dataesr/scientific-paragraphs-categorization huggingface dataset:
At high emitter densities, eSRRF also outperforms the high-density emitter localization algorithm of ThunderSTORM 9 even in combination with HAWK analysis 11 (Supplementary Fig. 3 ). At the other extreme of sparse blinking densities typical of SMLM acquisitions, single-emitter fitting still provides unsurpassed localization precision and image resolution; however, it requires the processing of a large number of images. In this particular case of sparse blink dataset (typical SMLM data), eSRRF can provide a fast preview of the reconstructed image (Supplementary Fig. 4 ).
The parameter sweep allows to explore trade-offs between FRC resolution and RSP fidelity (Supplementary Video 1) as a consequence of reconstruction parameter choice. To balance the two metrics, we use an F1 calculation to compute our quality and resolution (QnR) score: https://doi. org/10. 1038/s41592-023-02057-w QnR = 2 × RSP × nFRC RSP + nFRC Here nFRC is the normalized FRC resolution metric, ranging between 0 and 1, with 0 representing a poor resolution and 1 representing a high resolution. The QnR score ranges between 0 and 1, where scores close to 1 represent a good combination of FRC resolution and RSP fidelity, whereas a QnR score close to 0 represents a low-quality image reconstruction. Figure  2  shows a representative dataset acquired with COS-7 cells expressing Lyn kinase-SkylanS, previously published by Moeyaerd et al.
3a ), even though they are tightly packed along the dendrites (Extended Data Fig. 5a ). eSRRF can also be applied to volumetric datasets as obtained for example with LLS microscopy (Supplementary Video 2). Here, the eSRRF reconstruction of volumetric image stacks is obtained by processing each slice sequentially. Note that this approach can only effectively improve the lateral resolution (xy plane), while there is a sharpening comparable to deconvolution in the z direction, no resolution improvement over the diffraction-limited images should be expected. Figure  3b, c  shows the plane by plane eSRRF processing of a LLS dataset of the ER in Jurkat cells allowing us to distinguish sub-diffraction-limited features along the x direction (Fig. 3b(i )), https://doi. org/10. 1038/s41592-023-02057-w but not in the z direction (Fig.
To realize 3D eSRRF, we extended the algorithm to calculate the RGC in 3D (Supplementary Note 3). Consequently, we can reconstruct a volumetric image with enhanced resolution in the axial direction and in the lateral image plane. The approach was first validated with simulated 3D data (Extended Data Fig. 9 ). In these data, a woodpile structure can be seen, featuring filaments with axial distances ranging from 350 to 600 nm. These filaments are populated with single-molecule emitters, whose blinking events are set at increasing densities in each dataset. Their analysis allowed us to identify that, while at the highest densities only filaments crossing at an axial distance of 600 nm could be resolved, 3D eSRRF was able to distinguish filaments separated by under 400 nm in other density regimes.
4 (i),(ii) and Extended Data Fig. 10 ). Extending the 3D eSRRF processing to the full live-cell time lapse reveals rearrangement of the mitochondrial network at a super-resolution level (Fig. 4 (iii)). To constrain cell damage, we maintained the observation time to ~20 s to minimize cellular damage. We were able to extend the observation time even more by using an extra dataset in which we reduced the excitation intensity by a factor of two. This second experiment achieves slightly lower resolution due to the reduced signal in these experimental conditions, while letting us observe mitochondrial network dynamics over the course of more than 3 min without observable signs of cell damage (Supplementary Video 5).
Discussion The new eSRRF approach builds on the previous capacity of SRRF, considerably improving image reconstruction quality and fidelity, as shown here for gold standards such as the Argo-SIM calibration structure  51  , simulated data and the nuclear pore complex  60. It showcases a new analytical engine for calculating the RGC transform, replacing the lower quality radiality transform of the original SRRF method. These modifications have also allowed us to extend the approach into full 3D super-resolution, by combining it with MFM, realizing fast 2D and 3D super-resolution imaging in live cells. While the performance of eSRRF may be surpassed in spatial or temporal resolution by deep-learning-based SRM approaches, these face a very specific set of limitations. Such deep-learning methods have been shown to convert sparse SMLM data  61  or even diffraction-limited images of dynamic structures  62  into super-resolved time series at rates above 50 Hz.
