin3 interacting partners and ranked by peak intensity. PANTHER overrepresentation tests (GO Ontology database released 2019-12-09) were conducted on the top 50 Bin3 interacting partners. Fisher's exact tests with FDR correction were performed using the GO biological process complete and GO cellular component complete data sets.
When a cluster contained two or more paralogous genes derived from duplications, the species was removed from the cluster. Finally, only clusters with a minimum of 100 species were retained for further analysis. Following these cleaning steps, we obtained 5328 clusters containing one gene per species. Proteins sequences of each cluster were aligned using MAFFT (v7. 271) with default parameters (https://mafft. cbrc. jp/alignment/server/). Multiple alignments were cleaned using trimA1 (v1. 4. rev22 build [2015-05-21]) with -st 0. 01 option. Fastree (version 2. 1. 10 SSE3) with default parameters were used to build phylogenetic trees  67. The consensus fungi tree was obtained using ASTRAL (astral. 5. 6. 3) software with default parameters 68. To detect the presence of genes coding for Bin3-like and Pof8-like proteins, blast searches (blastp and tbalstn) were performed on each studied fungus genome, using initially either the full-length S.
Acknowledgements: We thank  Toru Nakamura  and  Virginia Zakian  for sharing published yeast strains and  Marc Fabian  and  Raymund Wellinger  for helpful comments on the manuscript. J. P. is supported by a  Canada Graduate Scholarship (Doctoral)  from the  National Sciences and Engineering Research Council of Canada. M. A. B. is supported by a  Discovery Grant  from  NSERC  ("Impact of chemical modification of non-coding RNAs on gene expression in S. pombe"). JMD is supported by the  Institut Universitaire de France (IUF) , JMD and MEB by the  CNRS  and the  University of Perpignan (UPVD)  and their study is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ) and of the  "École Universitaire de Recherche (EUR) "  TULIP-GS  ( ANR-18-EURE-0019 ).
Results LDLR is a promising candidate for human PDAC targeting. We have previously shown that PDAC modifies its metabolic requirements, making this tumor highly reliant on LDL-derived cholesterol uptake through LDLR. To investigate whether LDLR is a suitable candidate for targeting PDAC in patients, we first performed an in silico analysis of the LDLR mRNA expression profile in primary and metastatic pancreatic tumor samples (n = 728 and n = 76, respectively) compared to normal adjacent pancreas (n = 96). The merged genomic dataset, established from fifteen published expression datasets of PDAC (Supplementary Table  S1 ), revealed a significant increase in LDLR transcripts in primary PDAC (p = 7. 42 × 10 -4 ) and metastases regardless of their location (p = 1. 40 × 10 -4 (liver) and p = 1.
3d , e vs Supplementary Fig. 3d, e ). Overall, these data highlight that Fc(A680)-VH4127 conjugate specifically targets LDLR in pancreatic cancer cells and, as the natural ligand of LDLR, is properly internalized by endocytosis and delivered to lysosomes. The LDLR-targeting conjugate is selectively addressed and uptaken by PDAC in vivo. PDAC targeting by Fc(A680)-VH4127 was first explored in athymic mice subcutaneously transplanted with Ldlr WT or Ldlr KO PK4A cells using real-time NIRF imaging. Fluorescence acquisitions of living tumor-bearing mice were performed following intravenous injection of either Fc(A680)-VH4127 or control conjugate. The quantitative analysis of fluorescence restricted to tumor area showed that Fc(A680)-VH4127 accumulation into Ldlr WT tumors reached a peak from 4 h and up to 24 h post-injection (Fig.
