dataesr/scientific-paragraphs-categorization huggingface dataset:
Continuing our investigation, ChIP-seq experiments were extended to the GM12878 (B-LCL) cell line, unveiling the binding of ZNF24, CTCF, and cohesin subunits RAD21 and SMC3 at the rs1771575 site (Figure  3A ). The interaction between CTCF and the cohesin complex is visually depicted in Figure  3B. Moreover, the analysis of ZNF24 binding via a position weight matrix reaffirmed a preference for binding to chromatin in the presence of the reference allele of rs1771575 (Figure  3C ). We further augmented our understanding by conducting additional analysis of ChIP-seq data for CTCF binding, utilizing read counts from diverse immune cell lines as sourced from the ADASTRA database (Supplemental Table  S3 ). This meticulous examination revealed that the count of mapped reads carrying the rs1771575-C allele significantly differed when the alternative allele was present, indicating an allele-specific binding event.
This observation underscores the preferential binding of CTCF to chromatin in the presence of the C allelic variant of rs1771575 (Figure  3D ). (B-LCL) cell line, unveiling the binding of ZNF24, CTCF, and cohesin subunits RAD21 and SMC3 at the rs1771575 site (Figure  3A ). The interaction between CTCF and the cohesin complex is visually depicted in Figure  3B. Moreover, the analysis of ZNF24 binding via a position weight matrix reaffirmed a preference for binding to chromatin in the presence of the reference allele of rs1771575 (Figure  3C ). We further augmented our understanding by conducting additional analysis of ChIP-seq data for CTCF binding, utilizing read counts from diverse immune cell lines as sourced from the ADASTRA database (Supplemental Table  S3 ). This meticulous examination revealed that the count of mapped reads carrying the rs1771575-C allele significantly differed when the alternative allele was present, indicating an allele-specific binding event.
We also identify a preferential binding of CTCF to chromatin when the rs177157C allelic variant is present, which may influence enhancer-promoter interactions, favoring coordinated and cell-specific activation of the targeted FCGR gene expression that contributes to the acquisition of an efficient immune response. In this study, we utilized various tools to conduct a large-scale immune cell-specific analysis of expression quantitative trait loci (eQTL), providing valuable insights into the impact of the rs1771575 variant on the coordinated expression of the FCGR gene repertoire in neutrophils. Our analysis of the ImmuNexUT database revealed that the rs1771575-CC variant is significantly associated with lower gene expression of FCGR2B inhibitory receptors and FCGR3A activating receptor transcripts in neutrophils.
Bioinformatic Identification of Non-Coding Variants The selection of Fc gamma receptor SNPs was conducted using a series of filtering criteria. SNPs with expression quantitative trait locus (eQTL) annotation in whole blood for FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B genes were obtained by querying the GTEX version 8 database  [32]  (Supplemental Table  S1 ). Subsequently, the RegVar tool  [33] , which utilizes the GTEX version 7 database, was employed to prioritize the SNPs based on gene-specific and tissue-specific annotations. The top 20 SNPs with the highest scores were selected (Supplemental Table  S2 ). While a RegVar threshold of 0. 23, optimized for sensitivity and specificity, was recommended for whole blood, we found that the specificity at this threshold was too low, with a false positive rate of 31%  [33].
Therefore, a decision was made to substantially reduce the false positive rate to 0. 2%, guided by the specificity study conducted by Lu et al. [33]. Furthermore, the eQTL annotations of these 20 selected SNPs were cross-checked in two independent immune cell eQTL databases, namely ImmuNexUT  [45]  and the ebi eQTL catalog  [46]. SNPs were retained if they had annotations in both of the aforementioned databases. The positions of the eQTL candidates were cross-referenced with ATAC-seq regions in immune cells  [47]  and regions binding transcription factors in immune cells using the ReMap catalog  [54]. The ReMap data were filtered by primary NK, T, and B lymphocytes, monocytes, macrophages, neutrophils, peripheral blood cells, and monocytic and lymphocytic cell lines. The RSAT tool  [55]  was then employed to assess whether the allele of an SNP influenced the affinity of the transcription factor for the sequence containing the polymorphism.
