dataesr/scientific-paragraphs-categorization huggingface dataset:
We chose an application deeply linked to biomolecule research. Nevertheless, the generality of the approach allows applications to any kind of point cloud data. Our future initiative will center on four topics. First, we will develop Genuage-cloud, a platform extension to allow complex analysis to be run on the cloud through a Django interface and entirely run the visualization and analysis in the cloud while streaming the images to the VR headset. Second, we will extend the Genuage platform to run Augmented Reality (AR) applications on glasses and tablets with a new library focused on amortizing the representation of large point clouds on devices with limited memory. In addition, we will develop new shaders to render the fusion of different data types in the VR environment, facilitating data interpretation during the analysis.
Results are displayed as changes of the trajectory colors, with intensity depending on the value of the sub-diffusive exponent. The inference analysis is performed in real-time on the selected trajectories (inset shows real-time calculation performed). Various means to display results are available. The screenshot also shows the interface in the current version of the Genuage software. Most of the buttons and displays are self-explanatory [an introduction to the design can be found in the videos associated with (Blanc et al. , 2020) ].
FUNDING We acknowledge funding from the  Fondation pour la recherche médicale (FRM ;  DEI20151234398 ), the  Agence National de la recherche  ( ANR-19-CE42-0003-01 ), the  LabEx CelTisPhyBio  ( ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02 ), and the  Institut Curie. We recognize the support of  France-BioImaging infrastructure  grant  ANR-10-INBS-04  (Investments for the future). We acknowledge the financial support of the  Agence pour la Recherche sur le Cancer (ARC Foundation ), DIM ELICIT and from  ITMO Cancer of Aviesan  on funds administered by  Inserm  (grant N  °20CP092-00 ). We acknowledge funding from the  Institut Pasteur , the  Institut Curie , the  Paris Science lettre (PSL) University , the sponsorships of  CRPCEN ,  Gilead Science and foundation EDF , the  INCEPTION  project ( PIA/ANR-16-CONV-0005 , OG), the programme d'investissement d'avenir supported by  L'Agence Nationale de la Recherche   ANR-19-P3IA-0001 Institut 3IA Prairie  and the support of the AVIRON grant from the  Région Ile-de-France (DIM-ELICIT).
MATERIALS AND METHODS Institutional ethics approval (RMH SE1020) was mandated, and data transfer agreements were obtained between the Royal Marsden Hospital and participating institutions. Adults (age >15 years) treated with systemic therapy for advanced (unresectable or metastatic) CCS were identified from June 1985 to May 2021. Patients were identified from 10 participating sarcoma reference centres across 9 countries within the WSN. Written informed consent to treatment was obtained as per local standard of care. Molecular confirmation of the diagnosis (EWSR1eATF1, EWSR1eCREB1, or EWSR1 fusion with an unknown partner) was mandatory for patient inclusion. Demographics and treatment information were retrospectively collected by investigators at each site. Radiological treatment response was retrospectively assessed through imaging review, using the same cut-off values defined by Response Evaluation Criteria in Solid Tumours Version 1.
1 (RECIST 1. 1). Imaging was carried out as per institutional standards, which is generally every 2-3 cycles of therapy. Descriptive statistics were applied, and mPFS and median overall survival (mOS) were calculated using the Kaplane Meier method using SPSS statistics version 27 (IBM, Armonk, NY). Progression-free survival was defined as the time from the start of systemic therapy to radiological progression defined by RECIST 1. 1. If radiological progression was unable to be obtained but there was overt clinical progression, then the date of clinical progression was used. Patients who remained on treatment and had not progressed at the time of data cut-off were deemed non-assessable. Overall survival was defined as the time from the diagnosis of metastatic disease to death from any cause.
