dataesr/scientific-paragraphs-categorization huggingface dataset:
To deepen the understanding of Anopheles interactions with RNA viruses, we sequenced messenger RNAs and small RNAs of A. coluzzii midguts three days after oral infection with ONNV, a time when the virus has not yet escaped to the systemic compartment  [6]. Analysis of the datasets identified differentially regulated transcripts for messenger RNA (mRNA) and microRNA (miRNA) during ONNV infection of A. coluzzii, and predicted novel potential miRNAs. Transcriptional responses of the primary midgut ONNV infection were compared to published transcriptional datasets of A. coluzzii infected with ONNV by needle injection, and to the primary midgut infection with the eukaryotic protozoan malaria parasite, Plasmodium. In addition, joint analysis of mRNA and miRNA expression levels predicted putative regulatory networks of immune signaling.
Comparison with other Anopheles transcriptional datasets The current Illumina RNAseq dataset was compared to previously reported microarray gene expression data, also from A. coluzzii, infected by intra-thoracic injection with ONNV  [10]. In that study, 211 transcripts were differentially regulated at four days post-injection. Only three genes overlap with the transcripts regulated by the normal bloodmeal-infection route used in our study (Fig. 2 ): LRIM4 (AGAP007039) and AAPP (AGAP009974) are upregulated both in blood-infected midgut and needle-infected mosquitoes at one day post-injection, while LRIM10 is downregulated in blood-infected midgut but was upregulated in needle-infected mosquitoes at four days post-injection. The small number of overlapping regulated genes between the studies, and the opposite regulation of LRIM10, is consistent with the dichotomy in immune signaling previously described for the two compartments, that is, the primary midgut infection by bloodmeal, and the secondary disseminated systemic infection  [6].
Histograms indicate fold change of transcript abundance (adjusted p-values, Additional File 1: Table  S1 ). Gene identities are indicated by Vectorbase AGAP identifiers. The six genes with a gene name to the left of the AGAP identifiers have an annotated function, while the majority of ONNV-regulated genes are without known function, and bioinformatic functional prediction data is shown in Additional File 1: Table  S1  some gene IDs were split, and many new gene IDs were created since then. Technical differences would also limit the value of completely remapping the 2006 microarray probe sequences to current candidate gene IDs, because the microarray coverage of transcripts was much lower than the current RNAseq coverage. Nevertheless, we remapped the recognizable gene ID matches to the current A.
gambiae PEST strain genome (assembly AgamP4) in order to compare the two biological datasets to the extent possible. Among the 30 current ONNV-regulated transcripts, a total of 12 gene IDs could be linked to old gene IDs from the Plasmodium dataset (Additional File 1: Table  S1 , column U: ID_Old-ENSANGT). All 12 shared transcripts were induced by ONNV infection (Additional file 3: Figure  S2 ; Additional file 1: Table  S1 , column V: ONNV, 1 = induced, 0 = no change, -1 = repressed). Seven of the transcripts were also induced by mosquito feeding on a viable but invasion-defective P. falciparum mutant (Additional file 1: Table  S1 , column Y: PfCTRP), while only four and three genes were regulated by midgut infection with P. falciparum and P. berghei, respectively (Additional file 1: Table  S1 , column W, PfGUT, column X, PbGUT).
Correlation of miRNAs with regulation of mRNA expression Sequence-based target prediction methods of miRNAs are based on the prediction of complementarity between the seed region of mature miRNAs (nucleotide positions 2-7) and the 3' UTR region of messenger RNAs  [19, 20]. A drawback of these methods is a high rate of false positive predictions due to the small size of the seed region of mature miRNAs  [21]. Consequently, it has been recommended to filter these sequence based predictions by using the joint analysis of expression levels of miRNAs and mRNA targets  [22]. In order to generate a large dataset to empirically test the correlation between miRNA expression and regulation of mRNAs, we silenced the key immune signaling nodes, Rel2 and Stat-A, with and without ONNV infection, to perturb large networks of gene expression in the signaling pathways Imd and JAK/STAT, respectively.
berghei parasites and Sephadex beads  [43, 44]. The repression of CLIPB8 during ONNV infection raises the possibility that either the protective response of Anopheles to ONNV infection requires an inhibition of the melanization cascade, or that the virus suppresses the cascade as host manipulation. That possibility would be consistent with the observed decrease of melanization of P. berghei parasites in mosquitoes co-infected with ONNV  [10]. The extensive small RNA sequence data in this study produced a comprehensive database of known and potential miRNAs in A. coluzzii. We identified one miRNA, aga-chr2L_23370, which is differentially regulated by ONNV expression. Characterizing this miRNA and its target genes could shed light on the RNA virus-Anopheles interaction. No previous miRNA studies have been performed on the Anopheles viral response to our knowledge.