The databases of selected transcription factor motifs in the RSAT parameters were JASPAR and ENCODE. All other parameters were set to their default values. The presence of epigenetic marks (ATAC-seq, H3K4me1, H3K4me3, H3K27ac, and H3K27me3) in monocytes, macrophages, NK cells, and B cells was investigated using the WashU Epigenome Browser and data from the CistromeDB database and ENCODE portal  [49]. The threshold allele frequency for the minority allele was set at 10% to ensure sufficient statistical power for the analysis of malaria cohorts. This criterion avoids selecting SNPs with very low representation in the cohorts used. LDlink, a tool including LD information from the 1000 Genome project, was used to identify the SNPs in linkage disequilibrium (LD)  [58]. Through this comprehensive selection process, the most relevant and informative Fc gamma receptor SNPs were identified and prioritized for further analysis in the context of malaria infection.
Genotyping of FCGR Variants Blood samples were collected using venipuncture, and DNA was extracted from mononuclear cells separated by a Ficoll-Hypaque density gradient following the described procedure  (27). Before genotyping, the samples underwent whole-genome amplification using the primer extension pre-amplification method. The genotyping of the FCGR2B-232I/T (rs1050501) SNPs was performed using a homemade TaqMan discrimination assay. The TaqMan assay was designed using Software Primer Express v3. 0 (Applied Biosystems, Inc. , Foster City, CA 94404 USA). The TaqMan genotyping assay for FCGR2B included a sense primer (TGGGGATCATTGTGGCTGTG), an antisense primer (TACACTGCTCTCCC-CAAGAC), and two probes, MGB (ACTGGGACTGCTGTAG for the C allele coding for Thre and ACTGGGATTGCTGTAG for the T allele coding for Iso).
JASPAR matrix ID and genomic coordinates (GRCh38) are also provided. (D) CTCF binding ChIP-seq analysis. Analysis of ChIP-seq data for CTCF binding using read counts from several immune cell lines demonstrates allele-specific binding. The number of mapped reads carrying rs1771575-C is significantly different in the presence of the alternative allele, indicating an allele-specific binding event. These data were obtained from the ADASTRA database.
78 × 10 -13 - - - FCGR3B Macrophage, naive 5. 62 × 10 -13 - - - FCGR3B Macrophage, Listeria 6. 17 × 10 -13 - - - FCGR3B CD16 Monocyte, naive 1. 07 × 10 -11 a p values were obtained from the ImmuNexUT database. b p values were obtained from the eQTL EBI catalogue.
Methods This questionnaire was written with the guidance of caregivers from 4 family associations for KCNQ2-related encephalopathies (European KCNQ2 Association Odv, Association KCNQ2 España, KCNQ2 Einblick Beratung Netzwerk E. V. , KCNQ2 France Mila) and with the collaboration of the Alliance of Rare and Complex Epilepsies. The survey was developed using G Suite TM tools (Google, CA, USA) and shared through the four association networks. The survey was available in Italian from 20th December 2020 to 10th January 2021. The questions were then translated into English, French, German, and Spanish and made available in all five languages from 10th February to 30th March 2021. A sixth version in Polish was translated and made available online from the 15th of March to the 15th of June 2021. One caregiver per patient was allowed to complete the survey, anonymously.
Statistical analysis Statistical analysis was made using SPSS Statistics Version 25. 0 (IBM Corp, NY, USA). Since the number of responders varied in each question, frequencies are reported as percentages, and the number of answers over the total number of responders is in parentheses. Taking into consideration the varying sample size, all statistical analyses were carried out using non-parametrical methods and normal distribution was not confirmed. Continuous variables are reported as means and ranges in parentheses. Correlations between category variables were carried out using chi-square (X 2 ). Relationships between continuous and category variables were carried out using non-parametric analysis, U Mann-Whitney, and Kruskal-Wallis ANOVA depending on the number of subgroups (two or more than two respectively).
Results Demographical data are reported in Table  1. Moreover, 4 groups of comparable size were identified; 24% were younger than 3 years old, 25% were between 3 and 6 years old, 27% were between 6 and 9 years old, and 24% were older than 9 years old. We received answers from 14 countries on 3 continents, however, 92% of answers were collected from European families, in particular, 22% of all 80 families are from Italy.
Conclusions The findings from a worldwide large cohort of caregivers of patients with KCNQ2-DEE were collected. This study is based on the collaboration of clinicians with caregivers and patient associations. This approach enabled not only to report the clinical features of KCNQ2-DEE individuals with a wide age range but also to define their relevance with respect to the quality-of-life of their families and caregivers; the data obtained might help in defining more clinically meaningful primary and secondary objectives when planning interventional clinical trials for KCNQ2-DEE patients. Our study identifies three subgroups of patients based on neurodevelopmental features, regardless of seizure frequency, with different degrees of severity and differences in epilepsy onset and course. A particularly early epilepsy onset could be an early marker of a more severe phenotype.
