dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion N-terminal annotations of fungal NLRs are not evenly distributed. In previous studies we computationally screened N-terminal domains of fungal NLRs using profile Hidden Markov Models (pHMM) from the Pfam database directly and complemented the search with several Pfam-like inhouse models  [14, 5]. Here we expanded the most recent analysis with a more sensitive search using the state-of-the-art clustering offered by MMseqs2 and pHMM-pHMM searches with HHblits. The study increased the overall Pfam annotation coverage of N-terminal domains by about 16% (or 19% when MLKL_NTD from CDD is counted), but also highlighted remarkable deficiencies in availability of annotations. While the vast majority of longer domains is at least partially annotated, this is true only for a definite minority of shorter domains (Fig.
MLKL-related HeLo-and Goodbye-likes form the most abundant superfamily of effectors. Analyzing the annotation results, we found clusters with overlapping HeLo/HeLo-like and HeLolike/Goodbye-like hits assigned through the HHblits search. The HeLo domain is a fungal homolog of human MLKL, plant RPW8 and bacterial Bell domains, while HeLo-like is closely related  [32, 40, 20]. It is understood that upon oligomerization, these domains, whose central part is a four-helix bundle, expel a N-terminal alpha-helix to form a pore targeting the membrane and thus induce the cell death. The structure and function of Goodbye-like is yet to be established experimentally. The Helo-like/Goodbye-like cases, typical to Basidiomycota, were usually below the default threshold for direct Pfam annotation but were often assigned the MLKL_NTD annotation of the Conserved Domain Database.
(In depth study of the co-aggregation process is left for a separate study. ) The effector-side PUASM sequence was shown to be capable of forming prions in vivo in the Podospora anserina model. Interestingly, we found the PNP_UDP domain associated also with a HRAM motif variant (NLR12) and with one another amyloid-like motif discovered in this study (PF01048_40, Fig. S3 ). For NLR12, only in a few cases the effector-motif and motif-NLR pairs present in the genome were co-localized (that is encoded by adjacent genes). In some genomes, the NLR-side motifs were relatively more frequent (mean ratio 5. 7:1, Tab. 3). We speculate that the presence of many NLRs controlling the same effector could potentially relieve the need for genomic co-localization. Less diversity of ASM in fungi as compared to bacteria even after extensive search.
Computational methods Annotation of NLR N-termini. A set of 36,141 NLR proteins from 487 fungal strains was identified in a previous study through the PSI-BLAST  [115]  search among completely sequenced fungal genomes in the NCBI nr database  [14, 20]. 32 962 N-termini at least 20 amino-acids long (91%), delimited according to the NACHT or NB-ARC query matches, were further considered, of which 18,674 (57%) were annotated using direct matches to Pfam  [25]  or inhouse HMM profiles (Supplementary File 2)  [14, 20]. The set of N-termini at least 20 amino-acid long was clustered with MM-seqs2  [46]  in mode 1 (21 758 N-termini in 127 clusters, 15 105 already annotated). Then, sequences in each cluster with at least 20 members were aligned using Clustal-Omega  [116]  (Supplementary File 3) and searched for homologs in UniRef30  [47, 48]  using HHblits  [49]  (parameters: -e 0.
001 -n 2 -E 0. 01 -Z 1000000 -M 50). Subsequently, the resulting alignment was used to search Pfam (HHblits parameters: -e 0. 001 -n 1 -E 1 -Z 1000000). The clustering required mutual coverage of at least 80% of sequence length, and the annotations were only assigned to sequences which covered at least 50% of the match to the Pfam profile. The resulting cluster-level annotations were retained only if the alignment match to the Pfam profile covered at least 50% of the profile length, and assigned only to individual sequences which covered at least 50% of the match. After completing the main processing, the set of N-termini was re-scanned for the Crinkler domain (PF20147) added recently to the Pfam database. The tabularized results of the annotation are provided in Supplementary File 4. The overlapping Pfam annotations were resolved as in  [14, 20].
Results). Comparative analysis of Goodbye-, HeLo-and MLKL-likes. The largest clusters annotated as HeLo, HeLo-like, Goodbye-like and BaMLKL were submitted to AlphaFold2 structure prediction  [69]  through the ColabFold advanced notebook  [70]. Standard parameters of the notebook were applied except of (1) using the cluster alignments instead of searching genetic databases, (2) trimming off fragments just upstream the NACHT domain were applicable. Successful models -with the mean predicted lDDT score  [69]  above 70 overall, and around 80 or more for the core helix bundle -and respective ColabFold outputs are provided in Supplementary File 5. For each cluster, the highest rank model was selected and structurally aligned to the experimentally solved MLKL domain (pdb:6zvo) using TM-align  [71].
