dataesr/scientific-paragraphs-categorization huggingface dataset:
Each deposit identification was checked using the MSI-2 database (https://msi. happy-dev. fr/). MALDI-TOF mass spectrometry data analysis. Preprocessing. MALDI-TOF raw data were preprocessed using python environment 3. 8 with smoothing using Fourier transformation, the asymmetric least squares method  [20]  for the baseline correction and peak picking with the detection of the sign changes in the spectra derivative  [21]  (Figure  1 ). Alignment. Alignment of the spectra was performed after the preprocessing step with MSIWarp, a Python package provided with C++ implementation. MSIWarp is a flexible tool compatible with multiple instrument types to perform mass alignment of mass spectrometry imaging spectra  [22]. The alignment approach works on TOF data and reduces the mass range shift by applying a recalibration function on mass (m/z) data and by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra.
It can be applied using a reference spectrum. Here, the chosen reference spectrum was the spectrum with the highest correlation coefficient with all other spectra. Step-by-step preprocessing of the spectra from the raw spectrum to the processed spectra before use in the machine learning phase. Alignment. Alignment of the spectra was performed after the preprocessing step with MSIWarp, a Python package provided with C++ implementation. MSIWarp is a flexible tool compatible with multiple instrument types to perform mass alignment of mass spectrometry imaging spectra  [22]. The alignment approach works on TOF data and reduces the mass range shift by applying a recalibration function on mass (m/z) data and by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra.
However, in contrast to image recognition, experimental data on MALDI-TOF mass spectra remain scarce. Although MALDI-TOF mass spectrometry has become the main method used for the routine identification of bacteria, yeasts and filamentous fungi, only a few studies have explored the benefit of deep learning algorithms in MALDI-TOF spectra classification. This observation can be applied either in studies designed to distinguish closely related species or to identify a particular characteristic within a microbial species, such as resistance to certain antimicrobial molecules or belonging to an epidemic clone. To date, no study has focused on the preanalytical steps involved in classifying spectra using a neural network. We show here that these steps are important by highlighting the role of the culture media, the growth time, the machine used to acquire the spectra and, finally, the mathematical treatment applied to the spectrum, in particular its alignment with a reference spectrum before its classification by the neural network.
In some cases, this has led to the inclusion of spectra acquired at various ages of the culture in the reference databases to improve the identification performances. In the special case of the search for clones within a yeast species, the degree of precision makes it essential to control this parameter. Beyond the colony's time of growth, our study showed the importance of the culture medium on which the colonies are grown in obtaining the most reliable results. This was not a surprise for us, as this parameter has often been pointed out in studies, even though those studies concluded that the impact of such variation on identification reliability was not a hurdle. In the case of the search for clones, the level of precision is such that it would be better to consider this parameter. Our study shows that classifying clones was possible either by extending the learning process to several culture media or by restricting the use of the model to spectra obtained from isolates cultured on the same medium as that used for learning.
Thus, an outbreak involving 39 cases in two different hospitals is already a problem, which is why it is necessary to establish good detection tools. All isolate identifications in our study were confirmed by MALDI TOF mass spectrometry using both the Bruker database and MSI-2 online, and all obtained C. parapsilosis identifications with high scores, confirming the species. Nevertheless, we acknowledge that the MALDI-TOF, even with a high score, may not be enough to ensure the quality of the identification results. Therefore some of the isolates used for this study have been sent to the Belgian collection of microorganisms (IHEM 28980; IHEM 28981; IHEM 28982; IHEM 28983; IHEM 28984; IHEM 28985; IHEM 28986; IHEM 28987; IHEM 28988; IHEM 28989; IHEM 28990; IHEM 28991; IHEM 28992; IHEM 28993).
