dataesr/scientific-paragraphs-categorization huggingface dataset:
Three-dimensional correlative light and electron microscopy (3D CLEM) are attaining popularity as a potential technique to explore the functional aspects of a cell together with high-resolution ultrastructural details across the cell volume. In order to perform such a 3D CLEM experiment, there is an imperative requirement for multi-modal probes that are both fluorescent and electrondense. These multi-modal probes will serve as landmarks in matching up the large full cell volume datasets acquired by different imaging modalities. Fluorescent nanodiamonds (FNDs) are a unique nanosized, fluorescent, and electron-dense material from the nanocarbon family. We.
CC-BY 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted October 29, 2019. ; https://doi. org/10. 1101/823278 doi: bioRxiv preprint CLEM methods correlating 3D information from both LM and EM. Combining 3D fluorescence microscopy with 3D EM would significantly improve the technical possibilities for investigating complex cellular processes across the full volume of a cell. Recently, several volume-CLEM methods that demonstrate 3D correlation have been presented  (31) (32) (33) (34) (35) (36). Typically, there should be some common landmark to allow correlation, as 3D imaging from both LM and EM generates substantial image datasets.
Preferably, these landmarks should be detectable with both modalities. Consequently, intracellular fluorescent and electron-dense landmarks are critical for the execution of 3D CLEM experiments. One such fluorescent and electron-dense CLEM marker is the fluorescent nanodiamond (FND)  (19, (37) (38) (39). (40) (41) (42) (43) (44). FNDs are non-toxic to cells, and being nanosized particles, they can be easily internalized in living cells via endocytosis  (45) (46) (47). FNDs have excellent photostability, and they have non-blinking far-red emission, which makes them well-suited for imaging of living and fixed cells. We recently reported that FNDs are robust intracellular landmarks in 2D CLEM experiments  (39). In this article, a 3D CLEM method is demonstrated using 35 nm FNDs as intracellular landmarks for correlating cell volume datasets from live-cell confocal microscopy and serial block-face scanning electron microscopy (SB-EM).
3D SB-EM Imaging The area of interest with the selected cells was trimmed from the plastic block and mounted onto a pin using conductive epoxy glue (model 2400; CircuitWorks, Kennesaw, GA). The trimmed block was further trimmed as a pyramid and its sides were covered with silver paint (Agar Scientific Ltd, Stansted, UK). To improve conductivity the whole assembly was platinum-coated using Quorum Q150TS (Quorum Technologies, Laughton, UK). SB-EM data sets were acquired with a FEG-SEM Quanta 250 (Thermo Fisher Scientific, FEI, Hillsboro, OR), using a backscattered electron detector (Gatan Inc. , Pleasanton, CA) with 2. 5-kV beam voltage, a spot size of 2. 9, and a pressure of 0. 15 Torr. The block faces were cut with 50-nm increments and imaged with XY resolution of 25 nm per pixel. The collected 16-bit images were processed for segmentation using an open-source software Microscopy Image Browser(50) as follows: a) individual images were combined into 3D stacks; b) the combined 3D-stack was aligned; c) the contrast for the whole stack was adjusted and d) the images were converted to the 8-bit format.
Image correlation The multi-modal datasets were registered using the eC-CLEM plugin on the Icy bioimage analysis platform. In order to match the large datasets on a laptop (i7, 16Gb RAM memory), the EM stack. CC-BY 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted October 29, 2019. ; https://doi. org/10. 1101/823278 doi: bioRxiv preprint was binned 4 times. The FM stack was matched to the binned dataset using the FNDs as landmarks, targeting the center of the FNDs aggregates both in LM and EM using orthogonal views from Icy. 9 FNDs were sufficient to achieve the good overlay accuracy depicted in this manuscript.
