dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical Practice Points • Studies of anti-EGFR agents in mCRC show that favorable ETS and DpR are associated with prolonged survival, but data on the association between ETS, DpR, and baseline patient characteristics/prognostic factors are limited. • In particular, it remains unclear whether Köhne category, BRAF status, or baseline tumor load would be the best predictor of ETS or good DpR in these patients. • A retrospective analysis of the PRIME (NCT00364013) and PEAK (NCT00819780) studies demonstrated that patients with RAS wild-type mCRC categorized at baseline as high risk by the Köhne criteria (or those with BRAF mutations) were less likely to achieve an ETS of 30% or greater or a high DpR than those in lowor medium-risk categories. • The baseline tumor load was not predictive of ETS or DpR.
Acknowledgments The authors thank all patients and investigators involved in the PEAK and PRIME studies. Medical writing support (including development of a draft outline and subsequent drafts in consultation with the authors, assembling tables and figures, collating author comments, copyediting, fact checking, and referencing) was provided by  Debbie Sherwood, BSc, CMPP , and  Emma McConnell, PhD , CMPP, at  Aspire Scientific (Bollington, UK ), and funded by  Amgen (Europe) GmbH (Rotkreuz, Switzerland).
Whole Genome Sequencing DNA extraction was performed with DNeasy UltraClean Microbial Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Twelve S. aureus strains were sequenced by Illumina MiSeq Sequencing system (Illumina, San Diego, CA, USA), with Nextera XT DNA Library Prep Kit (paired-end read libraries, Illumina) according to supplier's recommendations. Quality control of the reads was performed with FastQC software (v. 0. 11. 7). SPAdes software was used for genome assemblies  [23]. Bacterial genome annotation was performed with Prokka  [24]. MLST, SpA Typing, ResFinder 4. 1, VirulenceFinder-2. 0 and PlasmidFinder 2. 1 were used for sequences analysis  [25] [26] [27] [28]  (https://cge. cbs. dtu. dk (accessed on 19 May 2021)). The contigs were matched against the NBCI database to obtain the closest S.
aureus reference sequence for the 12 strains isolated from the wounds at both D0 and 28 (Table  S1 ). The reads were aligned to reference sequences using the CLC Genomics Workbench software (Qiagen, Germantown, MA, USA). To determine the difference of genes and Single Nucleotide Polymorphisms (SNPs) of our strains between D0 and D28, Roary  [29]  and Snippy  [30]  software were used online (usegalaxy. org (accessed on 21 May 2021)). Genome-wide representation was performed with phandango application  [31]. Whole Genome bioinformatic data are summarised in Table  S1.
Growth Curves Evaluation Growth profiles of the 12 S. aureus strains were performed in Luria Broth (LB) medium. Briefly, bacterial suspensions were calibrated to obtain an optical density (at 600 nm) around 0. 1. Each bacterial suspension was added to a 48-well plate. The plates were incubated at 37 • C for 48 h and the absorbance at 600 nm of each well was determined with the Infinite M Mano automatic absorbance reader (Tecan, Männedorf, Switzerland). We used the nonlinear regression model of Gompertz  [34]  to obtain the equations of each growth curve, as well as various notable points (Ym, Y0, K and 1/K), with GraphPadPrism 9. 1. 0 software (San Diego, CA, USA). The Gompertz equation can be written as follows: Y(t) = Ym * ( Y0 Ym exp (-K * t) ) where Y(t) corresponds to the absorbance at a time t, Ym corresponds to the maximum absorbance (stationary phase), Y0 corresponds to the absorbance at t = 0 h, K determines the lag time (1/h) and 1/K (h) the inflection point of the exponential phase.
Determination of Early Biofilm Formation To explore capacity of S. aureus to form biofilm, we used the Biofilm Ring Test ® technique (Biofilm Control, St. Bauzire, France) following the manufacturer's recommendations  [36]. Standardised bacterial cultures were deposited on 96-well plates containing magnetic beads. After incubation at 37 • C, the plates were inserted onto a magnetic block and then into a reader (Epson Scanner modified for microplate reading). Images of each well were acquired at 1. 5 h, 2. 5 h, 3. 5 h, before and after magnetisation. Data were analysed by the BFC Elements 2. 0 software (Biofilm Control) giving a result in the form of a Biofilm Formation Index (BFI). A BFI > 7 corresponds to a complete mobility of magnetic beads and thus an absence of biofilm. A BFI < 2 indicates an immobility of beads, blocked by the formed biofilm.