1f  and Fig. S1. Extraction of amyloid-like motifs in short N-termini. A subset of 54 NLR N-termini clusters with mean/medium sequence length at most 160/161 amino acids was scanned using the PCFG-CM software  [119, 43]  probabilistic grammatical model inferred from ten BASS families  [43]  (Supplementary File 7) with scanning window of 20 to 40 amino acids and the smoothing factor of 10 PAM  [43]. Very high scoring fragments (maximum log10 score at least 3. 5, mean log10 score above 1. 67) were found in 18 clusters with 1456 sequences. The N-terminal sequences were made non-redundant at the identity level of 90% using CD-HIT 4. 7  [87, 88]  and submitted to motif extraction with MEME 5. 0. 5  [120, 82]. For each of 51 motifs found at the E-value threshold of 1, HMM profiles were built with HHMER 3.
For further analysis only hits in N-termini shorter than 200 amino acids not located beyond position 150 were considered. Motif sequences in envelopes of 5 amino acids were tested for the beta-arch structure with ArchCandy 2. 0  [42]  using the recommended threshold of 0. 56. For each motif-containing NLR sequence, proteins coded by genes within the ±20kbp neighborhood of the genes encoding these NLRs were fetched from NCBI GenBank  [121]  or EMBL ENA  [122]  using an in-house Python (version 3. 7. 3) script (aided by requests  [123]  and xmltodict  [124]  packages). The set was confined to proteins in the length range of 200-400 amino acids, which is typical for proteins with single domain architectures known to be associated to NLRs via amyloid signaling  [33, 20]. Next, C-termini (100 amino acids) of the found neighboring proteins were scanned for the presence of the motifs using HMMER (the independent domain E-value threshold of 1e -2).
Pairwise hits of the same motifs in N-termini of NLRs and C-termini of genomically neighboring proteins are collected in Supplementary File 9. Homology search of effector domains. Homologs of NLR effector domains, as listed in  [5] , were iteratively searched using Pfam profiles: Pkinase (PF00069), Peptidase_S8 (PF00082), C2 (PF00168), PNP_UDP_1 (PF01048), TIR (PF01582), Patatin (PF01734), RelA_SpoT (PF04607), DUF676 (PF05057), HET (PF06985), PK_Tyr_Ser-Thr (PF07714), PGAP1 (PF07819), Abhydro-lase_6 (PF12697), CHAT (PF12770), TIR_2 (PF13676), HeLo (PF14479), NACHT_N (PF17100), SesA (PF17107), Goodbye (PF17109) and Helo_like_N (PF17111). First, HMM profiles for each of the domains were used for searching against a local copy of the non-redundant protein sequences database (NCBI's "nr", downloaded in November 2019)  [121]  using HHMER 3.
2. 1 with E-value of 1e -2. Found proteins were then used to build the new HMM profiles and the search was repeated until the number of hits did not change by more than 7%. In addition, all fungal NACHT (PF05729) and NB-ARC (PF00931) proteins (as listed in the Pfam database in January 2020) were retrieved. C-termini of effector domains and N-termini of NACHT/NB-ARC NLRs were extracted and -in both cases -only fragments between 10 and 150 aa were selected for further analysis. (This effectively excluded proteins with effector + NOD architectures. ) The final set included around 200k of effector C-termini and 6. 8k NLR N-termini (redundant). Identification of paired amyloid motifs. Sets of N-and C-terminals were clustered using CD-HIT to reduce redundancy at the 70% similarity threshold (separately for each effector domain, together for NACHT and NB-ARC).
Specialized searches for amyloid motifs in Basidiomycota. MLKL_NTD homologs were searched in UniProt  [117]  through the web-based hmmsearch  [118]  with standard parameters starting from the alignment of the largest BaMLKL cluster in Basidiomycota (representative protein: KIM77258), trimmed to the NACHT_N match. Hits were further restricted to GenBank sequences with length up to 400 amino acids and no Pfam P-loop_NTpase clan (CL0023) annotation at E-value of 1. Ctermini (100aa) of resulting 242 BaMLKL homologs were scanned with the PCFG BASS model (Supplementary File 7) with the same parameters as above (except the minimum scanning window length of 15). For proteomes with the most promising hits in BaMLKL homologs (log10 score above 3, eight sequences from six species), N-termini (150 aa) of all NLR proteins were again scanned with the grammars.