Their extension to 3D isotropic volumetric live-cell SRM has demonstrated imaging rates of up to 17 Hz 59 ; however, unlike eSRRF, these methods require previous knowledge of the dataset and generally require either simulated or experimental ground-truth data. These features also mean that insufficient, unbalanced or unsuitable data can lead to severe image degradation and hallucinations by deep-learning methods that are not easy to detect  63. Here, we have introduced a data-driven parameter optimization approach that aids users in selecting optimal parameters learned directly from the data to be analyzed. These optimal parameters are chosen by balancing the need for high reconstruction fidelity together with high spatial and temporal resolution. In contrast to the training procedures employed in deep-learning approaches, the data-driven eSRRF parameter optimization bases its scoring on the direct unsupervised analysis of the data being collected via FRC and RSP calculations.
As such, an eSRRF analysis is easy and reliable, especially when the data being collected have new properties or features that have not been seen before. Furthermore, the implemented parameter optimization based on the QnR metric directly provides dataset-specific insight into the relationship between image fidelity and resolution, enabling the user to critically analyze results and take an informed decision on analysis settings. By combining the QnR-based parameter sweep with a temporal window optimization, eSRRF achieves optimal spatial and temporal resolution while minimizing reconstruction artifacts and reducing user bias. This makes eSRRF a super-resolution method that shows users how to best analyze their data and gives them the information they need to find the best conditions for live-cell super-resolution imaging that is sensitive to phototoxicity.
In addition, it may be possible to explore gentler and faster MFM approaches  66. The computational complexity of eSRRF makes processing times longer compared to SRRF. Additionally, finding the optimal balance between resolution and fidelity relies on user evaluation of the parameter space, so there is still some subjectivity in selecting final parameters. At low emitter densities, single-molecule localization methods can still provide better resolution than eSRRF. There are also challenges with observing very fast dynamics below the temporal resolution of the image stack used for reconstruction. A further machine-learning accelerated version of eSRRF is now being developed and available in the NanoPyx Python-based framework, which also includes a napari plugin  67. Future advances could automate the parameter optimization process more fully using machine learning.
Overall, while current implementations still have limitations, the development of eSRRF demonstrates the potential for data-driven optimization to improve image reconstruction quality and accessibility in SRM. eSRRF is currently implemented as an open-source GPU-accelerated Fiji plugin, accompanied by a detailed user guide (https://github. com/HenriquesLab/NanoJ-eSRRF) as well as in napari and Python, making it widely available to the bioimaging community.
4). A realistic Poisson photon noise and a Gaussian read-out noise were added to the images to simulate an experimental dataset. The diffusing particle datasets were generated as single emitters represented by a Gaussian PSF and with Gaussian noise moving at a constant speed, with a Python script available as a GoogleCoLabs Jupyter notebook on GitHub at https://github. com/HenriquesLab/ NanoJ-eSRRF. The MFM simulations of stacked lines were performed using a custom-written framework NanoJ-TheSims in ImageJ. Ground-truth images were provided as 3D stacks on an upsampled grid (5-nm pixel size in (xy) and 10-nm slice separation). For 3D multiplane simulations, a PSF stack was generated with the ImageJ plugin PSFGenerator 69 using the Born and Wolf model, 1. 4 NA, and emission wavelength of 650 nm, with pixel sizes and plane separations matching the ground truth.
DNA-PAINT imaging was performed on an N-STORM microscope (Nikon) equipped with 647-nm lasers (125 mW at the optical fiber output). After injection of an 0. 25-nM imager strand (I1-ATTO655, Ultivue) in 500 mM NaCl in 0. 1 M PBS, pH 7. 2, buffer, 50,000 frames were acquired at 60% power of the 647-nm laser with an exposure time of 30 ms per frame to obtain low-density ground-truth data. Image reconstruction was performed with the ImageJ/Fiji plugins NanoJ-SRRF, NanoJ-eSRRF, ThunderSTORM and HAWK. To create datasets with increased emitter density, temporal binning was performed by summing substacks of the SMLM image sequence. The raw data had an average emitter density of 0. 121 localizations per frame and µm 2 , which remained constant throughout the whole DNA-PAINT acquisition.
SIM imaging of the Argo-SIM calibration sample Super-resolution 3D-SIM imaging was performed on a Zeiss ELYRA PS. 1 microscope (Carl Zeiss) using an αPlan-Apochromat ×100/1. 46 oil immersion objective and an EMCCD Andor 887 1K camera. For SIM reconstruction, 15 images (five phases and three rotations) of the gradually spaced line pattern on the Argo-SIM calibration slide (Argolight) were acquired. SIM image reconstruction was performed with the software ZEN Black v. 11. 0. 2. 190 (Carl Zeiss).