Description of Patients Samples from 55 patients were initially included in this study (Figure  S1 ). Finally, 52 PU biopsies collected on D0 and D28 were analysed. S. aureus was isolated in 11 biopsies (21. 1%) at D0 and in six biopsies (11. 5%) at both D0 and D28. These six patients were included in this study. Three presented PU with improved evolution (P5, P32, P41), while three had worsening evolution of their wounds (P7, P37, P50) at D28, despite appropriate management. Demographic, clinical and biological data of patients with S. aureus strains isolated on PU at D0 et D28 are presented in Table  1. Table  1. Clinical characteristics of SCI patients with pressure ulcers infected or colonised by S. aureus after 28 days and according to wound evolution (improvement vs. worsening evolution).
A programmable temperature controller regulated constantly the temperature of the water bath to a target air temperature of 30 °C measured by an inserted thermocouple (Chrom-Constantan, OMEGA, Biel, Switzerland). Additional thermocouples were mounted in order to survey the chamber air temperatures. Photosynthetic Photon Flux Density (PPFD) was measured by two quantum sensors (LI-COR, PAR-SB 190, Lincoln, NE, USA) fixed outside the chambers. A LED lamp (LX60 Heliospectra AB, Göteburg, Sweden) illuminated the chambers with a PPFD of 600 ± 30 µmol m -2 s -1. Temperature and PPFD data were recorded by a 21× Campbell data logger (Campbell Scientific Ltd. , Shepsherd, UK).
A programmable temperature controller regulated constantly the temperature of the water bath to a target air temperature of 30 • C measured by an inserted thermocouple (Chrom-Constantan, OMEGA, Biel, Switzerland). Additional thermocouples were mounted in order to survey the chamber air temperatures. Photosynthetic Photon Flux Density (PPFD) was measured by two quantum sensors (LI-COR, PAR-SB 190, Lincoln, NE, USA) fixed outside the chambers. A LED lamp (LX60 Heliospectra AB, Göteburg, Sweden) illuminated the chambers with a PPFD of 600 ± 30 µmol m -2 s -1. Temperature and PPFD data were recorded by a 21× Campbell data logger (Campbell Scientific Ltd. , Shepsherd, UK). For measuring the aboveground emissions (Figure  4a ), the plants were mounted inside the open chambers (lids removed) in the evening the day before VOC measurements.
Hence, the belowground emissions that are reported in our study do not represent true root emissions, but characterize the net VOC exchange at the soil-air interface associated with the presence of the plants. Statistical analyses were carried out while using XLSTAT statistical software (v. 7. 0, Addin soft, Paris, France). The normality and homogeneity of data were assessed by the ShapiroWilk and Levene test. If the data showed normality and homogeneity, then the genotype effect was tested with a one-way ANOVA, followed by a Bonferroni post-hoc test for multiple pairwise comparison; otherwise, the non-parametric Kruskal-Wallis test combined with a Dunn test was applied. Paired Student tests or Wilcoxon signed-rank tests were used for the comparison of VOC emissions under light and dark. The differences between groups of measured variables were considered to be significant at a probability level of p < 0.
90 • C min -1 to 170 • C, 10 • C min -1 to 250 • C,held for 6 min. GC-MS both used helium as carrier gas at a constant flow of 1 mL min -1. Transfer lines were kept at 200 • C and ion sources at 250 • C. MS acquisition was operated in scan mode (m/z range: 35-350), applying an ionization energy of 70eV. The peaks were identified by comparing their retention indices and mass spectra with those of commercial databases (NIST 2005, Wiley 2009, Adams, 2005, supplementary data Table.
Best practice in data assimilation worldwide From the very beginning, nuclear technological science intended all concepts and statements to have a solid basis in reality. In all domains of nuclear engineering from design to safety regulation, it seems crucial having access to objective observations, including operational background, basic and dedicated Integral Experimental (IE) programs  [39] [40] [41] [42] [43] [44]. However, we could use both legacy and newly established IEs to improve or to validated nuclear data libraries. The only issue is that we have to unfold somehow the IEs data using them in a nuclear data evaluation process. It is possible to do if IEs are numerous, their set is statistically significant and there is a robust DA approach consistent for a given field of interest  [1, 2, 8, 19].
