dataesr/scientific-paragraphs-categorization huggingface dataset:
Observer Alert Script We have developed a script which can alert the observer when a potential transient is in the data stream. This is based on a comparison on the percentages of data flagged between the two RFI flaggers, and various criteria which are expected to be different between transient RFI and an astrophysical signal. In this script, first the RFI flagger checks the percentage of data flagged for the entire data set in each channel-a standard output given in AOFlagger-for both the default and modified strategies. Although we expect the modified flagger to flag slightly less data than the default flagger, due to less stringent criteria, a transient in the data stream would also create a difference in the percentage flagged between the two methods. As such, the difference between the percentage flagged can then be used to then determine whether a signal is in the data stream which may be worth further examination through further selection criteria.
(Figure  3 ), the default flagger will additionally flag extra data before and after the transient which is not part of the signal. The percentage flagged will then steadily increase as the percentage of time the signal is in the measurement set increases, until it peaks at 40 seconds, and begins to decrease. This is the duration where partial flagging becomes a factor, where a fraction of the transient signal is flagged instead of the entire signal. This results in the difference in percentages flagged beginning to decrease, although this difference still remains consistently higher than when there is no transient in the data. Based on these tests, we decided that any difference in percentage of data flagged between the two strategies that exceeds 1% is of interest for further inspection for these transients.
For this, we used pyrap to sort the data by time series and to search for the following: • First, we searched the data set for baselines where the columns FLAG ROW were set, which occurs when all the data in a row of visibilities are flagged. We chose this parameter because a broadband transient signal is expected to be visible in all frequency channels in our data set, but a flag in only a few channels is expected to be RFI. We should note that because LOFAR data sets often include a flagged row in the final time step of data, we set our script to disregard the last ten seconds of the measurement set. • Next, we searched the flagged row by baseline. Specifically, we were interested in occurrences when FLAG ROW was true for a majority of baselines at a given time. We chose this parameter because a flagged row only present in a small number of baselines is likely caused by local interference, whereas an astronomical signal would be visible in most baselines (although not all, depending on the noise within that specific baseline).
Observed AOFlagger Effects From our simulation work, it appears that AOFlagger, which is relied on by many radio surveys searching for brief transient signals, could be flagging out existing bright, brief transients in the data stream. In particular, it is possible all bright transients of just a few seconds' duration sufficiently bright to be detected in the integrated images have been flagged in previous surveys such as those of Stewart et al. [35] , if they were present. Re-examination of the data's flagged sections with the observer alert script could aid transient detection. Using the observer alert script does not require the more computationally intensive imaging steps to identify previously flagged data which merits further inspection, but any astrophysical transient would originate from one point in the sky and converge to a point source upon imaging.
Effects of AOFlagger Modifications and Detection Script There are some modifications to AOFlagger that can be implemented in order to detect more transients than one would with the default settings (though, it should be emphasized, not all transients). In particular, for AOFlagger, setting SumThreshold to only operate in the time direction, and eliminating all time selection steps (i. e. , only use frequency selection), would be advisable when seeking transients of ∼minute long durations within snapshots. For transients on the order of tens of seconds long, flagging on a shorter time scale is advised, but consideration must be given to the number of time steps and resolution in the length of the data stream to ensure flagger accuracy. We note that while these modifications to the RFI flagger will still result in automated flagging of narrow-band RFI, and not have significant effects on low-level RFI in general, implementing such a modified flagger opens up the possibility that bright, broadband RFI signals will no longer be flagged.
We have also presented in Section 4. 3 a potential solution in which the differences between the default AOFlagger and modified version, along with the known distinguishing properties between RFI and transient signals, are used to alert the observer when a transient may be present in the data stream. This script could be further modified depending on the class of transients in which the observer is interested-for example, a search for minutes-long transients such as that described in Stewart et al. [35]  could use a higher difference in percentage flagged than 1%, which would decrease the number of false positives. Further, the data could be examined for the sequential occurance of the script criteria in several time stamps, which would indicate a potential transient of longer duration. We would recommend that an observer using this method choose their own parameters for the difference in percentages flagged, and the percentage of baselines with completely flagged visibilities at a given time, as these are dependent on the location of the telescope, and the resolution of the data acquired by the observer.