4% d-glucose, and maintained in a humid chamber at 36 °C for the duration of the experiments (Okolab). TIRF microscopy. Live imaging of neurons expressing pSkylan-NS-α-tubulin were performed on an inverted microscope ECLIPSE Ti2-E (Nikon Instruments) equipped with an ORCA-Fusion sCMOS camera (Hamamatsu Photonics K. K. , C14440-20UP) and a CFI SR HP Apochromat TIRF ×100 oil (NA 1. 49) objective. Samples were sequentially illuminated with laser light at 405 nm and 488 nm for 100 ms at 5% laser power, respectively with an active Nikon Perfect Focus System and with the NIS-Elements AR 5. 30. 05 software (Nikon). 2D-SIM microscopy. Live-imaging experiments of neurons expressing pEGFP-α-tubulin were performed on a Nikon N-SIM S structured illumination microscope with a Nikon Ti inverted microscope using a CFI Apochromat TIRF ×100 C Oil (NA 1.
The focus offset dz was 390 nm between consecutive focal planes. The 3D image registration was performed based on multicolor fluorescent beads (TetraSpeck Fluorescent Microspheres kit; T14792, Invitrogen), immobilized on a coverslip. Images of the beads were recorded while axially displacing the sample with a z-step size of 60 nm. To overlay and align the nine focal planes, a calibration table was created based on the bead images with the NanoJ-eSRRF plugin tool 'Get spatial registration from MFM data' (NanoJ-eSRRF>Tools>Get spatial registration from MFM data). This spatial registration was applied to the live-cell MFM data during the 3D eSRRF processing (a detailed manual can be found at https://github. com/HenriquesLab/ NanoJ-eSRRF/wiki). To extract the shape of the MFM PSF in the different focal planes (Supplementary Fig.
Acknowledgements We thank  J. Lippincott-Schwartz  and  C. Obara  at  Janelia Farm  for their assistance with the dynamic ER-TIRF data and for reading the paper. This work was supported by the  Gulbenkian Foundation  (H. S. H. , S. Coelho, D. M. and R. H. ) and received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 101001332  to R. H. and D. M. ), the  European Commission  through the  Horizon Europe program  ( AI4LIFE  project with grant agreement  101057970-AI4LIFE  https: //doi. org/10. 1038/s41592-023-02057- w and  RT-SuperES  project with grant agreement  101099654-RT-SuperES  to R. H. ), the  European Molecular Biology Organization  ( EMBO-2020-IG-4734  to R. H. and  ALTF 499-2021  and Scientific Exchange Grant  9958  to H.
UTechS  PBI is part of the  France-BioImaging infrastructure  network and A. Salles and A. D. are supported by the  French National Research Agency  ( ANR-10-INBS-04 ; Investments for the Future) and acknowledge support from  ANR/FBI  and the  Région Ile-de-France  (program ' Domaine d'Intérêt Majeur-Malinf' ) for the use of the  Zeiss LSM 780 Elyra  PS1 microscope. S. Culley  acknowledges support from a  Royal Society University Research Fellowship  ( URF\R1\211329 ). This study was supported by the  Academy of Finland  (G. J. ,  338537  and G. F. ,  332402 ), the  Sigrid Juselius Foundation  (G. J. ), the  Cancer Society of Finland  (G. J. ), the  Åbo Akademi University Research Foundation  (G. J. ,  CoE CellMech ), the  Drug Discovery and Diagnostics  strategic funding to  Åbo Akademi University  (G.
J. ) and by the  InFLAMES Flagship Program   of the Academy of Finland  (decision no. 337531 ). The  Cell Imaging and Cytometry Core facility (Turku Bioscience, University of Turku ,  Åbo Akademi University and Biocenter Finland)  and  Turku Bioimaging  are acknowledged for services, instrumentation and expertise. C. L. acknowledges funding from the  Agence National de la Recherche  ( ANR-20-CE13-0024 ). C. L. acknowledges the  INP NCIS imaging facility  and  Nikon Center of Excellence for Neuro-NanoImaging  for service and expertise, with funding from  Excellence Initiative of Aix-Marseille University , A* MIDEX , a  French 'Investissements d'Avenir' program  ( AMX-19-IET-002 ) through the  Marseille Imaging and NeuroMarseille Institute. B. H. acknowledges funding from the  Fondation pour la Recherche Médicale  ( DEI20151234398 ), the  Agence National de la Recherche  ( ANR-19-CE42-0003-01 ), the  LabEx Cell (n) Scale  ( ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02 ), the  Institut Curie ,  Agence pour la Recherche sur le Cancer (ARC Foundation) ,  DIM ELICIT  and from  ITMO Cancer of Aviesan  on funds administered by  Inserm  (grant no.