Analysis of RNAseq sequencing data Raw fastq files were clipped of their adaptor and filtered for quality using Cutadapt  [55]. Sequence reads were then mapped against the A. gambiae PEST strain genome (assembly AgamP4) using BWA mem version 0. 7. 7 with default parameters  [56]. Read counting in genes/features was done with HTSeq-count version 0. 6. 1  [57]  and the AgamP4. 3 gene set. Differential gene expression analysis was done under R version 3. 3. 1  [58]  with the package DESeq2 version 1. 14. 1  [59]  The raw p-values were adjusted for multiple testing according to the Benjamini and Hochberg procedure  [60]  and only the genes with an adjusted p-values under 0. 05 were considered as differentially expressed. Venn Diagrams were drawn using the R package VennDiagram version 1. 6. 0, and GO term analysis was performed using the R package topGO  [61].
Analysis of small RNA sequencing data Raw fastq files were clipped of their adaptor and filtered for quality with Cutadapt  [55]. Next, a database of miRNA was build using reference miRNA from miRBase  [62]  and from the literature  [16, 63]. For this purpose, genomic coordinates of reference miRNAs in gff format were integrated with bedtools v2. 26  [64]  in order to generate a non-redundant set of 167 A. coluzzii miRNA genes. Genomic coordinates of miRNA genes are based on A. gambiae PEST strain genome (assembly AgamP3). The miRNA database was complemented using our small RNA datasets to predict potential novel miRNA using miRdeep2 version 2. 0. 0. 7  [65]  (miRNA_ONY_pre. fasta + mature), which yielded a prediction of 300 miR-NAs, of which 118 were reference miRNAs and 182 were novel miRNAs detected in the present study (Additional file 4: Table  S2 , miRNA databases).
The quantifier. pl module of miRdeep2 was used to map filtered and clipped small RNA sequence reads over reference and novel miRNAs, quantifying the number of reads falling into an interval 2 nt upstream and 5 nt downstream of the mature/star sequence. Raw counts were analyzed for differential expression using DEseq 2  [59]  using same procedure followed with RNAseq data above. Sequence-based prediction of target genes of reference and new miRNAs were performed with MiRanda  [66]. Pairwise comparison between log 10 transformed normalized expression matrices of mRNAs and miRNAs were performed using Pearson correlation with rcorr package in R version 3. 3. 1.
Funding This work received financial support to KDV from the  European Research Council, Support for Frontier Research , Advanced Grant 323173 AnoPath;  European Commission ,  Horizon 2020 Infrastructures 731060 Infravec2 ;  National Institutes of Health NIAID USA ,  AI121587 ; and  Agence Nationale de la Recherche Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases "  ANR-10-LABX-62-IBEID. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and material All RNAseq datasets are available at the EMBL ArrayExpress Archive (https:// www. ebi. ac. uk/arrayexpress/), accession number E-MTAB-6443. All small RNAseq datasets are available at the EMBL ArrayExpress Archive (https://www. ebi. ac. uk/arrayexpress/), accession number E-MTAB-6444.
However, in cooperation with the manufacturer it was possible to receive the current modified and improved version: each of the PMTs has been continuously operated for at least six weeks in a LAr test stand prior to the deployment in Gerda, and has been operated stably in Gerda since the start of Phase II more than one year ago (see Section 6). The voltage dividers are designed for negative bias and high signal quality. The electrical power consumption is kept low at about 20 mW to prevent the argon from boiling. For further protection against discharges due to argon gas bubbles the PCB is potted into epoxy resin and a copper pot (see Fig. 13 ). Custom made SAMI RG178 coaxial cables are used for both signal and bias voltage inside the Gerda lock and cryostat. The PMT signals are amplified outside of the lock by custom made shapers and split into two branches; one output leads to a FADC for digitization, and a second branch is used to independently monitor the count rates: the signals are fed into a custom made scaler device that applies a threshold of ∼20 % photoelectron amplitude to count hits and store the rate in a database.