NLRs in these genomes were then scanned with the PCFG model and the hits exceeding the log10 score threshold of 2. 33 were aligned with their C-terminal counterparts on the per genome basis with Mafft  [125]  in the accurate mode. Finally, the alignments were curated manually (poorly aligned sequences were excluded, sequences were extended or trimmed if necessary, Fig. S6  and S7 ). Visualization. Basic data processing and visualization was conducted in Python using pandas  [126, 127] , matplotlib  [128]  and seaborn  [129]  packages, as well as in LibreOffice, GIMP and Inkscape. Multiple sequence alignments and logos were generated using TeXshade  [130]. The graph of logos in Fig. S3  was generated with graphviz 2. 40. 1  [131]. Visualizations of structural models were generated with RasMol  [132]  (Fig.
Spectroscopy data treatment. ATR-FTIR spectra were initially preprocessed using OMNICTM software (version 8, Thermo Fisher Scientific, USA): atmospheric and ATR correction. All spectra were analyzed using the OriginPro (version 2019, OriginLab Corporation, USA). The analysis included: baseline correction, smoothing using the Savitzky-Golay polynomial filter (polynomial order 2, a window size of 9 points)  [133]  and normalization to 1 for the Amide II' band. Spectra in the amide bands region (1750-1500 cm -1 ) were deconvoluted into subcomponents using the Lorentz function based on second and fourth derivative spectra. Atomic Force Microscopy. AFM images were acquired in tapping mode using a Nanoscope IIId scanning probe microscope with Extender Module (Bruker) in the dynamic modus. An active vibration isolation platform was applied.
22 µm syringe. ThT solution was dissolved in 50 mM Tris-HCl (pH= 7. 4) to final concentration 10 µM and filtered. The 90 µL of ThT buffer was mixed with 10 µL of peptide solution (concentration 400 µM) in the 96-wells plate. Samples were measured on the SpectraMax R Gemini TM XPS Microplate (Molecular Devices LLC). The measurements were conducted in RT. The excitation wavelength was set at 450 nm and the emission was recorded in the range from 470 to 500 nm. Each group of experiment contained three parallel samples and the data were averaged after measurements.
Acknowledgments. This research has been partially funded by  National Science Centre, Poland , grant no. 2019/35/B/NZ2/03997  (PI:  Prof. Małgorzata Kotulska ) (WD, MG-G), by  National Centre for Research and Development, Poland  (ncbr. gov. pl),project no. POWR. 03. 02. 00-00-I003/16  (NS), and by  Politechnika Wrocławska  statutory funds, and supported by  Wroclaw Centre for Networking and Supercomputing  grant no. 98  and the E-SCIENCE. PL infrastructure. Work in the SJS lab was supported by an  ANR  grant (SFAS,  R-17-CE11-0035 ).
Because we only simulate data in short segments around the times containing a BNS merger rather than a continuous oneyear data stream, the false alarm rate (FAR) cannot be meaningfully calculated by PyCBC Live. Rather than using an FAR threshold to indicate detection, we instead set a threshold on the network matched-filter S/N GW recovered by the search for each event of S/N GW > 9 in order for the source to count as "found. " Of the 625 independent BNS mergers, 96 are found by PyCBC Live. By averaging over the 101 different realizations of BNS merger combinations for each rate as described in the previous section, we obtain the median and 90% symmetric credible interval on the number of found events shown in Table  1. The fraction of the total number of events detected with each number of interferometers is shown in Figure  3 , along with the recovered S/N GW distribution.
WINTER Simulated Observing For each found gravitational-wave event, we use the corresponding skymap and simulated time of the event to create a realistic observing schedule with the gwemopt package  (Ghosh et al. 2016; Coughlin et al. 2018b Coughlin et al. , 2019b)). The package takes gravitational-wave probability maps as inputs, such as the BAYESTAR and bilby skymaps, subdivides the sky into tiles sized to the telescope field of view, and generates an optimized observing schedule. During O3, a network of telescopes including the Zwicky Transient Facility (Bellm et al. 2018) created GW follow-up schedules with gwemopt (e. g. ,  Coughlin et al. 2019c; Kasliwal et al. 2019) , and we expect to use the package during nominal WINTER operations in O4. We run a set of observing simulations allowing one, three, five, seven, and 14 nights of dedicated telescope time searching for the kilonova from each event.