As said, Data Assimilation contributes to a nuclear data evaluation combining differential and integral experiments data. In this case we are dealing with the simple errors À discrepancies between calculated and experimental values À and their covariances in order to generate optimally balanced problem-oriented libraries. Eventually, Data Assimilation could substantiate a science-driven Validation providing assessor with an evidence-based background. In addition, Data Assimilation could be used in gap analysis somehow contributing in an establishment of dedicated basic research programs. Further development of Data Assimilation for Nuclear Data evaluation could be considered, among others, by the following axes: (1) an extension of the applications enhancing comprehensive optimization and validation of nuclear data libraries; (2) an improvement of numerical algorithms involving recently developed data science techniques; and (3) an elaboration of experimental databases.
Summarizing the reasoning above we could conclude that Data Assimilation, as an approach, has a sufficient maturity for the nuclear engineering applications having, at the same time, significant potential for further refinement. The paper is written in a memory of Dr. Massimo Salvatores. He, among others, made a great contribution to the rise of a Perturbation theory and Data Assimilation involvement in a broad range of scientific domains of nuclear engineering, including reactor physics and control, innovative technologies and, on a top of this, in nuclear data evaluation and validation. We would also extend our appreciation to OECD/NEA staff and expert groups' members for their deep involvement in a scientific discussion on a role and practical implementation of Bayesianbased methodologies in a nuclear technological science.
Acquisition of MRI data MRI scans were conducted using a Siemens Prisma 3. 0 T Magnetic Resonance Scanner (Siemens Healthineers, Erlangen, Germany) with a standard 20-channel head coil. Each neuroimaging session included one 3D T1-weighted structural scan and 4 scans of blood oxygen leveldependent functional MR imaging (fMRI). fMRI scans were performed using following parameters: repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, voxel size = 3 × 3 × 3 mm, and number of slices = 30. For anatomical reference, a T1-weighted image was scanned before fMRI examination with TR = 2300 ms, TE = 2. 32 ms, voxel size = 1 × 1 × 1 mm, and number of slices = 192. Four designed tasks were performed by the participants during fMRI examinations, including closing eyes normally, closing eyes intensively, pursing lips, and lifting the tongue up against the palate and down toward the floor of the mouth.
fMRI data preprocessing Task-related functional image analysis was performed with the fMRI Expert Analysis Tool (FEAT, http://www. fmrib. ox. ac. uk/fsl) from FMRIB Software Library (FSL v. 6. 0)  (Jenkinson et al. , 2012; Smith et al. , 2004). Individual-level pre-processing included brain extraction, motion correction, spatial smoothing using a Gaussian kernel of full-width halfmaximum of 6 mm, high-pass temporal filtering (60 s cutoff), and linear coregistration of functional images to their high-resolution T1-weighted structural image with FMRIB's Linear Registration Tool (FLIRT). A univariate general linear model (GLM) was applied to estimate contrast images of each task condition. Task regressors were convolved with a double-gamma hemodynamic response function. To correct for multiple comparisons, the resulting Z-statistic images were thresholded using clusters determined by z > 2.
Correlation analysis between neural activation and FN function To examine the correlations between fMRI measures and FN function, we further conducted region of interest (ROI) analyses. ROIs were selected based on the group-mean activities in patients, with reference to previous facial-movement related areas  (Rottler et al. , 2014). Detailes are provided in section 3. 4. Pearson correlations of task-related activation with its corresponding continuously measured CMAPs and the Spearman correlation with HB grading scores were conducted in SPSS (version 20; IBM Corp. , Armonk, NY, USA). All variables using in the correlation analyses have passed the normality test. We checked for the associations of brain activation with both 1) nerve function during the same visit, 2) nerve function during follow-up visits to investigate the prediction effect.
Acknowledgements This work was supported by grants from the  National Natural Science Foundation of China  (Grant No. 81471239 , Beijing, China),  Platform Construction of Basic Research and Clinical Translation of Nervous System Injury  ( PXM2020_026280_000002 ,  Beijing Municipal Health Commission ), and the  Institut pour la Recherche sur la Moelle Epinière et l'Encéphale (IRME, Paris, France).