ACKNOWLEDGEMENTS This work was carried out during a visit at the  Pompeu Fabra University (UPF ) in Barcelona and was funded by the  Artificial and Natural Intelligence Toulouse Institute (ANITI) -Institut 3iA  ( ANR-19-PI3A-0004 ). We thank  Hector Geffner ,  Anders Jonsson  and the  Artificial Intelligence  and  Machine Learning  group of the  UPF  for their warm hospitality.
These panels would need a significant additional increase in sequencing costs to maintain a suitable sensitivity to analyze the clonal evolution of relapsing patients. Therefore, it remains challenging to decipher the clonal architecture and dynamics in patients carrying a fusion gene. The present study aimed to develop and validate an asymmetric capture sequencing strategy (aCAP-Seq) and a bioinformatics tool (HmnFusion) (https:// github. com/guillaume-gricourt/HmnFusion, last accessed May 6, 2022), enabling the detection and quantification of MBCR::ABL1 (leading to e13a2 or e14a2 transcripts), mBCR::ABL1 (leading to e19a2 transcript), PML::RARA, and FIP1L1::PDGFRA fusion genes in a unique targeted NGS panel designed to identify the mutational landscape of myeloid malignancies at diagnosis, relapse, progression, or treatment failure.
Statistical Analysis The Wilcoxon signed rank test was conducted with SciPy (1. 7. 3; https://github. com/scipy/scipy/releases/tag/v1. 7. 3, last accessed May 16, 2022). Other statistics, linear regression, paired t-test, or Bland and Altman analysis were performed with GraphPad Prism software version 6. 0 (GraphPad Software, San Diego, CA). When specified, average values are presented as means AE SEM. P < 0. 05 was assigned statistical significance.
BCR major breakpoint region (MBCR) spanning 3. 1 kb between exon 13 and exon 15 of BCR was covered on average at 4996Â AE 212 (mean depth AE SEM). Within this genomic interval, the presence of an ALU sequence spanning 298 bp is responsible for a drop in sequencing depth (1037Â AE 56). This region was predicted to be uncovered by an online software design tool (SureDesign, Agilent Technologies). However, the use of long reads (2 Â 300 bp) and the increase in probe tilling (Figure  2, A  and E ) likely contributed to maintaining region coverage. The 1. 3 kb mBCR breakpoint region in intron 19 of the BCR gene was sequenced at 7737Â AE 482 (Figure  2, B  and E ). RNA baits were designed to cover 3. 5 kb spanning BCR1, BCR2, and BCR3 breakpoint cluster regions on the PML gene. The average sequencing depth of PML BCR3 and BCR1/2 were at 6916Â AE 268 and 6698Â AE 265, respectively (Figure  2, C  and E ).
Eleven PCRs (BCR::ABL1, n Z 9; PML::RARA, n Z 2) were designed to analyze the oncogenic fusion breakpoint by Sanger sequencing according to GeneFuse output (n Z 10) except for UPN8, for which only ABL1::BCR breakpoints were identified. All 11 oncogenic breakpoint sequences could be confirmed by Sanger sequencing, verifying the accuracy of breakpoint identification by GeneFuse on aCAP-Seq data (Supplemental Figures  S2, S3 , and S4).
Impact of Read Length on Fusion Characterization by aCAP-Seq All samples were sequenced by using a 2 Â 300 bp flow cell (Illumina) as increased read length was expected to maximize the number of reads spanning the breakpoint and improve fusion detection sensitivity. Using 70 ng (QXT) to 150 ng (XT-HS1) of input DNA for library preparation led to a mean insert size of 235 bp. With 2 Â 300 sequencing cycles, fusion-supporting reads were mostly split reads (82 AE 2%) and reads with soft-clipped bases (15 AE 2%), whereas mate reads were less abundant (3 AE 1%) (Figure  4A ). However, this sequencing strategy implies limited sequencing turnaround time (56 hours per run) and sequencing platform flexibility (MiSeq). Fusion identification by aCAP-Seq was thus evaluated on the whole fastq data set positive for a fusion after in silico trimming of reads to 150 bp (see Materials and Methods).
5(2 Â 150 cycles) chemistry (Reference no. 20024905). A pool of 16 libraries was prepared for each run according to jmdjournal. org -The Journal of Molecular Diagnostics Illumina instructions. Then, 600 mL of denatured library pool at 17 pM containing 1% PhiX was loaded on an Illumina v3 2 Â 300 bp chip and sequenced. Demultiplexing and adapter trimming were performed with MiSeq reporter or bcl2fastq (NextSeq 500). Bioinformatics Pipeline fastq files were trimmed to remove low-quality bases with Sickle (1. 33) (GitHub, https://github. com/najoshi/sickle, last accessed May 1, 2022) for QXT libraries or with bbduck (38. 23) for XT-HS libraries. Trimmed reads were aligned on the hg19 reference genome provided by the Broad Institute (Cambridge, MA), with BWA-MEM 11 (0. 7. 15) and the option -M to flag shorter split hits as secondary.