II. EXPERIMENTAL AND THEORETICAL METHODS Bulk CrI 3 single crystals were grown by chemical vapor transport, by reacting chromium powder (99. 5%; Sigma-Aldrich) and anhydrous iodine beads (99. 999%; same supplier) in a 1:3 ratio inside a glovebox with an argon atmosphere. Figures  1(a  netometer with magnetic fields μ 0 H up t p 7 T applied parallel and perpendicular to the crystallographic c axis, respectively. The calculations of the RIXS spectra of CrI 3 in this paper were performed in the framework of the Anderson impurity model (AIM) using the QUANTY software package (for detailed information, see Supplemental Material  [22] ). The spectra of Cr 3+ were calculated considering the Cr 3d hybridization with the ligand valence states and the full multiplet structure due to intra-atomic and crystal field interactions.
ACKNOWLEDGMENTS The authors would like to thank  Håkan Rensmo  for the fruitful discussions. A. G. and M. A. -H. acknowledge financial support from the  Carl Tryggers Foundation  and the  Swedish Research Council (VR)  under Project No. 2018-05393. D. J. M. acknowledges support from  SSF  Grant No. RIF14-0064. O. E. acknowledges financial support from the  Knut and Alice Wallenberg Foundation ,  eSSENCE , the  Swedish Research Council (VR) , and the  Foundation for Strategic Research (SSF). O. E. and P. T. acknowledge support from  ERC  synergy Grant No. 854843-FASTCORR. Y. K. acknowledges support from VR (Project No. 2019-03569 ) and the  Göran Gustafsson Foundation. Support from the  P220 program  Project No. 075-15-2021-604  is acknowledged. The computations were enabled by resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at the  National Supercomputer Centre (NSC)  partially funded by the  Swedish Research Council  through Grant Agreement No.
2018-05973. D. P. acknowledges support from the  Swedish Research Council  under Project No. 2020-00681. T. S. acknowledges financial support from the  Swedish Research Council  (VR Grants No. 2017-05030  and No. 2021-03675 ). M. H. acknowledges support by the  European Union 's  Horizon 2020 Research and Innovation Programme , under Grant Agreement No. 824109  (European Microkelvin Platform). Work at Heidelberg has been supported by  BMBF  via the project  SpinFun  ( 13XP5088 ) and by  Deutsche Forschungsgemeinschaft (DFG)  under Germany's  Excellence Strategy   EXC2181/1-390900948  (the  Heidelberg STRUCTURES Excellence Cluster ). M. V. K. acknowledges support from  ERC  Grant No. 101002772-SPINNER.
Study Design and Treatment This randomized, double-blind, phase III study enrolled patients at 123 centers in 19 countries (Data Supplement). Patients were randomly assigned 1:1 to receive nivolumab 240 mg once every 2 weeks plus ipilimumab 1 mg/kg once every 6 weeks (combination group) or nivolumab 480 mg once every 4 weeks monotherapy (nivolumab group), along with appropriate placebo doses. Random assignment was stratified by tumor PD-L1 expression (, 1% or indeterminate v 1% to , 5% v $ 5%) and disease stage (AJCC-8 IIIB v IIIC-D v IV). Treatment continued for # 1 year or until disease recurrence, unacceptable toxicity, or withdrawal of consent. Dose modifications were not permitted and both study drugs had to be discontinued simultaneously. The details regarding a 10 mg/kg ipilimumab group that was discontinued are provided (Data Supplement).
Assessments Dual primary end points were RFS in all randomly assigned patients and in patients with tumor PD-L1 expression , 1%. Per a 2019 data monitoring committee review, results from the dual primary end point in patients with PD-L1 expression , 1% remained blinded because that end point (which occurred first) was not met; descriptive results in this population are presented here on the basis of the original allrandomized population database lock (September 8, 2020).
Baseline tumor PD-L1 membrane expression was assessed centrally with the PD-L1 IHC 28-8 pharmDx Kit (Dako, an Agilent Technologies company, Santa Clara, CA) to determine expression levels for stratification. Adverse events (AEs) were graded according to Common Terminology Criteria for Adverse Events, version 4. 0; those reported occurred between the first study dose and 30 days after the last dose. Treatment-related adverse events (TRAEs) were investigator-assessed. HRQoL was assessed at baseline and every 4 weeks until week 49 using the European Organization for Research and Treatment of Cancer (EORTC) 30-item Core Quality-of-Life Questionnaire (QLQ-C30)  14, 15  and the European Quality-of-Life-5 Dimensions (EQ-5D) summary index and visual analogue scale. 16, 17 rther study assessment details are provided in the Data Supplement.