However, the original data would need to be reexamined in order to determine whether part of the signal was mistakenly flagged. Reexamining the data may also reveal shorter transients already flagged in the data stream, which face a significant loss. For a shorter LOFAR measurement set of just 2 minutes duration, it is equally likely that 100% of radio transients of up to 20 seconds duration would be flagged. Approximately 60% of transients on the order of tens of seconds or longer will be detected, but partially flagged. We have presented a script which can be used to alert an observer that the flagged data contains a potential astrophysical transient, which identified our simulated transients which had been flagged. This could be used for any additional investigations into whether there are any transients in already flagged RFI data, in both previous survey data and in any future data preflagged to default settings.
Acknowledgements We acknowledge support from the  European Research Council  via the  Advanced Investigator Grant  no. 24729. This work is also supported in part by  European Research Council  Advanced Grant  267697. LOFAR, the  Low Frequency Array designed  and constructed by  ASTRON , has facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the  International LO-FAR Telescope (ILT)  foundation under a joint scientific policy. We would like to thank the  LOFAR Observatory  staff for their assistance in obtaining and the handling of this large data set. We would also like to thank  Andre Offringa  for answering some initial questions. S. C. acknowledges funding support from the  UnivEarthS Labex program of Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ).
According to the Oncomine database (www. oncomine. org), the expression of ZRF1 is reduced in breast cancer  [44]. In accordance with this, a recent study analyzing tumorassociated antigens in the sera of breast cancer patients demonstrated that ZRF1 antigen and autoantibody response were suggested as a potential molecular marker of breast cancer. Moreover, the antibody response to the ZRF1 antigen was found to be higher in sera of patients with breast invasive ductal carcinoma particularly with less aggressive tumor phenotype  [45]. On the contrary, another paper investigating the role of ZRF1 in breast cancer revealed that silencing of ZRF1 impedes survival of estrogen receptor positive MCF7 cells  [68]. Here we report that ZRF1 keeps breast cancer progression at bay. ZRF1 depletion in breast invasive ductal carcinoma cells leads to events that mimic the early phases of metastasis in vitro.
Depletion of ZRF1 has a mild effect on cell proliferation properties in MCF7 and T47D cells Breast cancer is a heterogenous disease in which each subtype displays different prognosis and requires different therapy options. Given the role of ZRF1 in other types of cancer, we first aimed to explore which type of breast cancer is mainly associated with ZRF1. We analyzed the DNA copy number and mRNA expression changes of ZRF1 employing the TCGA breast cancer dataset (dated June 30th, 2016) and by using the publicly available cBioPortal software  [46, 47]. According to the alteration frequency analysis, most of the DNA copy changes (mutation, deletion or amplification) related to ZRF1 are enriched in breast invasive ductal carcinoma (Supplementary Figure  1A ). Based on the mRNA expression changes of ZRF1, breast invasive ductal carcinoma is ranked first among other breast cancer types (96.
As microscopy images indicated, control cells were responsive to RA mediated differentiation and were capable of generating 2 times more lipid droplets after the treatment. In contrast shZRF1 MCF7 cells were not responsive to RA mediated differentiation and failed to generate lipid droplets. Interestingly, shZRF1 cells had more lipid droplets in vehicle treated control conditions compared to the RA treated conditions suggesting an abnormal differentiation capacity (Supplementary Figure  7C ). In addition, the transcriptome data indicates a central role for ZRF1 in the regulation of cell movement in MCF7 cells (Figure  7A ). To identify if this finding is not just limited to ER (+) breast ductal carcinoma (MCF7), we additionally performed wound healing assays in ER (-) breast ductal carcinoma cell lines (MDA-MB-231, MDA-MB-453).
TCGA analysis For genomic alteration and mRNA expression analysis, the publicly available cBioPortal software was used: http://www. cbioportal. org/ [46]. The TCGA breast cancer dataset from June 30th, 2016 was chosen for the analysis. In the software, putative copy number calls on 1080 cases were determined using GISTIC 2. 0. Values: -2 = homozygous deletion; -1 = hemizygous deletion; 0 = neutral/no change; 1 = gain; 2 = high level amplification. Mutations were determined by whole exome sequencing. mRNA expression data were determined as mRNA z-scores (RNA Seq V2 RSEM). The link for the aforementioned analysis is shown in the following: http://www. cbioportal. org/index. do?session_id=5abcb604498eb8b3d5656c7a.
