dataesr/scientific-paragraphs-categorization huggingface dataset:
Inter-population analysis of genetic structure To quantify genetic differentiation between populations, we computed pairwise estimators of F ST for each pair of populations using the ENA correction described in  [30]  and implemented in the software FreeNA  [30] , as there was some evidence of null alleles. We then used these corrected values to test for isolation-by-distance pattern by regressing pairwise estimates of F ST /(1-F ST ) against ln-distance between sample sites. Mantel tests were used to test the correlation between matrices of genetic differentiation and geographical distances between sampling sites by 1000 permutations in Genepop 4. 0. 10  [32]. To investigate the spatial genetic structure among individuals, we used STRUCTURE 2. 3. 4  [36]. The individualbased Bayesian clustering procedure was run with 20 independent runs for each of K from 1 to 10.
Each run included 10 6 iterations, following a burn-in period of 10 5 iterations. We used admixture ancestry model and correlated allele frequencies model (with λ = 1). The output of STRUCTURE analysis was post-processed in CLUMPAK software  [37]  to identify separate groups of runs on the base of similarity between Q-matrices for each K. We used the LargeKGreedy algorithm, random input order and 2000 repeats. Different modes from the results of the 20 runs for each K value at a threshold of 0. 9 for similarity scores were identified. Summary barplots for a given K value contain averaged proportions of individual membership obtained for all runs in the same mode. The likelihood of K (Ln Pr(X|K)), the ΔK criterion  [38]  and a proportion of similar runs that formed the major modes for each K were used to infer the best number of real populations.
Three upper Wami basin populations formed a separate cluster on mitochondrial and nuclear markers, apparently consistent with a recent elevation of the populations from this region to a specific level, as N. prognathus  [17]. In our mitochondrial dataset, this lineage additionally included one population (T15) from an isolated pool at the periphery of the Ruvu basin, but this population did not cluster with the three Wami populations on nuclear markers. On nuclear markers, this population was recovered as relatively unique since it also differed from geographically close Ruvu population. The difference was likely driven by its low intrapopulation genetic diversity and hence important contribution of genetic drift. Indeed, that isolated population is located at relatively high elevation (246 masl; compared to ≤166 masl in all other non-Wami populations, A single divergent haplotype has been discovered in coastal Kenya (Ramisi basin, KEN15-1).
Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its Additional files 1, 2, 3, 4. Microsatellite data were deposited to Figshare (doi: https://doi. org/10. 6084/m9. figshare. 9631907). New sequences used in this study are available in GenBank (accession numbers MN413245-MN413327).
We dissected wild and F1 female mosquitofish to obtain four life-history traits related to reproduction and offspring size: reproductive allocation (gonadosomatic index, hereafter 'GSI', calculated as gonad weight ÷ total weight), fecundity (number of embryos), mean embryo diameter and mean embryo mass (F1 fish only). We sought to obtain trait data from females across a similar range in body size from each site. We also measured embryo stage as a potential covariate affecting these traits, using a modified protocol from  [55]  in which one of six development stages was assigned to each brood. Our modified trait measurement protocol is provided in electronic supplementary material, appendix S1.
(d) Adult wild and F1 trait analyses We tested for effects of source temperature and maternal body length on the focal adult traits. We excluded non-gravid females, which were rare, from the dataset. We assigned each fish a source temperature which was the average from the time series of the population's source temperature (figure  1 ; electronic supplementary material, table  S1 ). We used generalized linear models for each trait by dataset (wild in spring, wild in summer, F1) combination. We sought to remove (control for) the independent covariate effect of embryo stage, which we expected could influence life-history traits (e. g. since embryo diameter increased with embryo stage; electronic supplementary material, figure  S4 ). We started with the full model specification: trait ∼ maternal length × source temperature + embryo stage.
To obtain model coefficients and associated p-values, we used the summary() function. To obtain model R 2 values for the fecundity model, we used the package 'rsq'  [58]. To approximately visualize models for GSI and log 10 embryo size without the influence of embryo stage (the covariate), we obtained residuals from the OLS regression trait ∼ embryo stage, and plotted predictions for the model residuals ∼ maternal length × source temperature.