The Gerda Slow Control has access to this database and invokes an immediate automatic safety ramp down of a PMT's bias voltage in case its rate ex-ceeds a limit of 20 kHz. The bias power supply is the same CAEN SY1527 system as used for the Cherenkov veto PMTs  [3]. High voltage filters are mounted at the feedthroughs to the Gerda lock to reduce electronic pickup noise on the bias power. The gain of the PMTs is calibrated to (2-3)•10 6 with bias voltages in the range from -1300 V to -1550 V. At these settings the peak-to-valley ratio reaches its best values of 3. 5 to 4, which is relevant to identify hits above baseline noise at a low threshold. The gain is constantly monitored and found to be stable throughout the operation in Gerda (see Section 6).
Data acquisition and analysis The same FADC system (SIS 3301 Struck) used for the Ge detectors  [3]  records the pulse shapes of the 16 PMT and 15 SiPM channels and saves them for off-line analysis. The PMT traces are digitized with 100 MS/s and for each channel a trace of 12 µs length is saved to disk. The resolution of the SiPM traces is reduced to 80 ns to save disk space but traces of 120 µs length are recorded. All LAr channels are read out together with the Ge channels if at least one Ge detector has an energy deposition above 100 keV. Fig. 18  shows the traces of a representative background event that has been triggered by a Ge detector and that has produced in addition signals in the PMTs and SiPMs of the LAr veto system. PMT hits are reconstructed in the offline analysis following the procedure described in  [24].
Acknowledgements The Gerda experiment is supported by the  German Federal Ministry for Education and Research (BMBF) , the  German Research Foundation (DFG)  via the  Excellence Cluster Universe , the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  Max Planck Society (MPG) , the  Polish National Science Centre (NCN) , the  Foundation for Polish Science  ( TEAM/2016-2/17 ), the  Russian Foundation for Basic Research (RFBR)  and the  Swiss National Science Foundation (SNF). These research institutions acknowledge internal financial support. This project has received funding/support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreements No  690575  and  No 674896. The Gerda Collaboration thanks the directors and the staff of the  Lngs  for their support of the Gerda experiment.
Assessment The approach used by EFSA for safety evaluation of flavouring substances is referred to in Commission Regulation (EC) No 1565/2000, hereafter named the 'EFSA Procedure'. This Procedure is based on the opinion of the Scientific Committee on Food (SCF, 1999), which has been derived from the evaluation procedure developed by the Joint FAO/WHO Expert Committee on Food Additives  (JECFA, 1995 (JECFA, , 1996 (JECFA, , 1997 (JECFA, , 1999a)) , hereafter named the 'JECFA Procedure'. The CEF Panel compares the JECFA evaluation of structurally related substances with the result of a corresponding EFSA evaluation, focussing on specifications, intake estimations and toxicity data, especially genotoxicity data. The evaluations by EFSA will conclude whether the flavouring substances are of no safety concern at their estimated levels of intake, whether additional data are required or whether certain substances should not be put through the EFSA Procedure.
Subgroup I (Acyclic sulphides) In vitro data are available for the candidate substance, di-(1-propenyl)-sulfide [FL-no: 12. 298]; Ames test: S. Typhimurium TA98, TA100, TA102, TA1535, TA1537, 1-100 lg/plate. Result was negative with and without metabolic activation  (Stien, 2005). For supporting substances, only data on diallyl sulfide [FL-no: 12. 088] are available. Diallyl sulfide was negative in a limited bacterial reversion assay using one strain only (TA100) and provided equivocal results in an in vitro cytogenetic test in which increased incidences of cells with chromosomal aberrations and sister chromatid exchanges (SCEs), statistically significant but not dose related, were observed. In vivo diallyl sulfide was evaluated as negative in a micronucleus test in mouse bone marrow, which was, however, not designed to evaluate the genotoxicity of the substance itself as it was tested in a mixture.
08Rev1. These supporting substances have been evaluated by JECFA at the 53rd meeting  (JECFA, 2000JECFA, 2000 , 2004b). These supporting substances have been considered by EFSA in FGE. 91 (EFSA CEF Panel, 2010b). In addition, genotoxicity of the candidate substance methyl methanethiosulfonate [FL-no: 12. 159], included in subgroup X, could not be assessed from the data available. However, due to the similarity with methyl methanesulfonate, a direct acting mutagen and carcinogen, there is concern with respect to genotoxic potential of this candidate substance. Therefore, the Panel decided that the Procedure could not be applied to the candidate substances [FL-no: 12. 159, 12. 172, 12. 174, 12. 304 and 16. 057] until adequate in vivo genotoxicity data become available. The other in vitro/in vivo genotoxicity data available, often from limited or poorly reported studies do not provide clear indication of concern for genotoxicity for the remaining candidate substances included in the present evaluation.