Tumor-spheroid based migration assay Tumor-spheroid based migration assay was performed as published with minor changes  [51]. Briefly, 1 × 10 3 MCF7 cells per well were dispensed into ultralow attachment 96-well plates (Corning-Ref:7007) and incubated for 5 days. Flat-bottomed, 96-well plates were coated with 0. 1% (v/v) gelatin (Sigma) in sterile water for 1 h at 37° C. A total of 200 μl/well of culture medium supplemented with 2% (v/v) FBS was then added. 5 days old spheroids were transferred into the prepared 'migration' plate and were allowed to adhere. Images were acquired with a Leica DM-IL microscope. The total area of each spheroid was measured at days 5 and 7 using the ImageJ software.
Reads were mapped to the Human Ensembl GRCh38 version 84 reference genome  [97]  using STAR  [98]  version 2. 5. 2b, allowing up to 2 mismatches, a minimum intron length of 21, discarding reads mapping to more than 10 loci, and eventually keeping only the primary alignment. Quality control of the sequenced reads was done with FastQC, dupRadar  [99]  and other in-house developed tools. We then counted reads on genes using featureCounts  [100]  from the Subread package version 1. 5. 1 and using the gene model provided by Ensembl for the same assembly version (GRCh38 version 84). We did the differential expression analysis with DESeq2  [101]  version 1. 14. 1, with the default Wald test to calculate the significance. The GO enrichment analysis was done using the clusterProfiler package for R.
Cell differentiation Cell differentiation experiments were performed as published  [55]  with minor changes. Briefly, MCF7 cells grown on cover slips were treated with 10 µM all-trans-Retinoic acid (ATRA) (Sigma) for 96 hours. The same volume of DMSO was added to the cell culture medium as vehicle control. The cell culture medium was replenished after 48 hours. After 96 hours, coverslips were fixed with 4% PFA for 20 min at RT. After permeabilization with 0. 5% Triton in 1xPBS, lipid droplets were stained with 1/2000 dilution of a Nile Red stock (1 mg/ml in 1xPBS) for 5 min and mounted with Fluoromont G. Lipid droplets were counted using the ImageJ software.
ACKNOWLEDGMENTS AND FUNDING This work was supported by grants from the  Boehringer Ingelheim Foundation. We are grateful to the IMB microscopy and flow cytometry facilities for their expert help and assistance. We would like to thank  Dr. Luciano Di Croce  for shRNA plasmids and  Dr. George Reid  for MCF7, MDA-MB-231 and MDA-MB-453 cells used in this study. We thank the members of the  Richly laboratory  for comments on the manuscript.
Variant annotation The Variant Effect Predictor (https://useast. ensembl. org) was used to annotate all variants with in silico impact prediction by SIFT and Polyphen2 software, analysis of 3-dimensional (3D) structure, amino acid conservation, and the Combined Annotation Dependent Depletion (CADD; http://cadd. gs. washington. edu/home). Population-variant allele frequencies were determined from the Exome Aggregation Consortium (ExAC; http://exac. broadinstitute. org), and 1000 Genomes (http://www. internationalgenome. org/) (supplemental data). The RTEL1 variants identified in our study were annotated using the isoform 3 (1300 aa; NM_001283009. 1; Ensembl: ENST00000360203. 9). A variant was considered rare if it was novel or had a minor allele frequency of less than 0. 1% in ExAC or 1000 Genomes.
Assessment of variant pathogenicity We screened the KCH and NIH cohorts (n 5 516) for inherited mutations in telomere-associated genes. A total of 23 rare RTEL1 variants were identified in 27 unrelated patients from both cohorts (Figure  1A ). According to ACMG criteria, 7 variants were classified structure information obtained by comparative modeling predicted 14 of 23 RTEL1 variants as substitutions that may destabilize the domain 3D structure in which they are located or affect the protein partner interactions of RTEL1 (supplemental data). (H) The frequency of potential deleterious RTEL1 missense and LoF in the ExAC database as pathogenic or likely pathogenic (c. 30111 G. A, M652T, D719N, G951S, F1262L, R974X, and R986X; Table  1  and Figure  2B ), including 2 stop codons previously reported in patients with HH syndrome or IPF.