Acknowledgements We would like to extend our thanks to  Mrs. Sylvia Sestini  who gave her invaluable suggestions for the paper form and data presentation. This received no specific funding. We would like to thank especially the  EJP-RD Treat KCNQ consortium  for harboring the spirit of this study and for further developing better treatments for potassium channel-related disorders. Prof. F. Darra  was funded under the  National Recovery and Resilience Plan (NRRP) ,  Mission 4 Component 2 Investment 1. 3   -Call  for tender No. 341  of  15/03/2022  of  Italian Ministry of University  and Research funded by the  European Union -NextGenerationEU ; Project code  PE0000006 , Concession Decree No. 1553  of  11/10/2022  adopted by the  Italian Ministry of University and Research ,  CUP D93C22000930002 , ''A multiscale integrated approach to the study of the nervous system in health and disease" (MNESYS).
Conclusions A 3-D S-wave velocity model of La Palma island was obtained by unifying results from a new ANT model obtained in this study and a LET model obtained by D' Auria et al. 3. We applied ANT on data recorded by 38 broadband seismic stations to extract the dispersion curves of all the station pairs. Subsequently, we obtained the 2-D group velocity maps through a non-linear multiscale inversion taking the topography into account  33. Finally, we derived some S-wave 1-D profiles using a Bayesian tridimensional inversion. The final 3-D S-wave velocity model shows the presence of two high-velocity zones (H1 and H2) and four low-velocity zones (L1, L2, L3 and L4). The high-velocity anomalies H1 and H2 are interpreted as related to a plutonic intrusion related to the island's ancient volcanism and more recent solidified intrusive dyke complexes, respectively.
At greater depths, the velocity remains almost constant. Figures S10. E-F and G-H show the 1-D S-wave velocity profiles in depth for anomalies L3 and L4, respectively. In both profiles, the velocity increases rapidly at shallow depths and does not vary significantly at greater depths. In order to determine the depth resolution of our S-wave velocity model, we computed the group velocity sensitivity kernels for the fundamental mode of Rayleigh wave at different periods using the software senskernel-1. 0  55  (Fig. S11  of the supplementary materials). We note that the kernels have a sufficient resolution down to 3 km depth and no resolution below 5 km. Unification of S-wave velocity models. The S-wave velocity models obtained by ANT (this study) and LET  3  have different resolutions as a function of depth.
Acknowledgements The  INVOLCAN  team was supported by the project  VOLRISKMAC II  ( MAC2/3. 5b/328 ) co-financed by the  EC Cooperation Transnational Program   MAC 2014-2020 , " Impulso a la energía geotérmica de   alta entalpía en Canarias " ( 187G0132 ), financed by  Gobierno de Canarias , and  "Desarrollo de la geotermia en La Palma"  ( 425/226613 ), financed by  Cabildo Insular de La Palma. This paper benefited from various Python packages including Matplotlib 57 and Obspy 58,59.
Biofilm formation assay X. citri inocula containing pBRA-and pBBR-derived plasmids were grown for 12 hours in 2xTY supplemented with ampicillin, gentamicin, spectinomycin and 0. 3% arabinose. After growth, X. citri inocula OD 600nm was normalized to 0. 05 and incubated for 5 days at 30 o C in a laminated microscopy chamber (Nu155411; Lab-Tek, NUNC). Biofilm images were acquired using a Nikon Eclipse Ti microscope equipped with a 100x magnification objective (CFI Plan Apo Lambda 100XH) and a fluorescence filter for GFP (GFP-3035B, Semrock). Images were collected from the base to the top over 20 µm in the Z axis at 0. 5-µm intervals, and stacked using the FIJI software  (Schindelin et al. 2012). The results shown are representative images of 3 independent experiments. For the 24 wells plate-based biofilm assay, X.