Fish of a given treatment temperature were then assigned one of the five replicate tubs sequentially as they were born, such that fish density differences among tubs were minimized through time. Fish were fed an excess of Frystartr food (Skretting, Stavanger, Norway) thrice daily. Growth was measured as the difference in total length at age 0 and at age 15 days. Lengths were measured from top-down photos taken with a scale bar (electronic supplementary material, figure  S3 ), and analysed in ImageJ software  [59].
Acknowledgements. We thank  S. Parmenter ,  P. Pister  and  C. Wickham  for introducing us to these geothermal springs populations. For technical assistance, we thank  T. Apgar ,  R. Franks  and  J. Velzy. For help with field collections, we thank  K. Morrison ,  J. Penfield ,  S. Munch  and  B. Wasserman. For help with dissections and trait measurements, we thank  E. Portillo ,  T. Amarnath ,  S. Bell ,  R. Robinson ,  E. Glensky ,  M. Haptonstall  and  M. De Aquino. For help with fish care during common rearing, we thank  D. Ruiz ,  D. Weiler  and  T. Vance.
Interestingly, we observe that the accumulation of primary miRNAs caused by loss of HEN2 restores levels of mature miRNAs and mostly rescues the developmental phenotype of se-2 mutants. Taken together, our data suggest that SE is not only required for miRNA processing but also actively promotes the degradation of pri-miRNAs and other types of RNAs by recruiting the NEXT complex. We propose that SE fine-tunes miRNA biogenesis by facilitating miRNA processing or recruiting the NEXT complex to trigger the degradation of pri-miRNAs.
Control IPs were performed in Col-0 using either GFP or FLAG antibodies. Eluted proteins were digested with sequencing-grade trypsin (Promega) and analyzed by nanoLC-MS/MS on a QExactive+ mass spectrometer coupled to an EASY-nanoLC-1000 (Thermo-Fisher Scientific) as described before  (44). Data were searched against the TAIR10 database with a decoy strategy. Peptides were identified with Mascot algorithm (version 2. 5, Matrix Science, London, UK) and the data were imported into Proline 1. 4 software (http:// proline. profiproteomics. fr/). The protein identification was validated using the following settings: Mascot pretty rank ≤1, False discovery rate (FDR) ≤ 1% for peptide-spectrum match (PSM) scores, FDR ≤ 1% for protein set scores. The total number of MS/MS fragmentation spectra was used to quantify each protein from at least two independent biological replicates.
Biological replicates consisted of plants of the same genotype grown at different dates and in different growth chambers. In the case of ZCCHC8B we used plants derived from two independent transformants that were grown in parallel. For the statistical analysis we compared the data from two independent experiments for ZCCHC8A, ZCCHC8B, RBM7 and four IPs from two biological replicates for SE to the control IPs using a homemade R package as described in  (44). The R-package performs a negative-binomial test using an edgeR-based GLM regression and calculates the fold change and an adjusted Pvalue corrected by Benjamini-Hochberg for each identified protein. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  (45)  with the dataset identifier PXD014447.
RNA-seq data analysis Raw reads were trimmed for the first 12 nt with the FASTX-Toolkit. Sequencing adapters and remaining rRNA reads were removed using Trimmomatic  (47)  and Bowtie  (48) , respectively. Cleaned reads were aligned to the Arabidopsis TAIR10 reference genome using HISAT2  (49). The overall alignment rate was 95-96% for each sample. Stringtie and prepDE. py python scripts were used to obtain gene count matrix reads. For the Stringtie analysis, the TAIR 10. 46. gtf annotation file was supplemented with pri-miRNA coordinates retrieved from the mirEX2 database  (50). Boxplots were generated using R. Differential gene expression analysis was performed using the DESeq2 R package  (51). For Figure  5  and Supplementary Figure  S3 , RNA-seq read distribution profiles were generated using the bedtools package (52) and visualized using Microsoft Office Excel.