Rigid registration was performed despite a recommendation by the software to apply for non-rigid registration. This decision was made after careful observation of the LM dataset. Since living cell imaging was performed on the 3D stack, the cell dynamics causes some of the FNDs to move during the image acquisition. This natural movement is uneven in all FNDs. Local inaccuracies in this registration are coherent with the cell movement observed. The weighing of each individual landmark operated by eC-CLEM compensate for the shifts observed between the LM and the EM dataset and rigid registration leads to an accurate full registration. To generate the final overlay, the transformation was applied to the LM dataset to match the original EM dataset using the "apply a reduced scaled transform to a full-size image" function from eC-CLEM (Advanced usage).
Our 3D localization of FNDs with respect to the MitoTracker fluorescence signal can be seen in Video. 2. These 3D confocal datasets were used for software-based correlation with SB-EM datasets. CC-BY 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted October 29, 2019. ; https://doi. org/10. 1101/823278 doi: bioRxiv preprint After confocal imaging, the selected cells were fixed, stained and embedded for SB-EM (Figure. 1g). The use of gridded glass-bottom dishes allowed easy identification of the cells of interest within the plastic block and trimming the blocks accordingly. The trimmed area was mounted on a pin and imaged in SEM.
Figure. 1h. The two selected cells were identified (Figure. 1h) using a 15kV electron beam. The collection of 3D EM data was performed with an SEM instrument equipped with a system for serial block-face SEM (SB-EM). In SB-EM, an ultramicrotome performed automated sectioning of whole-cell volume by cutting thin sections (≥50 nm) from the sample's block-face (Video. 3). Consequently, after each cut, a high-resolution image of the freshly made block-face was acquired using a backscattered electron detector to form a 3D image stack. SB-EM imaging provided a threedimensional dataset of the selected cells with a resolution to recognize the structure of interest (mitochondria) and FNDs aggregated in vesicular structures for CLEM (Figure. 2). Figure  2. A collage of multi-plane images from the SB-EM 3D series.
a-h) Images from the 3D EM stack acquired from the selected cells shown in the increasing order of z-axis. The distance between the slices is 50 nm. Correlation of the LM and SB-EM volume datasets was done using the eC-CLEM plugin on the Icy bioimage analysis platform  (52, 53). First corresponding intracellular FNDs were identified in both datasets. FNDs aggregated in vesicles have a distinct appearance in SEM images (FigS1) and they are easily distinguished from morphological features of the cell. Quite commonly in literature the multi-modal correlation is performed in the absence of such a common landmark, and the process of 3D dataset correlation severely suffers from misalignment and errors in localizing critical information across 3D. However, in this type of CLEM approach, there can be multiple factors that could affect the precise image correlation.
The major challenge encountered in the CLEM experiment was inherently low axial (600 nm) and lateral (250 nm) resolution provided by confocal microscopes compared to the nanometer scale resolution provided by EM. Currently, the limited resolution of confocal microscopy can originate in misalignment of details within large scale datasets. Sample autofluorescence, unspecific binding of fluorophores and obtaining a bright FND signal with live cell imaging are additional parameters still have to be optimized.
Discussion We have introduced a novel FND enabled cell volume (3D) correlative microscopy method. The CLEM workflow is straightforward and can be performed without any dedicated CLEM imaging systems. We demonstrated that a standard organic fluorophore can be used for 3D CLEM experiments with the FND based method without any special sample preparation requirements. In general, organic fluorophores do not survive routine EM sample processing and are not electron dense molecules, and therefore are not detectable with EM. In contrast, the employed 35 nm FNDs were intracellularly detectable with both imaging modalities in our experiments, enabling successful correlation of volume datasets for 3D CLEM. FNDs can offer multiple advantages over currently used CLEM fiducials as their internalization does not need chemical permeabilization, which has impacts on cellular morphology and ultrastructure.
Acknowledgments The authors would like to acknowledge  Jenni Laine  and  Kai-Lan Lin  ( Electron microscopy unit, University of Turku ) for providing technical assistance with sample preparation for SEM and  Mervi Lindman ,  University of Helsinki  for excellent technical assistance in the preparation of SB-EM specimens. Helen Cooper  is acknowledged for kindly providing mitochondrial staining reagents. Financial contribution from  Academy of Finland  (Project Nos. 309374 ) is also greatly acknowledged. The authors acknowledge  Biocenter Finland  for financial support and  Euro-BioImaging  (www. eurobioimaging. eu)for providing access to imaging technologies and services via the  Finnish Advanced Light Microscopy Node (Helsinki, Finland). We also thank  Fen-Jen Hsieh  for the preparation of 35 nm FNDs.