Comparison of RTEL1 rare variants to population database To ascertain whether likely pathogenic RTEL1 variants were enriched in inherited BMF syndromes, we compared frequencies of these variants from our cohorts to the ExAC database. As we identified both loss of function (LoF) and missense RTEL1 variants in our patients, ExAC data were also stratified for comparison purposes. In the KCH cohort, both LoF and missense RTEL1 variants were identified, but all likely pathogenic variants led to RTEL1 LoF. These variants were enriched in this cohort in comparison with ExAC (1. 31% vs 0. 05%; odds ratio [OR], 25. 2; 95% confidence interval [CI], 11. 27-58. 67) (Figure  1H ). No difference was observed between frequency of rare missense RTEL1 variants in the KCH cohort (2. 8%) and ExAC population (2.
4%). Missense RTEL1 variants (but not LoF) were identified in the NIH cohort. Frequency of rare RTEL1 missense variants was increased in comparison with ExAC (16. 9% vs 2%; OR, 7. 90; 95% CI, 3. 845-15. 55). However, not all missense variants were predicted to be deleterious. Because it is not feasible to assess the pathogenicity of RTEL1 variants in ExAC on the basis of ACMG criteria, we stratified RTEL1 variants from this database on the basis of their CADD phred scores. Missense variants with CADD scores higher than 20, but not greater than 30, were still enriched in the NIH cohort in comparison with ExAC (Figure  1I ). In both cohorts, the enrichment of missense RTEL1 variants, even when stratified by CADD scores, appeared to be dependent on patient selection criteria between the cohorts.
RTEL1 haploinsufficiency associates with telomere shortening and single-strand 39 overhang erosion independent of length We first assessed patients' telomere integrity by measuring overall TL. Sixteen patients had very short telomeres (below first percentile of age-matched controls), and 5 had short telomeres (below 10th percentile). As was expected, all patients with likely pathogenic RTEL1 variants that led to protein LoF (R974X, R986X, and c. 30111G. A) or that were biallelic presented with very short from KCH and NIH had CADD over 20 as well. The CADD of 15 was selected as a cutoff to predict a variant as deleterious on the basis of the frequency identified in the ExAC database. HD1, helicase domain 1; HD2, helicase domain 2; HNL-1, first harmonin-N-like domain. The RTEL1 variants identified in our study were annotated using the isoform 3 (1300 aa; NM_001283009.
In summary, RTEL1 haploinsufficiency correlated with both accelerated telomere shortening and 39 overhang attrition independent of length, and TL assessment alone may not identify all primary telomere defects. The pathogenicity of heterozygous RTEL1 variants must be evaluated with caution, because many very rare variants are present in population databases. In 5 patients, the RTEL1 variant (M344T, P867L, and P996H) was not etiologic. The number of patients in which RTEL1 variants are not pathogenic may be higher, because 13 variants were of uncertain significance. The combination of different tools for in silico prediction, wellestablished criteria to classify variants, a variant allele frequency control from ExAC, familial investigation, and functional data were critical in increasing the confidence of our analysis and in providing evidence that RTEL1 variants that were classified as likely pathogenic were etiologic.
Acknowledgments This work was funded by  Bloodwise UK  (program grants  10024  and  14017 ); by the  Intramural Research Program of the National Heart, Lung, and Blood Institute, National Institutes of Health ; and by the  São Paulo Research Foundation  ( FAPESP; grant 13/08135-2 ). F. G. -R. was a recipient of a  FAPESP  scholarship (grant  2015/19074-0 ).
5 m, 0. 7 m, 1. 1 m), and the other is placed in the bottom layer (0. 3 m above the ground). After each spray operation, collect water-sensitive paper into an envelope and seal it for storage. WSP was scanned in grayscale at 600 DPI to produce a digital image in the lab. Images were analyzed using DepositScan™ software (U. S. Department of Agriculture, Agricultural Research Service, Wooster, OH) to calculate droplet information. In this study, the coverage, droplet density, and droplet deposition were used to evaluate the spray effect.