Interestingly, ZCCHC8A, a subunit of the Arabidopsis NEXT complex was the most enriched protein in the SE IPs. In fact, all components of the NEXT complex HEN2, ZCCHC8A, ZCCHC8B and RBM7 copurified with SE (Figure  1B --red open circles). Taken together, our data confirm the previously reported interaction of SE with the nuclear cap binding complex (CBC) and miRNA biogenesis factors, and detect the THO/TREX and NEXT complexes as additional partners of SE in plants. We then used Arabidopsis lines expressing GFP-tagged versions of ZCCHC8A, ZCCHC8B and RBM7 to test the association between SE and the NEXT complex. Indeed, SE was co-purified when ZCCHC8A, ZCCHC8B or RBM7 were used as baits (Figure  2  and Supplementary Table  S3 ). This result confirmed the association of SE with the NEXT complex.
Both Y2H and FRET-FLIM assays were also applied to test the individual interactions among the subunits of the NEXT complex. These experiments revealed that HEN2 directly interacts with the two zinc finger-containing proteins ZCCHC8A and ZCCHC8B, but not with the RNA binding protein RBM7 (Figure  4A  and B ). The interactions between RBM7 and ZCCHC8 proteins could not be tested by Y2H assays because their fusion to the Gal4 DNA BD auto-activated the expression of the reporter gene (Figure  4A ). However, FRET-FLIM analysis indicated that both ZCCHC8A and ZCCHC8B can interact with RBM7-RFP (Figure  4B ). Moreover, FRET-FLIM data obtained with ZCCHC8A and ZCCHC8B as either donors or acceptors suggested that the two proteins can interact and form both homo-and heterodimers (Figure  4B ). To test whether HEN2, ZCCHC8A and SE can form a trimeric complex we combined bimolecular fluorescence complementation (BiFC) and FRET-FLIM.
At last, we re-analyzed a recently published RNA-seq dataset (60) obtained from rrp41 RNAi mutants for the accumulation of all pri-miRNAs with known gene structure. Fortytwo MIR transcripts were detected in at least half of the libraries and showed an overall accumulation in rrp41 RNAi mutants as compared to controls (Figure  6C ), strongly indicating that pri-miRNAs are indeed substrates of the exosome complex. Taken together, these data support the conclusion that NEXT contributes to the degradation of miRNA maturation by-products and assists in the elimination of surplus pri-miRNAs by the RNA exosome.
Using TEX1, UAP56 and MOS11 as baits, Arabidopsis THO/TREX was shown to associate with a number of splicing factors and with components of the nuclear pore complex  (66). Our data now extend these observations by showing that the association of THO/TREX with SE/ARS2 is also conserved in plants. At last, a portion of Arabidopsis SE is associated with the NEXT complex, similarly to its human counterpart ARS2  (21). The association of SE with the NEXT complex was confirmed by multiple reciprocal co-IPs. Our data strongly indicate that this association is mediated by the ZCCHC8 subunits, which also directly interact with both the HEN2 and RBM7 subunits within the NEXT complex. This latter observation suggests that ZCCHC8A and ZCCHC8B, similar to the role of hZCCHC8 in human NEXT  (67) , function as central scaffold proteins of NEXT in Arabidopsis.
Interestingly, our FRET-FLIM data indicate also that each of ZCCHC8A and ZCCHC8B can interact with itself and with each other. This opens the possibility that the NEXT complex may contain two (or more) ZCCHC8 subunits, and that heterogenic NEXT complexes containing both ZCCHC8A and ZCCHC8B may also exist. However, as compared to ZCCHC8A, poor enrichment of ZCCHC8B in RBM7 or SE-IPs strongly suggests that the majority of Arabidopsis NEXT contains ZCCHC8A. Whether the incorporation of ZCCHC8A and/or ZCCHC8B influences the functional properties of NEXT remains to be studied in detail. In this study, loss of either ZCCHC8A or ZCCHC8B had no major effect and accumulation of miRNA precursors was only observed in the double zcchc8a zcchc8b mutant. Hence, ZCCHC8A and ZCCHC8B have at least partially overlapping functions and can replace each other.