Statistical Analysis Efficacy analyses included all intention-to-treat randomly assigned patients. The details relating to the primary end point in patients with PD-L1 expression , 1%, and sample size calculations, are provided (Data Supplement). RFS distributions were compared between the treatment groups using a two-sided log-rank test stratified by PD-L1 status and AJCC stage at screening. Hazard ratios (HRs) and CIs of the combination versus the nivolumab group were estimated using a Cox proportional hazards model with treatment group as a single covariate, stratified by the above factors. RFS was estimated using the Kaplan-Meier product-limit method. Median RFS and rates (with corresponding two-sided 95% CIs) were computed using the log-log transformation. DMFS and OS were similarly analyzed.
OS data were not analyzed at the original allrandomized database lock because of low events. OS data are presented here from the final database lock (April 20, 2021), although the events were still well below those required for statistical significance with $ 80% power (243 of the 630 deaths required overall). All analyses were performed using SAS software (version 9. 2; Cary, NC). The safety population included patients who received at least one study treatment dose.
ACKNOWLEDGMENT We thank the patients and investigators who participated in the CheckMate 915 trial. We acknowledge  Ono Pharmaceutical Company, Ltd (Osaka, Japan)  for contributions to nivolumab development and Dako, an  Agilent Technologies, Inc company  ( Santa Clara, CA ) for collaborative development of the PD-L1 immunohistochemistry 28-8 pharmDx assay. We also thank  Andriy Moshyk  of  Bristol Myers Squibb  for assistance in the analysis of patient-reported outcomes. Professional medical writing and editorial assistance were provided by  Melissa Kirk, PhD , and  Michele Salernitano  at  Ashfield MedComms , an  Inizio company , funded by  Bristol Myers Squibb.
, 2005; Strayer et al. , 2006 ). Yet, long-term data has rarely been used to disentangle the effects of alien species on the taxonomic and trait diversity of fish communities, nor to investigate effects of climatic or environmental changes (but see  Guareschi et al. , 2021). Using long-term biomonitoring data from the Rhine river in western Germany , we investigated changes in the taxonomic and trait composition in invaded fish communities over time. To this end, we specifically investigated if changes in the communities' taxonomic and trait composition were driven by disappearances of native fishes or by replacements by alien fishes as well as climatic changes over time and consequently, if identifiable changes were indicative of taxonomic and functional (i. e. , trait) homogenisation and subsequently, responding to environmental changes.
Data collection To investigate the changes in composition of freshwater fish communities over spatiotemporal scales, we used a time series of fish abundance from the Rhine river in western Germany. Fish samplings were conducted in the Rhine river between 1984 and 2018, although not continuously (Table  S1 ), over different seasons (i. e. , spring and summer) and from 31 sites. In total, the samplings covered ~170 km of the Rhine river from the border to the Netherlands upstream (6°12′55″N-7°11′55″N; 50°39′49″E-51°49′46″E; Fig. 1 ). Fish were collected at each site following a standardised protocol by bank fishing from the right river bank through continuous electrofishing. Over the study period, 403 samplings were conducted, capturing a total of 43,888 individuals identified at the species level.
Fish biological and ecological traits The sampled fish species were characterised with traits extracted from three resources: www. freshwaterecology. info (Schmidt-Kloiber and Hering, 2015), www. fishbase. org (Froese and Pauly, 2010)  and scholar. google. com (Google Scholar sources). When trait data were missing at the species level, information was retrieved from the genus. We retained a total of 25 traits split into: (i) 13 ecological traits, sub-divided into 43 'modalities' (e. g. if the considered ecological trait is 'habitat', the different modalities correspond to the diversity of habitats which the species can occupy; Table  S2 ) describing the environmental tolerance of species, and (ii) 12 biological traits with 37 modalities reflecting characteristics of species (Table  S2 ;  Devin and Beisel, 2007).