The WINTER data processing pipeline will subtract new science images from prebuilt reference images (constructed well before the GW alert) and detect candidate kilonovae in near real time. We follow up each skymap with a ranked search strategy, in which we subdivide the sky into a fixed grid of telescope pointings. The center of each tile corresponds to a WINTER reference image for easy image subtraction during data processing. We prioritize the tiles based on the skymapgenerated probability. Finally, we create a greedy observing schedule, where the highest probability tiles are observed first, as described in  Coughlin et al. (2018b). We prioritize scheduling at least two observations of each field, spread out over the length of an observing campaign to study the lightcurve evolving over time.
Although there is a need to demonstrate reproducibility in light microscopy acquisitions, the lack of standardized guidelines monitoring microscope health status over time has so far impaired the widespread use of quality control (QC) measurements. As scientists from 10 imaging core facilities who encounter various types of projects, we provide affordable hardware and open source software tools, rigorous protocols, and define reference values to assess QC metrics for the most common fluorescence light microscopy modalities. Seven protocols specify metrics on the microscope resolution, field illumination flatness, chromatic aberrations, illumination power stability, stage drift, positioning repeatability, and spatial-temporal noise of camera sensors. We designed the MetroloJ_QC ImageJ/Fiji Java plugin to incorporate the metrics and automate analysis.
, 2022; Swift and Colarusso, 2022). We developed the MetroloJ_QC (ImageJ/Fiji Java plugin) software tool that fully automates the QC data analysis based on existing MetroloJ plugin tools  (Cordelières and Matthews, 2010; Fig. 2). Processing was automated to minimize user actions. We also implemented multichannel image processing to avoid channel splitting and separately configuring the plugin for each color channel. The plugin incorporated metrics defined in this study, tools for camera characterization and drift monitoring, and permitted the creation of report documents to quickly identify parameters within/outside tolerances. We highlighted the importance of QC measurements during new microscope setup installations and their crucial role in building up a requisite vocabulary to interact with the microscope and device manufacturers and service technicians.
Analysis The MetroloJ_QC plugin works as follows: first, the xy coordinates of the beads are identified using a find maxima algorithm on a maximum intensity projection of the stack. Beads too close either to the image edge or to another bead are discarded from the analysis (the user in the plugin can modify this distance). Subsequently, FWHM is measured, where the maximum intensity pixel within the entire 3D data set is determined and an intensity plot along a straight line through this maximum intensity pixel point is extracted in all dimensions. Finally, the bell-like plots/curves are fitted to a Gaussian function using the built-in ImageJ curve fitting algorithm to determine the FWHM. After automation of the FWHM measurements on several beads/ datasets, average values are extracted in all three dimensions, and SDs are calculated.
Statistical analysis The figures and statistical analysis were prepared using GraphPad Prism software. For LAR metric, significance was tested for the WF, SDCM, and LSCM modalities. First, the assumption of normality was tested using the Shapiro-Wilk test, with a P value <0. 0001, 0. 104, and 0. 0813 for WF, SDCM, and LSCM modalities, respectively, which indicates that the WF distribution was not normal. Therefore, a Kruskal-Wallis test was used that determines whether the median values of the modalities are different. The test gave an H value of 42. 7 (out of 141 values) and a P value <0. 0001 (****), indicating that there is a significant difference in the LAR metric result of the three modalities.
The shortest wavelength of the color pair was used to calculate the theoretical resolution, as it is the most stringent resolution value. When analyzing the usual four-channel images, six combinations need to be considered. Analysis MetroloJ_QC automatically generated analyses for all possible channel combinations, measured the pixel shifts, the (calibrated/uncalibrated) intercenter distances, and compared them to their respective reference distance r ref. A ratio of the measured intercenter distance to the reference distance was also calculated. Images with more than one bead can be analyzed, and a batch mode enabled the analysis of multiple datasets. Fig. S8  and the plugin manual provide a more detailed description of our coregistration workflow. Biological imaging Monocyte dendritic cells (MoDC) were used with actin (phalloidin-Alexa Fluor 405, A30104; Invitrogen) and beta3 integrin staining (primary/secondary antibody conjugated to Cy3).