2 µl Taq DNA Polymerase (Qiagen, Hilden, Germany). Thermocycling conditions were optimized to shorten reaction times and included initial denaturation at 94 °C for 300 s, followed by 35 cycles of 94 °C for 30 s, annealing at 52 °C for 45 s and extension at 72 °C for 60 s; then further 600 s at 72 °C for final extension. All PCR products were purified using a commercial kit (QIAquick PCR Purification Kit, Qiagen GmbH, Hilden, Germany) following the manufacturer's instructions and sent for sequencing in one direction using the forward primer to an external service (Genechron S. r. l. , Rome, Italy). The same sequence of 612 bp in length was obtained in all samples and compared with sequences present in the GenBank database by similarity search using the Basic Local Alignment Search Tool (http://www.
Genetic matching with Asian populations of Trissolcus japonicus We used the barcode approach to identify and exclude Asian T. japonicus populations that were highly divergent from the "Ticino population. " Mounted specimens (three males and three females) recovered from three parasitized egg masses from Ticino were shipped to EBCL along with 10 females from the laboratory colony kept in the quarantine at CABI Switzerland, which originated from the Beijing Province, China (ESM1). Genomic DNA from all specimens was extracted according to  Gariepy et al. (2014) , except those from mounted specimens which were non-destructively isolated as described in  Giantsis et al. (2015). Amplification of all barcode sequences and their analysis were done as described in  Ganjisaffar et al. (2018). All sequences of 674 bp in length generated from this study are deposited in the GenBank (Table  2 ), and all residual DNA extracts are archived at EBCL (Table  2 ).
Of note, when this study was initiated, only 24 barcodes were deposited in the GenBank database. Twenty-three of the 24 are from Japan and most are published by  Matsuo et al. (2014). For this study, we had access to a not yet published EBCL database of 127 barcodes of T. japonicus mostly from laboratory colonies derived from natural populations collected in China, Korea, Japan and USA (EBCL custom database). Searches for sequence similarity against this database confirmed the close similarity of the "Ticino sequence" with those of Japanese origin. Therefore, the 26 sequences generated from this study were aligned with 10 "Japanese T. japonicus" sequences from this custom database and 22 sequences retrieved from GenBank following the procedure described in  Ganjisaffar et al. (2018). Two sequences from GenBank were not considered in our data set as the sequences were not of adequate length.
The actual pathways of entry for T. japonicus are unknown, but it is likely that either plants carrying parasitized egg masses of Asian Pentatomidae (H. halys or other hosts, see  Zhang et al. 2017)  or diapausing adults were introduced. As a result of population size bottlenecks and genetic drift in small founding populations as likely experienced by the T. japonicus "Ticino population," the level of haplotype diversity is reduced to a unique haplotype. Our haplotype analysis found a best match of the "Ticino populations" to Japanese populations so far, possibly indicating an introduction from Japan. Although our custom database is quite representative of the geographic distribution of T. japonicus, we cannot entirely exclude that the haplotype H1 may also be present in other parts of T. japonicus' native range, e.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant  Agreement No. 641456. Additional funding was provided by the  Phytosanitary Service of the Canton Ticino , and the  Associazione frutticoltori ticinesi. We would like to thank  Darren Blackburn ,  Jessica Fraser ,  Chelsey Blackman ,  Taylor Kaye ,  Anna Grunsky ,  Christie Laing ,  Mariah Ediger , Lindsay.
Determination of trans-epithelial electrical conductance The Ringer's solution (pH 7. 4) used comprised 25 mM NaHCO 3, 2. 4 mM K 2 HPO 4, 115 mM NaCl, 1. 2 mM MgCl 2 , 1. 2 mM CaCl 2 , and 0. 4 KH 2 PO 4. The solution was aerated continuously in the Ussing chambers at 37 °C with air containing 5 % CO 2. The spontaneous transmural electrical potential difference (PD) between the mucosa of serosa jejunum and the mucosa of the luminal jejuna was determined through 3 M KClcontaining 4 % agar bridges on the two sides of the jejunum linked to high-impedance voltmeter and calomel half-cells. Through short-circuiting current (I sc ), 0 mV PD was sustained for the duration of the study  [11, 12]. A continuous record of I sc , which is a measure of trans-epithelial ionic permeability, was produced with Biodaqsoft software.