Community metrics To investigate changes in the composition of the fish community over the investigated period , five commonly used taxonomic metrics were computed for each site and year: (i) total abundance, (ii) species richness, (iii) Shannon diversity, (iv) Pielou evenness and (v) temporal species turnover (Table  S3 ;  Baranov et al. , 2020). Taxonomic metrics were computed using the R package 'vegan'  (Oksanen et al. , 2020). The turnover of overall communities, native communities and alien communities were calculated using the turnover function implemented in the R package 'codyn'  (Hallett et al. , 2020). In addition, we calculated the beta-diversity (i. e. , as a measure of the ratio between regional and local species diversity) across all sites sampled within each year as a proxy for taxonomic homogenisation using beta.
Trait metrics To investigate trait changes in fish communities over time, all trait modalities (i. e. , ecological and biological) were 'fuzzy coded' with the prep. fuzzy. var. function following  Chevenet et al. (1994)  using the R package 'ade4'  (Dray and Dufour, 2007). The fuzzy coding procedure indicates to which extent a taxon exhibits each trait category by proportionally scaling them between 0 and 1  (Schmera et al. , 2015). This is essential as it simplifies the synthesis of trait information and enables the synthesis of diverse kinds of biological information  (Baker et al. , 2021). Then, four trait metrics: trait richness (TRic), trait divergence (TDiv), trait dispersion (TDis) and trait evenness (TEve; Table  S3 ;  Baker et al. , 2021; Renault et al. , 2022)  were calculated using the alpha.
Abiotic and biotic predictors Site-specific characteristics were used to investigate the spatiotemporal changes in fish communities. To assess the effects of climatic variables, we extracted the average daily temperature and precipitation of the 12 months before the sampling event from a gridded European-scale observationbased dataset for each year and site (spatial resolution: 0. 1 °C; www. ecad. eu/E-OBS/;  Cornes et al. , 2018). Lastly, we estimated the richness and abundance of alien species in each year of each time series as biotic predictors to investigate their effects on the community.
This produced diagnostic plots of the time series with which periods of significant changes were identified and superimposed within the respective time series upon the respective trends over time  (Simpson, 2018). Based on the results from the autoregressive models we identified significant changes to have occurred in ~2010. Accordingly, we splitted the study period into two  (1984-2010: 25 years; 2010-2018:  8 years; Fig. S1 ) to identify the effects of alien fishes in the community. Taxonomic and trait metrics were modelled using the gam function from the R package 'mgcv'  (Wood, 2021). All metrics were modelled using a negative binomial distribution except for species abundance and richness, modelled using a Poisson and Gaussian distribution respectively. Hence, each model was composed of the specific response variable (i.
Acknowledgements The authors express their sincere thanks to  Michele Mugnai  for helpful comments on a previous version of this manuscript and the  "Commission Bourses de Mobilité à l'Étranger de l'Université de Rennes 1 " and the  ERASMUS + program  for their financial support. The authors further acknowledge the help of  Dr. Lep Omis. PH and PJH received funding from the  EU Horizon 2020  project  eLTER PLUS (Grant Agreement No 871128 ). IS is supported by the  Grant Agency of the University of South Bohemia , project number  065/2022/Z.
It's applied through the collection of captured photography of urban built environment to build an augmented urban reality model  [35] [36] [37] [38] [39] [40]. Thereby, thematic analyzes of built urban environments require the acquisition of 3D urban landscape data, street furniture and several other real visual data. The data to be processed are bi-dimensional (2D) images captured from the tri-dimensional (3D) scene. The objects in 3D are generally composed of related parts that joined from the whole object. In computer graphics, we usually use a specialized software, for instance, Maya  [41]  or Blender  [42, 43]  to interactively create models or procedural 3D modeling  [44] [45] [46] [47] [48]  which creates a mathematical representation of a 3D object. It is common to use a few photographs as references and textures to generate models using a modeling tool.