Bioinformatics Prediction of the structure of the X-Tfi XAC2610 (54-267)/X-Tfe XAC2609 (1-194) complex was performed using the UCSF ChimeraX (v1. 4, 2022-06-03) software that includes an integrated link to the ColabFold-AlphaFold2 suite  (Mirdita et al. 2022; Jumper et al. 2021; Varadi et al. 2022). The search for homologous proteins was performed using the Blast algorithm  (Dooley 2004 ) with X-Tfi XAC2610 (54-267) as a query against refseq NCBI protein data bank using an expect values threshold of 1x10 -7 , and outliers and sequences with 99% redundancy were manually discarded. Next, the sequences were realigned with Muscle  (Edgar 2004 ) using the X-Tfi XAC2610 sequence as a reference. The realigned sequence file (S1 Supplementary File) was used as input for the Weblogo software  (Crooks et al.
Acknowledgments We thank  Dr. Frederico Gueiros-Filho  for support in the microscopy studies,  Central Analítica at Instituto de Química, Universidade de São Paulo  and  Dr. Ii-Sei Watanabe  and  Dr. Sonia R. Yokomizo  from the  Instituto de Ciências Biológicas, Universidade de São Paulo  for their support and assistance in transmission electron microscopy sample preparation and image acquisition. This work was supported by grants from the  Fundação de Amparo à Pesquisa.
A pre-trained MobileNet CNN (Howard et al. 2017) was trained using a dataset containing 50 distinct images of both opaque and transparent colonies (with multiple instances per photo). The model was validated with 23 images of opaque colonies and 26 images of transparent colonies from our colonies. Confidence index for predictions was computed using the np. argmax(prediction) function from the NumPy library (version 1. 23. 5). Confidence level associated with each prediction was accessed using prediction[0][class_idx]. Tensor Flow optimization, data augmentation, and learning rate reduction were employed for training the CNNs.
This will include the global CL-transition of Mrk 1018 a few years ago as well as the outburst of 2020. In this paper we analyse multi-wavelength data taken before and after the optical outburst to probe the response of the various AGN structures. For this reason the sections on each type of data are split into the latest available observations before the outburst and the earliest available afterwards. Our data set includes X-ray and UV data, optical (photometric and spectroscopic) data, and IR data. Throughout the paper we use the wording 'Mrk 1018' to refer to the entire system of AGN and host galaxy. When referring to the Mrk 1018 host, we specify the host galaxy and similarly when referring to the AGN. In Sect. 2, we outline the multi-wavelength data we use. We present the details on how to create an so-called AGN-only optical light curve of Mrk 1018 based on STELLA data in Sect.
3. We also analyse all multiwavelength data to investigate any structural changes in this section. In Sect. 4, we present our results and in Sect. 5 discuss their implications before concluding in Sect. 6. When we derive luminosities, we use the cosmological parameters H 0 = 68 km s - 1  Mpc -1 , Ω m = 0. 3, and Ω Λ = 0. 7 (Planck Collaboration XIII 2016). Using Mrk 1018's redshift (z = 0. 043), we infer a physical scale of 0. 873 kpc per arcsecond. The magnitude system used is that of SDSS, which is approximately similar to AB magnitudes. The photometric zero point in the SDSS u-band is +0. 04 mag relative to the AB system  (Fukugita et al. 1996). Uncertainties are 1σ (68. 3% for one parameter) confidence intervals unless otherwise stated.
VIMOS and GMOS images Through our collaboration with the Close AGN Reference Survey (CARS;  Husemann et al. 2017 Husemann et al. , 2019)) , we have access to an additional 20 images of Mrk 1018 taken by the European Southern Observatory's (ESO) VIsible MultiObject Spectrograph (VIMOS;  Le Fevre et al. 1998)  with exposure times of 300 s each and 21 images obtained by GMOS (the Gemini Multi-Object Spectrograph;  Hook et al. 2004; Gimeno et al. 2016)  with exposure times of 100 s each. All these data were taken in the time period from July 2017 to January 2019, which is after the AGN transformed from a type 1 Seyfert galaxy to a type 1. 9, therefore during a faint phase, but before the new outburst outlined in this paper.
However, they do provide important information about the overall strength of the IR output. The data and images are downloaded from the NASA/IPAC Infrared Science Archive 1. These data have already been run through the NEOWISE processing pipeline which firstly performs instrumental calibrations, detects sources in the images, calibrates for photometry and astrometry, and flags contaminating artefacts 2. We create a python script using the IRSA API (application programming interface), that downloads data on WISE sources based on input co-ordinates and a user-defined radius. The source fluxes and magnitudes are then re-binned so that all entries are combined for each six-month WISE epoch.