The ATLAS experiment is approaching mid-life: the long shutdown period (LS2) between LHC Runs 1 and 2 (ending in 2018) and the future collision data-taking of Runs 3 and 4 (starting in 2021). In advance of LS2, we have been assessing the future viability of existing computing infrastructure systems. This will permit changes to be implemented in time for Run 3. In systems with broad impact such as the conditions database, making assessments now is critical as the full chain of operations from online data-taking to offline processing can be considered: evaluating capacity at peak times, looking for bottlenecks, identifying areas of high maintenance, and considering where new technology may serve to do more with less. We have been considering changes to the ATLAS Conditions Database related storage and distribution infrastructure based on similar systems of other experiments.
Introduction The ATLAS experiment  [1]  is operating at the Large Hadron Collider (LHC) at CERN. AT-LAS has been successfully collecting collision data for the past 8 years, and in the next few years there will be significant upgrades of the LHC machine and of ATLAS subdetector systems, bringing an increase in luminosity and in the rate of collected data. The processing of the experimental data collected by ATLAS requires a wide variety of auxiliary information from many systems (e. g. Detector Control Systems (DCS), Trigger and DAQ, Data Quality, the LHC accelerator and ATLAS sub-detectors) stored in the ATLAS Conditions Database. Such pieces of information are heterogeneous both in data type (e. g. standard integer, floating point and string types, database-standard BLOBs, CLOBs, and types special to the Conditions database infrastructure like references to external POOL and ROOT files) and in time granularity (spanning intervals from minutes to hours in duration).
During the LHC Run 1 and Run 2, the ATLAS Collaboration deployed and used a system based on the LHC Computing Grid (LCG) Conditions Database infrastructure and the COOL API [2], a C++ library based on a software layer called CORAL which manages the actual database access and the queries that should be issued (hiding the SQL complexity from clients) and supports multiple relational platforms (SQLite and Oracle). We found that this architecture worked well so far, but is showing signs that it will not scale to cope with the processing of the increasingly complex and intense data flows coming with the growing LHC luminosity. For this reason, the ATLAS Conditions Database management is evolving to a new Conditions Database system [3] which is based on RESTful 1  client-server interaction and has an architecture which uses an intermediate server that disentangles the business components dealing with the database management aspect of the client.
This allows the client and server to evolve separately via well-identified interfaces. In the new framework, the database access layer is implemented at server level and the exchanged network traffic will be conditionsdatabase specific (instead of the present generic SQL), allowing to profit in a better way of the parameters used to retrieve the data, which are today completely invisible inside the SQL statement; this element can be relevant for caching optimization. During the intermediate transition phase, we are developing a set of new tools to permit RESTful access to COOL/CORAL for preserving the functionality of the present system.
The ATLAS Conditions Database during LHC Run 1 and Run 2 ATLAS conditions data are stored in a relational database. The database design is based on the LCG Conditions Database and is accessed by clients using the COOL API, both of which were developed by the CERN IT department for the LCG  [2]. COOL is a C++ API library based on the CORAL access layer. It provides high level functionality which allows users (the expert scientists that manage the conditions data for any ATLAS sub-detector) to create their own COOL Database and fill it with payload data corresponding to a given time range over which that data is valid (the IOV, or Interval of Validity). Using the COOL terminology, a COOL Database for a given system is called a schema, and the database tables dedicated to a given set of parameters inside each schema are called folders.
Database access is managed via the COOL API using the intermediate Frontier/Squid [5] services, as shown in Figure  1. Database queries which are created via the COOL/CORAL stack are propagated as parameters inside a URL and sent as HTTP requests to the Frontier server. Access to the Oracle Database is managed via a Java DB Connectivity (JDBC) layer. Since each request with an The database design and the data access model have been generally stable through LHC Run 1 and for most of Run 2, but in the later stages of Run 2 dips in data access efficiency were observed. This led to a general survey of the system to assess how to improve current operations as well as the viability of the current infrastructure for future operations. We found they current system is problematic in a number of areas:.