8 Given the emerging evidence suggesting that epigenomic changes may play a key role in the oncogenesis of fusion-associated sarcomas, such as CCS,  16  it is indeed intriguing that alterations in the CREATE  cohort were seen related to epigenomic modifications. Drug treatments targeting such epigenomic changes, such as tazemetostat,  17  are largely unexplored in CCS but may warrant further investigations. There are currently no actively recruiting clinical trials exploring the use of agents which target epigenomic alterations. There are currently no actively recruiting clinical trials exploring the use of agents which target epigenomic alterations. Beyond trials of immunotherapy, a phase I trial of anti-mitochondrial drug devimistat plus hydroxychloroquine (NCT04593758) was conducted. 18 Given the aggressive clinical behaviour of CCS, it may be that a combination approach with epigenomic modifying drugs with chemotherapy may improve typically poor responses to cytotoxic sarcoma-type chemotherapy.
Given the short mPFS and low clinical benefit rate with sarcoma-type chemotherapy, an informed patienteclinician discussion is required to consider the use of standard cytotoxic chemotherapy. There has been interest in exploring the role of immunotherapy in the treatment of patients with CCS. In the phase IB portion of the IMMU-NOSARC trial, there were two partial responses (n ¼ 2/4) to nivolumab and sunitinib; however, the responses were of short duration. 19 In the phase II OSCAR study, ORR was 7. 1% in a combined cohort of patients with CCS and alveolar soft part sarcoma. 20 Results of two additional ongoing phase II trials using dostarlimab (NCT04274023)  21  and atezolizumab (NCT04458922)  22  are expected in 2024. Results of these sarcoma-type-specific trials are awaited to further define the role of immune checkpoint inhibitors in this population.
Additionally, we found that the mRNA of the global regulator Vfr interacts with Hfq in all three strains and further identified the involved sRNA using rGRIL-seq  23. The RhlS sRNA, previously studied for its role in quorum sensing, was found to activate vfr transcription, revealing a new link between quorum sensing and virulence regulation. The untargeted analysis of the obtained datasets also resulted in the discovery of potential new sRNAs across the three lineages. Altogether, our comparative study reveals a wide functional intraspecies diversity of Hfq interactions as well as important conserved targets.
Source data are provided as a Source Data file. samples (Fig. 4d ), which may be due to a mutation in the upstream regulator histidine kinase LadS in PA14  45  and could explain the difference in RIP-seq enrichment. Most specificities in sRNA-Hfq interactions were due to differences in sRNAs content, thus raising the need of identifying all strain-specific sRNAs. To that aim, we performed an annotation-independent peak-calling analysis in order to rescue missed targets and unannotated sRNAs using the CLIP-seq and RIP-seq peak caller PEAKachu (https://github. com/tbischler/ PEAKachu) (Supplementary Data 4). A total of 332 strictly intergenic peaks was identified across all strains and growth phases (Supplementary Data 5). Among these, we identified seven sRNAs predicted structures matching the StructRNAfinder bacterial sRNA database  46  (Supplementary Fig.
3  and Supplementary Data 6), which could correspond to unannotated sRNAs from diverse known families of sRNA. Two of them were actually the same sRNA detected in both PA14 and IHMA87 genomes, and its sequence can also be found in PAO1. In PA14, this sRNA falls exactly after a previously identified TSS that was not associated with any transcript but proposed to correspond to an unidentified intergenic sRNA  29. Moreover, more than half of intergenic peaks showed high enrichment (>10-fold), suggesting that, even if not matching sRNA databases, more new regulatory sRNAs might be present in these regions. Also, antisense intragenic peaks were identified in 286 genes across strains (Supplementary Data 7), revealing a wide array of potential antisense regulatory sRNAs, which are common across the bacterial kingdom and often regulators of their cognate gene  47.
The DNA fragment was cloned into the integration vector mini-CTX-lacZ  72  and the vector integrated into the chromosome of PA14 ΔrhlS strain. Stellar E. coli cells (Clontech, cat# 636763) were used as competent cells in the cloning procedure. Homolog genes/sRNAs identification and phylogeny. All P. aeruginosa complete genomes (n = 192) were retrieved from the Pseudomonas Genome Database  30. Homolog identification was performed by Reciprocal Best Blast Hit (RBBH) analysis  74  on the European Galaxy server 75 using all protein sequences from P. aeruginosa PA14 against all protein sequences from the other 191 genomes with minimum percentage alignment coverages of 90 and sequence identity of 50. The sequences from 66 core genes were concatenated for each genome and a multiple alignment was performed with MAFFT Galaxy version 7.