The raw data are downloaded from the ESO archive 3  and reduced with the standard ESO FORS2 pipeline, the details of which are available in the documentation 4. Through our collaboration with CARS, we also obtained a proprietary data cube taken with the Kilofibre Optical AAT Lenslet Array (KOALA;  Zhelem et al. 2014 ) on the Anglo-Australian Telescope (AAT). This was observed on 4 September 2019, less than two months before the VLT observation. We took the spectrum from the central spaxel in the image, oriented on the AGN. However, this observation is taken with the 580V grating, which only covers the range 3700-5800 Å. Consequently, only Hβ is covered. This spectrum shows that the Hβ line profile did not vary significantly on time-scales of months in the pre-outburst phase.
Aperture photometry We visually screened all images (three per night) and discarded images affected by deformations (due to strong winds) and ghost patterns. This results in 20 nights that have at least one observation that survived our screening. All exposures are taken in the u -band. We analysed the exposures photometrically using Astropy's PhotUtils package 7. The background in STELLA images varies both spatially and in different exposures so a simple median is not sufficient. To compensate for the variable background, we used the PhotUtils Background2D function 8. We firstly divided the image into boxes of 35 by 40 pixels; we chose this size such that the box size is larger than the sources in the image, but small enough to capture the spatial variability of the STELLA background. We then performed sigma clipping with σ = 3 in each area to remove sources from the background estimation in each box.
We refer to the average value per night as an 'observation'. These averages are weighted by the statistical error in the individual measurements. Using the reference stars around Mrk 1018, we explored potential instrumental effects. The upper panel of Fig. 3  shows the average integrated counts per observation for the STELLA data set. These light curves show that the three stars' light emissions vary in a similar manner and therefore we assumed that they remain at constant brightness throughout. Reference stars one and two have respectively bluer and redder u -g colours than Mrk 1018 in the Sloan Digital Sky Survey (SDSS) database 9  (Ahumada et al. 2020). The SDSS u and g magnitudes and u -g colours are listed in Table  1  for Mrk 1018 and the reference stars. It is evident that reference star three is closest in colour to Mrk 1018 so we normalised to this one.
The latter was chosen in order to test if the light curve follows the characteristic shape of the decline of a TDE outburst. Canonically, this is described by a a power law with an exponent of -5/3  (Rees 1988; Phinney 1989). Although  van Velzen et al. (2021)  show that this parameter can vary from -2. 0 to -0. 8, the mean power-law index of their sample is consistent with p = -5/3. We note that the functions chosen are not intended to be a precise fit to the decline as the exact shape of the outburst cannot be determined due to sunblock. Figure  5  shows the range of points considered and the linear and power-law functions plot-10 Python fitting package lmfit: https://lmfit. github. io/ lmfit-py/ 58800 58900 59000 59100 59200 59300 This implies an asymmetric outburst with a faster rise than decline.
Light curve fitting We repeated the light curve fitting procedure, outlined in Sect. 3. 1. 3, for the ATLAS data. Results are shown in Table  2  and Fig. 6. For the c-band, the linear, parabolic and power-law functions have reduced χ 2 statistics of 76, 176 and 109 respec-12 https://gist. github. com/thespacedoctor/ 86777fa5a9567b7939e8d84fd8cf6a76 tively. For the o-band, the reduced χ 2 statistics are 64, 146 and 127, respectively. Again, the linear function is the best fit for both the c-and o-bands. We note that both ATLAS filter light curves look smoother then the STELLA light curve. We averaged the ATLAS data points over seven days to obtain a decent signal-to-noise quality, whereas the STELLA data points are single-night observations. Thus, naturally, STELLA data can show the intrinsic variability of the source on time-scales of a night, whereas the ATLAS data is smoothed out over a whole week.
XMM-Newton spectra We extracted X-ray spectra before and after the outburst using the SAS package (20. 0. 0) and HEASOFT (v6. 29), excluding observation times subject to an increased high energy background level. This left us 49 ks of good integration time in PN and 58 ks in each individual MOS for the 2019 spectrum, while the 2021 spectrum yielded 44 ks and 56 ks cleaned data for PN and each individual MOS, respectively. Pile-up does not affect these observations. We then created standard source and background spectra in PN, MOS1, and MOS2. We produced two spectra for PN: one only contained the events with pattern equal to zero (single events) and the other with patterns 1-4 (double). By not merging single and double events we increased the energy resolution in the single event spectrum. We grouped the spectra with a minimum of 20 counts per bin.