COOL access in Distributed Computing • Global management of the system is made more difficult because the conditions data are contained in over 30 schemas and stored in over 10,000 underlying folder tables in the database. The implementation of global tags in the LCG infrastructure proved cumbersome in ATLAS because of the way in which it is implemented at the database level. • The granularity of the conditions data in the current system is not well suited to the caching mechanisms of Frontier. • The support from CERN IT of the underlying software stack (COOL and CORAL) is decreasing and will stop at the beginning of Run 3. Maintenance of the complexity of the current implementation will be magnified as new data is added to existing schemas and as new detector systems come online. We also are concerned about the possible implications in terms of data preservation.
The new Conditions REST infrastructure A new REST-based architecture for the management of ATLAS conditions data is now being developed. The new Conditions REST (CREST) architecture enhances the role of the Frontier servers, as shown in Figure  2. CREST development started in collaboration with the CMS experiment [6] who have been successfully using this underlying database model for storing and accessing conditions data since the beginning of LHC Run 2. In this architecture, the client is not aware of the underlying persistence technology used, and interacts with the storage via functions implemented at server level, providing only the set of conditions data or metadata that are needed directly inside the URL (or requests body or headers) of the HTTP method (GET, POST,. ) used. Having the client compose requests via an abstraction layer above the SQL also allows alternative storage systems to be swapped on the server side in the future without changes needed on the client side.
Data analysis For each patient, we determined age, gender, left atrial size, type of AF (paroxysmal 5 AF episodes ,1 week; persistent 5 AF episodes 1 week-1 year; or longstanding 5 AF episodes. 1 year), number of antiarrhythmic drugs failed, CHA 2 DS 2 -VASc score, and presence or absence of hypertension, diabetes, and previous stroke or transient ischemic attack. For each ablation, we determined procedural, fluoroscopy, and total RF times. Complications were reported on a per procedure basis. Complications examined were death, incidence of pericardial tamponade, strokes occurring within 48 hours and strokes occurring from 48 hours-30 days, PV stenosis requiring intervention, phrenic nerve paralysis, atrioesophageal fistulas, steam pops, and catheter char. For atrioesophageal fistulas, we evaluated the patients by subgroup according to whether the full 45-50 W of power was used on the posterior wall for short durations or the power on the posterior wall was reduced to 35 W for 20 seconds.
This approach targets the G12C mutation that represents around 12% of KRAS mutations in cancers (Cosmic database v86, https://cosmic-blog. sanger. ac. uk/), and is only present in a subset of cancers, such as non-small cell lung cancers  22. Therefore, alternative strategies are needed to inhibit the most frequent mutations of KRAS accounting for 88% of KRAS mutant cancers. We report here the characterisation of two potent DARPins that selectively bind KRAS on a site of the allosteric lobe, encompassing histidine residue 95. The DARPin binding inhibits KRAS nucleotide exchange and KRAS dimerisation, thus impairing mutant KRAS-effector interactions and the downstream signalling pathways. These findings reveal a unique strategy to selectively inhibit KRAS.
Both K13 and K19 complexes gave bipyramidal crystals in 0. 5-1. 0 M lithium sulphate, 0. 5-1. 0 M ammonium sulphate, 100 mM tri-sodium citrate pH 5. 5. A cryoprotectant solution of 2 M lithium sulphate could be used for crystals of both complexes. The diffraction data were collected at 100 K temperature at the Diamond Light Source, UK on beamlines I04-1 and I03 at 0. 920 and 0. 976 Å wavelength, respectively. XDS, pointless and scala were used to process the data. Model building and subsequent refinement were carried out with Coot and Refmac. There are no Ramachandran outliers in either structures and over 98% are in the most favoured region. Coordinates and structure factors were deposited in the PDB with accession codes 6H46 and 6H47. Data collection and refinement statistics are summarised in Supplementary Table  1.