Results We collected data from setups of the three microscopy techniques using 92 different objectives. We observed that >73% of the calculated coregistration ratios show nearly perfect coregistration (i. e. , ratio <1) in blue-green (B-G) and green-red (G-R) channel combinations (Fig. 5 A ). Our tolerance values range below unity (green zone of Fig. 5, A  and B ). When looking at individual combinations, 96% of the measures were below 1 for the G-R, 75% for G-FR (green-far red), 60% for B-R, and only 61% for the B-G. A comparison of the three different microscopy techniques shows that the above statement is true with all setups (insets of Fig. 5, A  and B ). However, SDCM is more often associated with insufficient chromatic correction with 60% of the measured ratios below 1 compared to LSCM or WF with 70 and 83%, respectively.
Data points are 24, 15, and 9 for diode, DPSS, and gas lasers respectively. (F) Illumination laser power monitoring over a microscope (Leica SP5) lifetime (long-term stability). Intensities of a 405 nm diode, 488 nm argon, and 633 nm He-Ne gas lasers and a 561 nm DPSS laser were recorded for 9 yr. The 488 nm laser was replaced three times (black arrows). The orange arrow indicates when the optical coupling fiber of the 488 nm laser to the microscope was replaced. The manufacturer service visits are indicated with blue arrows and are often associated with power increases as the system was realigned.
For biological imaging, minimizing the stage drift is critical, especially for time-lapse experiments. As shown with the biological example of Fig. 7 , E and F, drift can be problematic for live cell imaging, even for short time scales (10 min in our case). Effective software solutions exist to overcome the drifts after the acquisitions (e. g. , StackReg  [Thevenaz et al. , 1998] ), but it is always preferable to understand and master the drifts at the hardware level (Table  1  for troubleshooting). Positioning repeatability Stage repeatability is directly linked to stage drift. When parameters involved in drift are controlled and the drift is characterized, the ability of the stage to reposition at the same xy location can be measured. Monitoring repeatability must then be performed after the stage stabilization time and the sample should be well secured to avoid a sample-associated source of drift.
, 2020). This must be taken into consideration when using image analysis algorithms (e. g. , singlemolecule localization) on images acquired with sCMOS cameras  (Babcock et al. , 2019; Huang et al. , 2013). Also, some quantitative image data analysis, like maximum likelihood estimation methods, requires individual pixel characteristics, such as variation of the read noise  (Huang et al. , 2013). In our protocol, the read noise is measured by the fluctuation of the value of a pixel along a temporal acquisition of 100 images, without any light influence. Read noise is then considered as a temporal noise. While the quantity of 100 frames may seem overestimated for CCD read noise measurement, it is a fair compromise for sCMOS read noise (obviously not to characterize every pixel's attributes;  Huang et al.
, 1998; Snoeij et al. , 2006). EMCCDs often have additional noise distributed over the image sensor chip area  (Rieger and Stallinga, 2014). For precise DSNU evaluation, thousands of frames should be acquired to eliminate the read noise. To define the optimal dataset size, we introduced a quality criterion that compares the DSNU obtained with datasets of different sizes. A threshold of 10% yielded an optimal dataset size of 100 frames (i. e. , the DSNU value difference obtained with larger datasets is <10% of the DSNU value calculated with 100 frames, Fig. S14 D ). Hence, we recommend using a 100 dark frames dataset for DSNU evaluation. To resume, read noise is an inherent property of camera sensors, regardless of whether or not light reaches it. Thus, the measurements were carried out without excitation light and sample.
Conclusion We described the essential aspects of the QC tools, the acquisition process, the analysis, and gave a vision of how helpful an image database that collects data from different laboratories could be (Fig. 10 ). We tried to use simple tools, as affordable as possible, such as custom-made bead slides, plastic fluorescent slides, and power meters to assess QC of light microscopes over time. Other QC tools exist, like the Argolight slide (Argolight SA, France), GATTAquant beads (Gattaquant GmbH, Germany), nanorulers, or PSFCheck slides. The drawbacks of these tools (i. e. , price or guaranteed performance for a limited duration of time) influenced our choice for using simple and costeffective tools. We defined metrics through simple and robust acquisition protocols that do not take >10 min to acquire or launch.