The gels were blotted onto nylon membranes (Bio-Rad, cat# 1704159) using Trans-Blot® Turbo™ Transfer System (Bio-Rad, cat# 1704150) and probed using primary anti-FLAG M2 mouse monoclonal antibodies (1:2000 dilution) (Sigma, cat# F1804), anti-Vfr (1:25,000 dilution) and anti-FliC (1:5000 dilution) rabbit antibodies  64. Secondary antibodies were anti-mouse-HRP (1:80,000 dilution) (Sigma, cat# A9044) or anti-rabbit-HRP (1:50,000 dilution) (Sigma, cat# A0545). Membranes were developed with Luminata Classico Western HRP (Milipore, cat# WBLUC0500). RT-qPCR. RNA was extracted as described for RIP-seq and treated with the TURBO DNA-free kit following manufacturer's instructions (Thermo Fisher, cat# AM1907). For RT-qPCR, 4 µl of DNase-treated RNA were used with the Luna Universal One-Step RT-qPCR kit (NEB, cat# E3005) and primers specific to PrrF1 or 6S RNA (Supplementary Data 12).
RIP-seq was also conducted on WT strains in each condition as negative controls. All RIP-seq experiments were performed in biological duplicates for a total of 36 samples. Library construction and sequencing. The quantity and quality of purified RNAs were then assessed on an Agilent Bioanalyzer and 1-5 ng of RNA were then used for library construction using the NEBNext Ultra II Directional Library Prep kit (NEB, cat# E7760). Sequencing was performed at the Biopolymers Facility at Harvard Medical School (https://genome. med. harvard. edu/) on an Illumina NextSeq500 for the 24 PAO1 and IHMA87 samples, and at the sequencing core facility of I2BC (http://www. i2bc. paris-saclay. fr) on an Illumina NextSeq500 for the 12 PA14 samples, with a total average of ~8. 4 million reads per sample. RIP-seq data analysis.
For coverage visualization, Bowtie2 alignment output files were converted to bedgraphs with the Genome Coverage tool from BEDTools  84  , counts were transformed to reads per million (RPM), and averaged between biological replicates. Average local RPM counts were used to generate coverage plots using matplotlib in Python 3. RNA abundance comparisons were done using averages of DESeq2 normalized read counts from WT samples transformed to RPM counts among the 4776 genes shared by all three strains and displayed using GraphPad Prism 7. 04. Genes were ranked on their RNA abundance based on their RPM values and a rank-matching analysis was performed for all pairs of strains in all growth phases using Pearson's correlation test. Abundance fold changes between pairs of strains in each growth phase were used to identify outlier genes with significantly different RNA abundance between strains.
For complementary analysis, peak calling was done using PEAKachu version 0. 1. 0. 2 (https://github. com/tbischler/PEAKachu) with maximum insert sizes of 60. The closest gene and distance from it were identified for each peak using the ClosestBed tool from BEDTools  84. Peaks that did not correspond to a gene found enriched with the DESeq2 method either corresponded to mRNA targets that were missed or to new unannotated sRNAs. For detection of potential new intergenic sRNAs, enriched sequences, extended by 10 bp in both directions using BEDTools SlopBed, were obtained from strictly intergenic peaks using BEDTools GetFastaBed  84. Prediction of sRNAs was performed using StructRNAfinder  46  on the obtained sequences. Potential new antisense sRNAs were manually curated from intragenic peaks that were detected on the opposite strand of their corresponding gene found with ClosestBed.