Methods LDLR-based genomic meta-analysis in human pancreatic samples. The merged genomic dataset, established from 15 publicly available mRNA expression datasets Fig. 5  The Fc(A680)-VH4127 conjugate discriminates advanced PDAC from inflammatory and healthy pancreas without causing hepatic and renal damages. a Representative merged fluorescence and visible light images of control (KI), induced-chronic pancreatitis (KI + CP), and spontaneous PDACbearing (KIC) mice before, 24 and 48 h after retro-orbital IV injection of the LDLR-targeting conjugate (1 nmole/mouse). Quantification of Fc(A680)-VH4127 fluorescence in abdominal region (bladder excluded) of each experimental group is illustrated (left graph). Receiver operating characteristic (ROC) curve established from Fc(A680)-VH4127 fluorescence values obtained at 48 h post-injection in each experimental group is shown in right graph.
9% NaCl, equivalent volume). One-way ANOVA with post-hoc Tukey HSD test, N. D. : not detectable. n = 3-7 mice/group. (Supplementary Table  S1 ), is composed of 728 PDAC, 76 metastases (including 36 liver metastases), and 96 normal adjacent pancreatic samples. Other metastatic samples come from the peritoneum (29%), the lymph nodes (22%) and the lung (20%). This in silico meta-analysis was thus based upon data from published studies for which the informed patients' consent to participate and the ethics approval from the institutional review board were already obtained by the authors. The data analysis required pre-analytic processing. First, we separately normalized each DNA microarray-based dataset: individual Affymetrix dataset was normalized using robust multichip average with the non-parametric quantile algorithm for the raw Affymetrix data (Bioconductor and associated-packages, R software), while non-Affymetrix datasets were normalized with quantile normalisation procedure.
Then, Agilent or Affymetrix hybridization probes were mapped across different technological platforms (Stanford Online Universal Resource for Clones and ESTs (SOURCE, https://source-search. princeton. edu/), NCBI EntrezGene (ftp:// ftp. ncbi. nlm. nih. gov/gene/, Homo sapiens gene information db, release from 04/ 27/2017) and Affymetrix NetAffx Annotation files (www. affymetrix. com; release from 01/12/2008). When multiple probes mapped to the same gene, we retained the most variant probe in a particular dataset. For the RNA-seq data, we used the available normalized data. We then log 2 -transformed the normalized genomics data. Subsequently, we corrected the fifteen studies for batch effects using z-score normalization. Briefly, for each separate LDLR expression value in each study, we subtracted the mean value of the gene in that dataset divided by its standard deviation, mean and standard deviation only being measured on primary tumor samples.
Confocal microscopy imaging. Cell and tissue fluorescent images were acquired with a laser-scanning confocal microscope (LSM700, Zeiss) using Plan Apochromat ×63 oil immersion objective lens and were analyzed with Zen Lite 2012 software. FACS analysis of cell surface and internalized fluorescent LDL and conjugates. 7000 Ldlr WT or KO PK4A cells were grown in DMEM-10% FBS for 48 h. Then, cells were incubated with Fc(A680)-VH4127 or Fc(A680)-VH4Sc (20 nM) in presence or not of DiI-LDL (20 µg/ml) in DMEM-1% BSA for 6 h. Half of the cells were additionally treated with an acid stripping solution (0. 15 M NaCl, 0. 2 M glycine, pH 3) to remove the cell-surface LDL and/or conjugate. Finally, all cells were fixed in 2% PFA and 5 mM EDTA before to be analyzed by FACS (Attune NxT flow cytometer, ThermoFisher) using the Attune NxT Software version 3.
Induced-chronic pancreatitis mice. Seven-week-old male and female KI mice (Mus musculus) were daily injected with cӕrulein (250 µg/kg, Sigma-Aldrich) for 12 days by intraperitoneal route. Intra-hepatic PDAC mice. Seven-week-old male and female KI mice (Mus musculus) were implanted with 1. 5 × 10 5 PK4A cells in two different liver lobes. Mice were previously injected with local analgesic and anesthetic agents and surgery was performed under anesthesia as described above. Imaging experiments were started 15 days after implantation. In vivo imaging and tissue distribution of fluorescent conjugates. A wholebody fluorescence acquisition was performed before IV injection (tail or retroorbital sinus) of 1 nmole of conjugate (Fc(A680)-VH4127 or Fc(A680)-VH4Sc) and at different post-injection times using the PhotonIMAGER and the Photo-Acquisition software (Biospace Lab, France).