169] and 2-mercapto-2methylpentan-1-ol [FL-no: 12. 241] do no longer give rise to concern with respect to gene mutations. Therefore, these two substances can be evaluated using the Procedure in the present FGE. The Panel noted that of the material of commerce for [FL no: 12. 169], approximately half consists of the a, b-unsaturated carbonyl, 4-methyl-3-penten-2-one [FL-no: 07. 101], for which concern for genotoxicity was ruled out in FGE. 204 (EFSA CEF Panel, 2012a) and evaluated using the Procedure in FGE. 63Rev2 (EFSA CEF Panel, 2013). Although the available data are limited  8  the Panel considered that for the 19 substances in FGE. 74Rev4 the genotoxicity data do not preclude evaluating these substances through the Procedure.
5. Evaluation of the additional data submitted for the tri-and polysulphides in FGE. 74 In the earlier EFSA-evaluations of acyclic tri-and polysulphides (subgroup VI in FGE. 08), the evaluation stopped at Step B4 due to lack of NOAEL for a representative substance. The available 90day feeding studies on dipropyl trisulfide and diallyl trisulfide in rats by  Morgareidge and Oser (1970a, b)  were not considered sufficient to derive a NOAEL. The shortcomings of the Morgareidge and Oser studies are the following: • No data on the stability of the test substance in feed are given. • There are no histopathology data. • Nearly all animals, including control animals, were affected by inflammatory changes in respiratory tract, and in other organs (mainly liver). These changes (probably caused by infections) prohibit adequate interpretation of the study results.
7. 3. EFSA Considerations relevant for FGE. 74 The 19 JECFA-evaluated aliphatic sulphides and thiols are distributed into five subgroups of structurally related substances. The subgrouping is in compliance with the one used in FGE. 08Rev5 (see Section 2. 2. 1 and Table  1 ). Although the available data are limited, the Panel considered that for the remaining 19 substances in FGE. 74Rev4 the genotoxicity data do not preclude evaluating these substances through the Procedure. The Panel agrees with the application of the Procedure as performed by JECFA for five aliphatic sulphides and thiols, namely [FL-no:  12   (Mondino, 1981 ) in rats fed with 3-methyl-1,2,4-trithiane [FL-no: 15. 036]. The Panel does not agree with JECFA that 2,3,5trithiahexane ] is structurally related to 3-methyl-1,2,4-trithiane [FL-no: 15.
4 lg/capita per day, which is below 1. 5 lg/day. In line with the opinion expressed by the SCF (1999), the Panel does not make use of this threshold of 1. 5 lg/ person per day. However, the Panel decided that this substance could be allocated to subgroup I, for which a supporting substance [FL-no: 12. 006] provides a NOAEL. Based on the intake estimate (MSDI) for diallyl sulfide [FL-no: 12. 088] and this NOAEL, an adequate MoS of 9. 7 9 10 5 could be calculated. For the two tertiary thiols [FL-no: 12. 169 and 12. 241], the Panel did not agree with JECFA that appropriate studies were available for deriving NOAELs, and accordingly the Panel concluded that additional data are required for these two substances. Within subgroup VI, a 90-day study on methyl propyl trisulfide [FL-no: 12. 020] has recently become available.
Specifications including purity criteria and identity are available for all the 19 JECFA-evaluated substances. These have been considered adequate for all substances. Thus, for 10 candidate substances in FGE. 74Rev4 [FL-no: 12. 009, 12. 013, 12. 020, 12. 023, 12. 045, 12. 074, 12. 155, 12. 169, 12. 241 and 12. 280], the Panel concluded that additional data are required to finalise their evaluation. For the remaining nine JECFA-evaluated aliphatic sulphides and thiols [FL-no: 12. 088, 12. 179, 12. 198, 12. 212, 12. 238, 12. 239, 12. 255, 12. 257 and 12. 291], the Panel agrees with the JECFA conclusion 'No safety concern at estimated levels of intake as flavouring substances' based on the MSDI approach. Appendix C -Summary of the genotoxicity data   (b): A statistically significant increase in the number of SCEs per chromosome was seen at 1,350 lg/mL and the 450 lg/mL dose level in the presence of metabolic activation; but no significant increase was seen in the remaining dose levels, and no dose level showed a two fold increase in SCEs; therefore, t-butyl mercaptan is not considered to be mutagenic.