After 11 PCR cycles for amplification, libraries were then purified twice with AMPure XP beads (Beckman Coulter, cat# A63880) using a 0. 9× ratio. Sequencing was performed on a Next-Seq500 instrument (Illumina) with 150 bp paired-end reads. Data analysis for rGRIL-seq was performed using CLC Genomic Workbench 7. 0 as used for GRILseq  23, 60. The vfr reference sequences were composed of vfr CDS, 5' and 3' UTRs from each genome (PA14 (NC_008463), 716,972 to 717,799; PAO1 (NC_002516), 705,988 to 706,815; IHMA87 (NZ_CP041354. 1), 672,875 to 673, 700). To create each vfr-deleted reference genome, the sequences including vfr and part of the flanking genes were deleted from each genomic reference genome (PA14, 716,451 to 718,140; PAO1, 705,706 to 707,168; IHMA87, 672,620 to 674,060). β-galactosidase assay.
Acknowledgements We are grateful to  Yanjie Chao  for his technical advices on RIP-seq. Work in SL's laboratory was supported by the grant  AI136789  from the  NIH. The work in IA's laboratory was supported by " Fondation pour la Recherche Médicale (FRM)" [Team   FRM2017 ,  DEQ20170336705 ]. J. T. received a Ph. D. fellowship from the  French Ministry of Education and Research  and a mobility allowance from the  Initiative of Excellence (IDEX) ,  Université Grenoble Alpes  for this project. The Pseudomonas aeruginosa strain IHMA879472 was kindly provided by  International Health Management Association (IHMA; USA).
In the present work, phytoremediation of a highly Pb-contaminated soil from an old abandoned mine located in Spain using F. arundinacea was studied by applying the LCA approach. For that purpose, data on cultivation and Pb uptake of F. arundinacea were calculated from the literature, and different final uses of biomass were studied: security landfill disposal and energy recovery (this option was simulated using SuperPro Designer 9. 5). The environmental impacts of both scenarios were quantified by using Gabi 10 software. On the other hand, the LCA of two traditional treatments (soil washing and excavation) was performed. For this purpose, the former treatment was simulated using SuperPro Designer 9. 5, whereas literature data were adapted for the latter to quantify their environmental impacts.
Life Cycle Inventory Analysis (LCIA) Inventory data of the three case studies (involving all the steps) were calculated and adapted from the literature and/or simulated using Super-Pro Designer 9. 5 (Intelligen Inc. Scotch Plains, NJ, USA) as described below for each case. Primary processes of energy and material production were taken from Gabi Professional 2021 database (Sphera Solutions GmbH. Leinfelden-Echterdingen, Germany). For electricity production, Fig. 1. LCA limits of the considered case studies. the Spanish electric mix updated to the current year was used. Transportation vehicles were adapted from inventory data available in Gabi Professional 2021 database.
arundinacea is cultivated in the same way  (Delgado, 1984; San Miguel, 2008; Tilvikiene et al. , 2016). Rainfall data are summarized in Table  S1  of Supplementary material. Having in mind the total amount of biomass (24. 120 kg ha -1 year -1 ) along with the experimental root/shoot ratio of 0. 9 (in agreement with previous data reported  (Crush et al. , 2012) , biomass from roots and shoots was calculated. Then, Pb extracted by roots and shoots were calculated using BCF as commented above). In these conditions, the amount of Pb extracted per year was 11. 59 kg ha -1 , resulting in a total duration of 31 years to remove the targeted amount of Pb (358 kg ha -1 ) to fulfill local government regulations. The biomass required to remove this Pb amount is summarized in Table  1. ii. Biomass conditioning and transport: To remove the soil contained in the biomass, a vibration screening was added to calculate its energy consumption by simulation using SuperPro Designer 9.