By contrast, our data strongly indicate that Arabidopsis SE recruits the NEXT complex via a direct interaction with the ZCCHC8 subunits. How these variations are linked to the differences in protein structure  (24)  and how this impacts RNA processing and stability will be an interesting focus of future research. It also remains to be investigated whether ZCCHC8A/B competes with components of the Arabidopsis THO/TREX complex for binding to SE in a similar manner as hMTR4 and human THO compete for binding to ARS2. It is also tempting to speculate that SE binding to NEXT may perhaps exclude the simultaneous binding of miRNA processing factors such as HYL1. Based on the results presented here we propose that SE participates in RNA degradation through the recruitment of the NEXT complex. It is therefore interesting to note that the double mutant se-2 hen2-2 is sterile while double hyl1-2 hen2-2 show normal fertility.
FUNDING National Science Centre  [ UMO-2014/13/N/NZ1/00049 ;  UMO-2018/28/T/NZ1/00392  to M. B. ,  UMO-2013/10/NZ1 /00557  to  A. J. ,  UMO-2016/23/D/NZ1/00152  to D. B. ];  Centre National de la Recherche Scientifique (CNRS) ;  Agence Nationale de la Recherche (ANR) ,  'Investments for the future' program  [ ANR-17-EURE-0023 ,  ANR-10-LABX-0036  NETRNA to D. G. ]. Funding for open access charge:  Adam Mickiewicz University (Poznan, Poland ), in the frame of the  Initiative of Excellence -Research University project  financed by the  Polish Ministry of Science and Higher Education.
This might be an effect of the lockdown and the fact that more women than are men are juggling caring for families and children despite both "working" from home  [10, 11]. Travel, social, and funding restrictions will also take a serious toll on scientific research worldwide. Research staff and resources have been purposely and purposefully prioritized to COVID-19 activities above all else. Distancing and transmission issues have caused most non-COVID clinical research to be suspended, causing a reduction in recruitment of research subjects and a delay in data entry into clinical trial databases  [12]. Research-related hiring has been suspended because of travel restrictions and young researchers might soon find themselves out of a job if their subject is not the pandemic. Indeed, though government-funded medical research bodies worldwide say they are committed to maintaining the continuity and breadth of biomedical research, how the economic downfall will influence government spending remains to be seen.
Listeria monocytogenes in the genomics and post-genomics era Lm microbiological surveillance is now based on whole genome sequencing (WGS) and the use of universally applicable genome-wide strain genotyping approach such as core genome multi locus sequence typing (cgMLST)  (Moura et al. , 2016). WGS-based cgMLST Lm typing not only greatly improves the identification of clusters of listeriosis cases which are linked microbiologically, but also the identification of the food sources of these clusters  (Moura et al. , 2017). It also offers the academic community with a large number of sequenced Lm genomes (more than 40,000 in Institut Pasteur database https://bigsdb. pasteur. fr/listeria/) from which the population structure of Lm can be studied, its evolution, transmission routes and worldwide spread investigated  (Moura et al.
, 2017). Retinas were dissected and imaged using Leica macroscope (MG0295) and LASAF software. Retino-collicular projection coordinates of the DiI injections were calculated as described  (Brown et al. , 2000; Reber et al. , 2004; Bevins et al. , 2011). For cortico-collicular map analyses, sagittal vibratome sections were performed on P14 SC and terminal zones (TZs) were plotted along the rostral-caudal axis on Cartesian coordinates (y axis). V1 were photographed as whole-mount and focal injections plotted along the V1 lateral-medial axis (x axis)  (Savier et al. , 2017; Triplett et al. , 2009). Cortico-collicular maps were generated using non-parametric smoothing technique, termed LOESS smoothing  (Efron and Tibshirani, 1991) , to estimate the profile of the one-dimensional mapping from V1 to SC.
The 3-step map alignment model in MATLAB (RRID:SCR_001622)  (Savier et al. , 2017)  was used to simulate the formation of both the retino-and cortico-collicular maps in the presence of an oscillatory Epha gradient in the retina. Each brain structure (retina, SC, V1) is modelled as a 1-d array of 100 neurons (N) in each network. Two maps are generated: first, the map from retina to SC; second, the map from V1 to SC. Each map is modelled sequentially in the same way. This model consists in the minimization of affinity potential (E) which is computed as follows: E = E act + Echem At each step, this potential is minimized by switching two randomly chosen axons probabilistically to reduces the energy in the system by Delta E (∆E). The probability of switching, p, is given by: 𝑝𝑝 = 1 (1 + 𝑒𝑒 (4Δ𝐸𝐸) ) Echem is expressed as follows: where α= 200 is the strength, RA(i) and RA(j) the Epha receptor concentration in the retina and V1 at location (i) and (j) and LA(i') and LA(j') the Efna ligand concentration at the corresponding position (i') and (j') in the SC.