Results Identification of microtubule regulators in neurons. To identify genes that may contribute to microtubule organization in neurons we took advantage of proteomic data previously obtained from cycling cells. Using information from the literature and from public databases we compiled a list of "microtubule cytoskeletonassociated" (MCA) genes (Supplementary Data 1). These genes encode proteins that in cycling cells were shown to associate with structures of the microtubule cytoskeleton including centrosomes, kinetochores, mitotic spindles, and midbodies, and/or have roles in mitotic spindle assembly and function (http://microkit. biocuckoo. org; 9] [30]. We then performed whole genome microarray analysis (20,398 genes including 1456 MCA genes) of cultured mouse hippocampal neurons at different stages of differentiation and maturation (0, 1, 3, 6, 12, and 15 days in vitro (DIV)) (Fig.
1a , cluster 4; Supplementary Data 2), and upregulation of genes with important functions in postmitotic neurons such as Snap25, encoding a SNARE protein regulating neurotransmitter release, or the brain-specific cell adhesion molecule Ncam2 (Fig. 1a , cluster 2; Supplementary Data 2), similar to what was observed in a previous study  31. Among the differentially expressed MCA genes, only upregulated genes were considered as candidates for further analysis. To evaluate the validity of the microarray results we performed quantitative real-time PCR (qRT-PCR) analysis on a subset of upregulated candidates and, as controls, also on some genes that did not display differential expression. Candidates were shortlisted based on evidence from previous studies supporting their physical and/or functional association with the microtubule cytoskeleton.
One-third of the medium was replaced every 4-5 days. HEK293T, Neuro2A cell lines (both from ATCC; not tested for mycoplasma, not in ICLAC database of commonly misidentified cell lines), or mouse embryonic fibroblasts were grown in DMEM containing 10% FBS. All cells were kept at 37 °C in a humidified atmosphere containing 5% CO 2. Microarray and qRT-PCR. For two independent microarray replicas, total RNA was extracted from mouse embryo hippocampal neuron cultures at different time points of differentiation (0 DIV, 20 h, 3 DIV, 6 DIV, 12 DIV, 15 DIV) using a commercial RNeasy minikit (Qiagen) according to the manufacturer's instructions. Then, 25 ng total RNA was amplified using the TransPlex® Complete Whole Transcriptome Amplification Kit (Sigma; reference WTA2) and subsequently labeled using GeneChip Mapping 10K Xba Assay Kit (Affymetrix; catalog no.
Normalized expression was quantified using CFXManager software with 2-ΔΔCT method where β-actin is the reference target. Plasmids. The shRNA-expressing plasmids used in this study are based on the pLKO. 1 backbone and are part of the Sigma-Aldrich MISSION shRNA collection. The following target sequences were used: shNek7#1: GCATCATTCATTGAGGA TAAT; shNek7#2: CGTGGACAATTTAGTGAAGTT; shNek6#1: CACCGACC TGACATTGTATAT; shNek6#2: CCATCCGAATATCATCAAGTA; shNek9#1: GCTCTGATATCTGTACCTCAT; shNek9#2: CGACAACATCATTGCCTACTA. A pLKO. 1 plasmid expressing a scrambled sequence (CAACAAGATGAAGAGC ACCAA) was used as control. A commercial Eg5 shRNA cloned in plasmid pGIPZ was obtained from Dharmacon (RHS4480). To introduce multiple silent mutations for rendering Nek7 shRNA resistant for rescue experiments, or to introduce point mutations to generate the kinase-dead NEK7 (D179A) and the constitutively active (Y97A) mutants of NEK7, PCR mutagenesis was performed on the cDNA of mouse Nek7.
Then, SDS sample buffer extracts were prepared from the upper side of the membrane. All samples were clarified by centrifugation and boiled in sample buffer for SDS-polyacrylamide gel electrophoresis. Proteins were separated and transferred onto polyvinylidene difluoride membranes (Millipore). Membranes were blocked with 5% milk powder in TBST (20 mM Tris, pH 7. 5, 150 mM NaCl, 0. 05% Tween-20) for 1 h and probed overnight with primary antibodies diluted in tris-buffered saline with Tween-20 (TBST). The relative intensity of the bands was quantified using Quantity One 1-D analysis software (Bio-Rad Laboratories). Uncropped images of western blots are shown in Supplementary Figs. 7 8 9. Immunofluorescence microscopy. Cultured neurons and cell lines were fixed in methanol at -20 °C for 5 min or in 4% paraformaldehyde (PFA)/4% sucrose diluted in PBS for 15 min at room temperature.