Statistical analysis Collected data were analyzed using IBM SPSS Statistics for Microsoft Windows version 23. 0. Continuous variables were expressed as Mean ± Standard deviation (SD) and categorical variables as percentages. The Student's t-test was used for group's comparisons. The one way analysis of variance (ANOVA) was performed for continuous variables, chisquare test and equivalents for categorical variables. The level of significance was set at p < 0. 05.
Materials and Methods This descriptive, multi-continental study was conducted to enhance the understanding of the variabilities of mineral content of commercially available bottled drinking water worldwide. The mineral content of bottled still water and bottled sparkling or carbonated water across different manufacturers was analysed globally. For data collection, the information regarding mineral composition was read from the manufacturers' ingredient label on water bottles which were commercially available in the two main supermarket chains of each country. As an alternative, the online shop of the supermarket could be used. Minerals of interest were bicarbonate, calcium, magnesium, potassium, sodium and sulphates. All data were obtained in milligrams per litre (mg/L) or otherwise converted to mg/L.
The study was conducted in 21 countries worldwide including: Australia, Belgium, Brazil, Canada, France, Germany, Greece, India, Italy, The Netherlands, Poland, Romania, Russia, Saudi Arabia, Serbia, Singapore, Spain, Switzerland, Turkey, The United Kingdom and The United States. For statistical analysis, the software of SPSS, version 26 (IBM Corp. , Armonk, NY, USA), was used. A check for normality showed that the data were not normally distributed, therefore they were treated as non-parametric data. Descriptive statistics and simple boxplots were used to graphically show the distributional features of the data. To improve the visual representation of the data, some extreme values were excluded from the boxplots. The data are available as supplement to the figures.
The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4 TB/s in real time. Because LHCb's physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. Over the past six years, the LHCb collaboration has undertaken a bottom-up rewrite of its software infrastructure, pattern recognition, and selection algorithms to make them better able to efficiently exploit modern highly parallel computing architectures. We review the impact of this reoptimization on the energy efficiency of the realtime processing software and hardware which will be used for the upgrade of the LHCb detector.
Introduction The upgrade of the LHCb detector, due to begin datataking in Run 3, will use a triggerless readout to send the raw data for the full 30 MHz of LHC bunch crossings to a custom data processing centre. Its dataflow is illustrated in Figure  1. With a peak data rate of 5 TB/s and an expected average rate of 4 TB/s, LHCb's upgrade will have to handle similar data rates as the ATLAS and CMS HL-LHC software triggers, but around five years earlier, making this one of the most complex data processing challenges in HEP today. This data processing will use off-the-shelf components to first build the raw bunch crossing fragments from each subdetector into a single "event" (Event Building), and subsequently to group the detector hits into higher level physics objects (High Level Trigger or HLT) and decide whether these are of sufficient interest to warrant further analysis of the event.
Its requirements and design were described in  [1]. The first HLT stage (HLT1) performs a partial event reconstruction based on a relatively small subset of inclusive selection algorithms, each of which selects a general topology of interest to a range of LHCb's physics analyses. The events which receive a positive decision at HLT1 are then processed by HLT2, which performs a full reconstruction and uses hundreds of selection algorithms to fully build and isolate signal candidates of interest to specific LHCb analyses. The events which receive a positive decision at HLT2 are recorded to permanent storage and sent to the GRID for further processing and distribution to the analysts. LHCb's real-time processing software is historically based on the GAUDI  [3]  framework, with a number of additional features related to data integrity and the bookkeeping of provenance information required by the real-time use case.
The RTA model was further refined during Run 2 datataking to allow an analysisspecific subset of raw detector data or associated pattern recognition objects to be appended to each RTA signal candidate. [8]  This transformed the binary choice between the traditional offline and RTA analysis models into a spectrum, which each analyst could tune to balance the data rate per selected event in the way which best suited their analysis. LHCb decided on the basic parameters of its upgrade real-time processing architecture, including cost, in 2014. At the time CPUs were expected to rapidy evolve towards having dozens if not hundreds of cores. This guided LHCb's priorities: the software had to become thread-safe, and the transient data classes had to become much lighter to fit better into the processor cache and optimally support vectorized computations.