THEORETICAL FRAMEWORK This article is drawn from research carried out between 2017-2019 in France and New Zealand, as part of PhD research comparing the educational experiences of immigrant teenagers in schools during the newlyarrived phase of language-of-schooling acquisition and integration into school systems dominated by French (in France) and English (in New Zealand), despite the linguistic diversity of student populations. The research examines the role of first languages during the newly-arrived learning phase for 13-15 year old immigrant students in two essentially monolingual educational contexts, however situated within the broader context of increasing sensitivity towards diversity and recognition of the multiple benefits of plurilingualism. While it is still too early to present data and findings from observations carried out with newly-arrived immigrant students in schools, this article presents an argument for language inclusiveness based on the well-supported premise that first languages play an essential role in acquisition of the language-of-schooling, and thereby in successful academic outcomes for immigrant students, through a broad overview of language education policy, languages in the curriculum, and attitudes towards plurilingualism and various immigrant communities in France and New Zealand.
72% increase in the inflow of foreign population seems substantial, the number of migrants is relatively small: from 5 57,618 new arrivals in 2010 to 99,321 arrivals in 2017. (OECD International Migration Database, https://stats. oecd. org/Index. aspx? DataSetCode=MIG Accessed 26/2/2020. NZ Immigration, https://mstn. govt. nz/wp-content/uploads/2019/06/refugees-and-asylum-seekers-factsheet. pdf.
Protective Gowns/Aprons There is a robust open source sewing community that shares patterns (e. g. , Free Sewing that made a face mask pattern available for the COVID-19 pandemic  [175] ) and open source pattern design software like Seamly2D  [176]. In addition, patterns for gowns and aprons are available. None, however, appear focused on medical needs, nor have the materials available to lay people been tested (as with mask materials) to see how they perform in medical environments.
This is because having many methods of solving the problem will ensure that these life-saving devices or supplies can be manufactured throughout the world. A good example of this that has occurred in the past is with a technology further down on the list of Indian's priorities, number 35, which is the syringe pump. An open source syringe pump library was developed that can be customized for any syringe using a scriptable CAD (OpenSCAD), 3-D printed from waste plastic, and driven wirelessly with a Raspberry Pi  [251]  and open source software  [252]. These and their variants (e. g. , using an Arduino  [253] ) and other types of open source syringe pump controllers have been developed  [254]  to allow for multi-syringe devices  [255]  or different sizes  [256] , and to help different applications in biotech  [237]  and DNA labs  [68] , and those with feedback that enable even more sophisticated control  [257, 258].
Therefore, the latter hypothesis implies temporary spatial isolation. It is also possible that a combination of both hypotheses explains differentiation within red crossbills. To distinguish between these two hypotheses, we collected data on the food resources of the different call types of the red crossbill in Europe, and compared them with spatial and temporal patterns of occurrence of call types. If isolation by ecology and therefore specialisation in a key conifer is the main driver for the differentiation of call types, we expected conifer species to predict call type distribution. Their geographic distribution should more or less coincide with the geographic distribution of their key conifer. Within a geographical region, the number of call types should not exceed the number of tree species present.
Food preferences and site fidelity of different call types In search of potential differences in the food resources of call types of the red crossbill, we evaluated decisions of birds to feed on a specific tree species. One of us (JR) noted the tree species in which individuals foraged during fieldwork in France. Red crossbills, like other members of the Fringillidae, visit drinking sites several times a day  (Glutz von Blotzheim and Bauer 2001)  and often switch between different trees while feeding (own observations). We cautiously considered only data about the food resource of the same call type taken at the same site if gathered on different days. This surely implies independent decisions to feed on these tree species. If recordings were made on the same day, we only retained a single recording when multiple recordings were made within 500 m of each other (we chose this distance to exclude mistakes which could occur due to inaccuracies of the location data).