005; and Ignore variants with >90% support on one strand Z no. A consensus variant call format file was finally generated and annotated with ANNOVAR 16 (20160201) and SNPEff 17 (4. 3 t). The Integrative Geno- mics Viewer (Integrative Genome Viewer v 5. 01; Broad Institute) was used for visual low VAF mutation curation. Fusion Calling HmnFusion (0. 7. 3) was designed to analyze some panel of genes sequenced with the aCAP-Seq library and performs with a high sensitivity and specificity. Source code (IDDN. FR. 001. 440,002. 000. S. P. 2021. 000. 31230) and Docker images are available at https://github. com/ guillaume-gricourt/HmnFusion (last accessed.
Advances in the different fields of application on Underwater Wireless Sensor Networks (UWSNs) makes this environment more attractive for researchers and industry. Communication in this environment faces exceptional challenges, since its data transmission is experiencing a limitation in the use of bandwidth, the presence of surrounding noise and also the long delays of acoustic propagation. It is of great benefit to design an adaptive transmission control protocol (TCP) for the underwater environment. In this work, we propose U-New Reno a transmission control protocol suitable for the marine environment wherein we consider both the adaptation of the maximum size of the congestion window and the expiry of the round-trip time (RTT). U-New Reno can achieve a very significant improvement concerning the retransmission rate of the packets and the evolution of the number of packets delivered.
Node, transmission rate, many-toone data transmission scheme, and dynamic time-varying transmission channels. Communication in this environment is very sensitive to disturbance which makes the use of normal transmission protocols difficult to adopt. Congestion significantly affects Quality of Service (QoS) parameters such as packet delivery rate (PDR), Packet lost, duplicated packet, end-to-end delay and power consumption in wireless nodes  (Yang et al. , 2018). Effective congestion avoidance is an important performance indicator of UWSNs. The TWSNs congestion variants being unsuitable for this environment force researchers to try to adapt these protocols or to implement new variants dedicated specifically for this type of environment. Layer 4 of the transmission protocols guarantees a reliable delivery of the packets while keeping the order of these packets with an end-to-end, connection-oriented transmission for all network applications using the IP service  (Tian et al.
To start studying data transmission in the aquatic environment, several comparative studies of different standard TCP variants in the underwater environment were carried out using the Aquasim simulator  (Xie, et al. , 2009). The results of these simulations revealed that only TCP Vegas  (Brakmo et al. , 1994)  which uses a proactive approach of Reno with modification of the Slow-Start and TCP New Reno were able to give satisfactory results. Therefore, the work done in  (Bennouri and Berqia, 2020)  focused to study  these two TCP variants; Vegas and New Reno in order to determine the most suitable protocol in a marine environment. They also studied their behavior with two different routing protocols. The simulation results show that New Reno TCP performs better than Vegas. In addition, the performance of the two TCPs using Destination-Sequenced Distance Vector (DSDV)  (Guoyou, 2002)  routing protocol gives more efficient result than those using Adhoc On-demand Distance Vector (AODV)  (Perkins and Royer, 1999)  routing protocol.
After determining the most efficient TCP for data transmission in the underwater sensor network. Adapting and improving this protocol in this environment has been the goal of our works  (Bennouri et al. , 2018; Bennouri et al. , 2019). We have proved the validity of separately varying two parameters of this traditional TCP New Reno protocol. In the first work  (Bennouri et al. , 2018) , we varied the CWND parameter to improve the performances of TCP new reno in an underwater acoustic transmission. In the second work  (Bennouri et al. , 2019) , we managed to adapt the initial value of the RTT parameter for better performance. Following this approach and taking into account the variation of these two parameters simultaneously. In this work, we present an algorithm that defines a new TCP adapted to this environment named Underwater New Reno TCP (U-NewReno TCP).
Simulation tool We performed extensive simulations using Aqua-sim  (Zhu et al. , 2013)  as a simulation tool which is based on NS2 to simulate and evaluate our proposed TCP U-NewReno. The advantage of using Aqua-sim consists in the presence of a simulation package which works in parallel with the CMU wireless package which support NS2 and allows the independence of the wireless package. Currently, Aqua-sim is composed of 4 files: Aqua-Sim has the same object-oriented structure as NS2. In addition, the network implementation is also done by C ++ classes as well as NS2  (Zhu, et al. , 2013). IW ¼ 2 Â.