Peptic peptides were trapped and desalted on an Acquity BEH C18 VanGuard pre-column (130 Å, 2. 1 × 5 mm, 1. 7 μm; Waters) kept at 0. 1 °C. The trapped peptides were eluted using a 9min gradient of 5-35% acetonitrile in 0. 1% (v/v) formic acid at 40 μL. min -1 on an Acquity UPLC BEH C18 column (130 Å, 1. 7 μm, 100 mm × 1 mm; Waters) at 0. 1 °C. Peptides were detected on a SYNAPT G2-Si HDMS mass spectrometer (Waters) acquiring over an m/z range of 50-2000 with an electrospray source and lock mass calibration (Leucine Enkephalin, 200 pg. μL -  ; Waters). The mass spectrometer was operated at a source temperature of 80 °C and a spray voltage of 2. 5 kV. Spectra were collected in positive ion resolution mode. Peptide identification was performed in Protein Lynx Global Server (Waters) using MS E data collected for the triplicate undeuterated control samples.
The ratio of these values was fitted using non-linear regression in the application Prism (GraphPad Software). An assay to measure only the inhibition of KRAS/CRAF RBD was like the coupled assay above, with the exception that test samples were added after the completion of the nucleotide exchange step. MANT nucleotide exchange and release assays. Exchange of GDP with fluorescent MANT-dGDP or fluorescent MANT-GTP on KRAS was studied in an assay previously described  8. DARPins were tested for inhibition of nucleotide exchange by incubating at 10 μM with 500 nM KRAS G12V , 1 mM SOS and 500 nM MANT-dGDP or MANT-GTP (Biolog). Exchange was measured over time by the increase in fluorescence of MANT-dGDP or MANT-GTP upon binding to KRAS and detected using an Envision plate reader (PerkinElmer) using 340 nm/450 nm excitation/ emission filters.
The data were captured at times from zero to 60 min. For nucleotide release assays, DARPins were added after equilibration of MANT nucleotides. DARPin K d measurements. ForteBio Octet Red384 was used to determine the binding kinetics of DARPins to KRAS. Concentrations of DARPin K19 from 15,000 nM to 6. 2 nM or DARPin K13 from 3000 nM to 12. 4 nM were added to sensors pre-loaded with biotinylated KRAS (1-166; wild-type; GDP or GTPγS), associated for 300 s and dissociated for 600 s by washing in buffer. For each sample, a reference well was included only containing buffer. The data were analysed using ForteBio data analysis. Reference wells were subtracted from sample wells, and 1:1 local fitting model was used to fit curves to sensorgrams and determine k on , k off and K d values. The experiments were carried out in kinetic buffer from ForteBio.
Secondary antibodies include anti-mouse IgG HRP-linked (CST), anti-rabbit IgG HRP-linked (CST) and anti-goat IgG HRP-linked (Santa Cruz Biotechnologies). Confocal microscopy. HEK293T cells were seeded on coverslips and transfected 24 h with pEF-GFP 2 -KRAS G12V 166 construct. Coverslips were washed in PBS, fixed 10 min in 4% paraformaldehyde and washed twice in PBS. Then the coverslips were mounted with DAPI Fluoromount-G overnight. Slides were analysed using a Zeiss 880 Inverted Confocal Microscope with a ×63 objective. Confocal images were analysed with ImageJ software. Quantification and statistical analysis. The quantifications were performed using ImageJ or Prism 7. 0c (GraphPad Software), BRET titration curves and statistical analysis were performed using Prism 7. 0c (GraphPad Software).
Acknowledgements DARPin antibody mimetics were isolated at  MedImmune  under a Research License Agreement with Molecular Partners AG. DARPin is a registered trademark of Molecular Partners AG. We thank  Anna Czyz  for performing the DARPin expression. We thank  Diamond Light Source  for beamline access and technical support. We thank  Dr Geoff Higgins  and  Dr Remko Prevo  for giving the T24 cells. The work of NBery & THR is supported by grant numbers  Wellcome Trust   099246/Z/12/Z. MRC MR/J000612/1.