In parallel LHCb hedged its bets by implementing various reconstruction algorithms on GPUs and other highly parallel architectures. These efforts converged in 2020, with LHCb choosing a GPU architecture for HLT1 while remaining with the traditional CPU architecture for HLT2. [9, 10]  The fact that this hybrid architecture could be delivered within the available cost envelope is a combination of the price evolution of the GPU and CPU architectures, the ability to exploit CPU processing resources left over from Run 2, and performance gains made by LHCb's software developers over the past years. Although this performance evolution has been documented in public notes, we summarise it here in a coherent manner, making clear the importance of each component. We document for the first time the impact of the new GPU and CPU architectures, and the performance gains of LHCb's software, on the power consumption of the system.
Finally, we discuss the implications of these developments on the future evolution of LHCb's real-time processing, particularly in the context of a planned second upgrade of the LHCb detector. [11, 12]  2 Evolution of LHCb's HLT performance We will use HLT1 in order to illustrate how the performance of LHCb's software developed over the past years. As this part of the codebase has to handle the biggest processing throughput, of around 30 MHz and 4 TB per second, it also places the most stringent requirements on the software framework and algorithms, and exposes suboptimal implementations that could remain hidden in later processing steps where more time is available. It is worth noting here that because of LHCb's budget constraints, HLT1 had to be implemented on O(10 4 ) physical cores, which at 30 MHz requires a processing rate of 3 kHz from each core.
2. Reoptimization of the transient data structures inspired by the PODIO  [14]  paradigm. This work involved the removal of unnecessary pointer chasing, a switch to SIMDfriendly structure-of-arrays format wherever possible, and the removal of unnecessary data members to minimize memory use and fit better into the CPU cache. In addition a dedicated optimization of the I/O configuration was required because of the extremely high per-core throughputs which had to be sustained. Thanks to this optimization, throughputs of over 5 kHz or 500 MB per second per physical CPU core could be achieved without saturating the I/O as documented in  [15]. In particular this work enabled the efficient use of AMD's new EPYC generation of CPUs, which were released in 2019 and offered up to 64 physical cores per socket as compared to the 10 cores of the reference E5-2630-v4 Intel Xeon node which formed the backbone of LHCb's Run 2 real-time processing infrastructure.
The performance of LHCb's CPU HLT1 on the 7502 AMD EPYC with 32 physical cores per socket was also documented in  [13] , reaching 171 kHz per dual-socketed server, a factor 4. 5 times faster than the reference dual-socket E5-2630-v4 Intel Xeon node. As the TDP of the 7502 EPYC is 180 Watts, while that of the E5-2630-v4 Xeon is 85 Watts, it seems apparent that the new generation of servers would also enable a significant improvement in energy efficiency, as we will document quantitatively in the next section. In parallel to this reoptimization of its CPU software, LHCb had been pursuing R&D activities into GPUs and other highly parallel computing architectures since the early 2010s. In early 2018, since there was not yet a CPU HLT1 which could fit into the available computing resources, it was decided to attempt to implement the full HLT1 on GPUs as a hedge against the possibility that the CPU implementation would not make it in time.
This project, codenamed "Allen", successfully concluded in 2020 and demonstrated sufficient benefits over the CPU implementation that LHCb finally decided to switch its baseline to a GPU HLT1. This decision was taken despite the fact that by 2020 the optimized CPU HLT1 discussed earlier could fit into the available computing resources, and the reasons for it have been documented in more detail elsewhere. [16]  In terms of software design both the CPU HLT1 and GPU HLT1 followed analogous design principles for efficient, parallel and architecture-aware programming. The latest documented performance of the GPU HLT1 can be found in  [17] ; for the purposes of this paper the relevant number is a throughput of 148 kHz on a reference Nvidia GeForce 2080 Ti GPU with a TDP of 250 Watts. Here again we see that LHCb's asynchronous processing architecture allows a GPU to leverage its parallel processing capabilities so that a compact system of O(200) GPU cards, comparable to the number of boards typically used in "hardware" first-level triggers, is capable of ingesting the full bunch crossing rate of 30 MHz.