For KI and KIC mice, hair of the abdominal part was removed to increase fluorescence detection. At the time of imaging, a visible light image followed by an Alexa Fluor 680 acquisition (Ex. : 660 nm, Em. 700 nm, acquisition time: 2 s) were realized on anesthetized mice. Then, mice were euthanized and fluorescence acquisitions of whole-body after laparotomy and/or of excised organs of interest were performed. Finally, half of the tissue samples were fixed in 10% (wt/vol) formalin, while the other half were fastfrozen in cold isopentane for further analysis. Analysis of fluorescence specific to a region of interest (ROI) was performed using M3 Vision software (version 1. 1. 2. 26170, Biospace Lab, France). For whole-body acquisitions, the ROI autofluorescence, measured before injection, was subtracted from the ROI fluorescence specific to each conjugate measured at each post-injection time.
Acknowledgements We sincerely thank  M. Masse ,  Y. Mechioukhi ,  N. Mercadal ,  K. Varini ,  R. Sonnette ,  D. Barea ,  T. Gicquel , and  E. Jaune  for their technical assistance and helpful advices;  Pr. S. Garcia  for his expertise in pancreatic histopathology, and  Dr. L. Peng  for critical reading of the manuscript. We also thank the mice colony facility, the cell culture platform, the  IPC/CRCM experimental pathology (Marseille)  and the interactome platform ( INP, Marseille ) for their assistance. This work in partnership with Inserm Transfert was supported by grants from  Vect-Horus  ( N°141309A30 ) and the  Association Nationale de la Recherche et de la Technologie  ( CIFRE 2016/1344 ).
[22]. Microbial communities in these studies were likely highly dominated by heat-shock resistant bacteria, such as Clostridium species [23]. Clostridium species do not harbour the pyruvate decarboxylase gene to go from pyruvate to acetaldehyde (Figure1, search in GenBank of NCBI 23rd Nov 2020) and therefore do not utilise the homo-ethanol pathway. Yeasts can thrive at low pH and usually dominate during spontaneous ethanol producing fermentations where pH is typically below 4, such as grape juice fermentation for wine production [24], barley fermentation for Belgian sour ales[25,26 ]  and milk fermentation for kefir [27,28]. Peinemann et al. recently carried out a non-sterile food waste fermentation inoculated with Saccharomyces cerevisiae [29 ]. When S. cerevisiae competed against the native food waste microbial community, the authors obtained ethanol yields and titres of 0.
Acknowledgements J. L. Rombouts  wishes to thank the early support of this work through the  SIAM gravitation grant  from the  Netherlands Organisation for Scientific Research (NWO)  under the number  024. 002. 002. He is also grateful for the discussions with  Prof. Dr. G. J. Witkamp  on the topic of an integrated bioethanol production processes using microbial communities. He further wishes to thank  J. J. de Iongh  and  B. van de Berg  for their effort in coevaluating the business case of bioethanol and electricity production from food waste (swill) in the Rotterdam area (The Netherlands).
, 2020) , enabled the rise of heterologous pathway reconstitution in yeast. For instance, the optimization of native metabolism is frequently employed to revise metabolic fluxes  (Luo et al. , 2019; Dusséaux et al. , 2020; Pyne et al. , 2020; Srinivasan and Smolke, 2020) , and enzyme(s) relocalization to the peroxisome appeared as a promising strategy to increase yields  (Dusséaux et al. , 2020; Grewal et al. , 2021). In parallel, with the expanding large highquality datasets, predictive engineering and systems metabolic engineering emerged as a tool to facilitate the design of desired cell properties via specific genetic modifications  (Lee et al. , 2012; Zhang et al. , 2020). Thus, further implementation of computation and rational strategies in addition to pathway characterization, as well as current and future progress in metabolic engineering, promise a prominent potential of yeast cell factories for any heterologous production, including the new-to-nature compounds.