5 according to the flowsheet shown in Fig. S1  (Supplementary material). The inventory data for transport were adapted from Gabi Professional 2021 database assuming 30 km between the contaminated area and the treatment plant for biomass disposal. iii. Biomass use: One of the key aspects concerning the sustainability of phytoextraction processes is the further use of the grown biomass  (Vocciante et al. , 2021). In this work, two possibilities are raised: A) Soil disposal: In this scenario, the harvested biomass is disposed as hazardous material in an existing landfill 30 km away from the contaminated area. The landfill was supposed as an underground deposit adapted from Gabi Professional 2021 database. B) Energy recovery: In this case, the harvested biomass is used to produce energy through a combined power and heating system (CPH).
The possibility of recycling the liquid streams allows reducing the consumption of freshwater. vi. Soil disposal: The fraction of the soil containing Pb was disposed as hazardous material in a landfill assuming 15 km from the treatment plant. As in Case 1A, the landfill was modelled as an underground deposit adapted from Gabi Professional 2021 database. vii. Soil refilling: This step consists of refilling the excavated site using fresh sand along with part of the treated soil obtained after the washing step. In this case, it was supposed that the sand was extracted from an existing quarry 30 km away from the excavated site (close to the landfill). A mini skid steer and a truck were used to charge and transport the sand, respectively. All these processes (extraction, charge and transport of sand) were taken and adapted from Gabi Professional 2021 database.
Case study 3: excavation and landfill deposition Inventory data of the selected treatments for this case are summarized in Table  3. In this process, the following steps were considered, as shown in Fig. 1:  i. Excavation: This process consists of extracting the contaminated soil by excavation. For this purpose, an excavator and a mini skid steer were used. Both processes were taken from Gabi Professional 2021 database and adapted to the functional unit, obtaining the values summarized in Table  3. ii. Soil deposition: The contaminated soil is charged and transported to the same landfill as in Case 2. The transport was carried out by truck, and the landfill was modelled, considering the contaminated solid as a hazardous waste due to the presence of Pb. These processes were also adapted from Gabi Professional 2021 database.
Event selection The data sample used for the present analysis was collected with the CMS detector in 2013, when the LHC collided protons at √ s = 2. 76 TeV. The sample corresponds to an integrated luminosity of 5. 4 pb -1. Jets are clustered from particles reconstructed with the particle-flow (PF) algorithm  [34] ; this algorithm reconstructs and identifies each individual particle in an event, with an optimized combination of information from the various elements of the CMS detector. The vertex with the largest value of the summed charged-particle track p T is the primary pp interaction vertex (PV). The photon energy is obtained from the ECAL measurement. The electron energy is determined from a combination of the electron momentum at the PV as measured by the tracker, the energy of the corresponding ECAL cluster, and the energy sum of all bremsstrahlung photons spatially compatible with originating from the electron track.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NK-FIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CIN-VESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCen-.
In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. e obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones.
Digital Image Acquisition and Preprocessing Techniques. In the literature, several studies have been presented for automatic TB detection, giving current researchers the ability to analyze the presence of TB bacteria automatically and quickly from a solid database  [28]. An automatic TB detection system is used to automatically analyze the presence of TB bacteria quickly using different steps. e first step is image acquisition and preprocessing. us, before performing the image acquisition, an essential part of the automatic TB detection system called autofocusing has to be done  [29]  to save time and provide a better focused image. Several methods have been proposed for autofocusing  [30, 31]. e performance of each method depends on different factors such as image characteristics, noise, and other specifics in the image.
Additionally, image processing techniques such as image enhancement, segmentation, and feature extraction have been used in  [35]  for TB diagnosis. To obtain input data, Poornimadevi et al. [36]  employed X-ray images; in 2017, Antony and Banu  [37]  added filtering to the same input database. Other works used a computed tomography (CT) scan for ImageCLEF, which organized a challenge based on CT image analyses of TB patients in 2017  [38]. Another frequently cited technique is machine learning (ML), which is used to model training data. e authors in  [39]  recently evaluated ML models for their efficacy in estimating TB prognosis. Other researchers detailed the ML mechanism, citing the survey in  [40]  in the development of their concepts and applications. e authors in the last work defined ML as "the union of forces between statistics and computer sciences" and "the basis for artificial intelligence.