Doing an aperture setting narrow enough to maintain a crisp focus is recommended  [53, 58]. The spatial precision between the pairs of images and the chromatic density of the textures are a guarantee of the quality of the images collected for the 3D reconstruction of built urban environments. Accordingly, key factors ensuring good quality of input data [15,  52, 53, 58, 90]  are summarized in Table  1. Our photographic database is made up of 800 photos taken in compliance with the overlap constraints to feed the model. The entire collection is organized into 799 image pairs. A first step consists in sorting the truly calibrated image pairs according to the constrained constraints of the stereovision image matching.
Datasets and input quality analytics The quality of data (accuracy, precision, and resolution) taken by sensors as smartphones is determined by many factors related to both the capture technique and the physical environment. Ideal physical conditions should favor diffused and homogeneous lighting and all protruding urban objects should have enough space around them. In addition, when taking photos, special attention should be paid to the following object/environment characteristics: sufficient texture detail and minimal reflective surfaces. To select from the entire set of photographic data, the images meeting these criteria as well as those set out in Table  1 , a valuation of the threshold values (high, and low) based on a fuzzy set as shown in Figure  7. To ensure the quality of the input data, the input image quality sorting process consisted of sifting through the 1028 raw images captured for the entire study area.
Indeed, the 800 photos organized in 799 pairs to constitute the input database are the result of the application of this cleaning process. Also, although variations in the quality of photogrammetric data are attributable to factors beyond the control of the operator, several steps can be taken to increase the likelihood that the data collected will achieve the desired quality. The following three points of vigilance are in order: 1) Consider the expected data collection conditions (e. g. , weather, lighting), the quality of the camera and the lens. 2) Using the target range and camera specifications, calculate the desired spacing between successive frames to ensure adequate overlap. The interval [0. 7-0. 9] is the optimum since the value of 0. 7 already gives excellent results. 3) After data collection, review and remove any poor quality/blurry images by manual or automatic means.
Performance analytics and originality To evaluate and measure the performance of our FPAK approach associated with the ARKit rendering engine, the results obtained are compared with two other approaches  [64, 87, 93, 95]  on the same basis of the five constraints referenced in Table  2. The first comparison model is the Deterministic Simulated Annealing (DSA) metaheuristics optimization algorithm. In  Pajares and Cruz [95] , this strategy for stereovision matching was exploited with satisfactory results. It is a comprehensive approach belonging to the category of methods that incorporate explicit smoothing assumptions and determine all disparities simultaneously by applying a energy minimization process. The limits of this approach are felt when the input database exceeds 82 pairs of stereo images and whose convergence is only reached after 30 iterations  [87].
The glider data processing The dataset is composed of the data corrected from the thermal lag and the sensor lag, despiked and interpolated every 1 m. A quality control (QC) with a data adjustment process has been performed when the gliders passed near the fixed station of DYFAMED (50 km from the coast;  Marty, 2002)  for T-S-DO and Chl-a (chlorophyll-a) measurements. The open sea site is visited every month with CTD casts and seawater sampling for biogeochemical measurements  (Marty, 2002). It provided a good reference dataset to check the quality of the glider sensors (CTD-DO, FLBBCD) and to correct the possible drifts and offsets of sensors. For the DO concentrations, the calibration coefficients of the SBE43F sensors used during the glider experiment (sn0247 and sn2905) have been adjusted (the SOC corresponds to the oxygen signal slope, the Foffset to the voltage at zero oxygen signal, and E to the pressure correction factor) based on the raw data processing algorithm  (Owens and Millard, 1985)  and by minimizing the sum of the square of the difference between the Winkler oxygen values (measured at the DYFAMED station during the monthly cruises) and the DO concentrations derived from the SBE43F sensor signal  (Coppola et al.
, 2018). The total uncertainty of the adjusted DO profiles is estimated at around ± 3 μmol kg -1. During the reference measurements at DYFAMED, the conductivity and fluorescence sensors were adjusted to correct for possible sensor drift by applying a slope and offset correction on the salinity values and by adjusting the scale factor and dark value used by the fluorescence sensor, respectively (Table  S1 ; Figures  S2-S4 ). Once the dataset of each glider section has been adjusted and concatenated (homogenization of the sensor frequencies), we used the secondary QC for physics (TSO 2 ) and for fluorescence data described by  Gregor et al. (2019). In this study, only the DO profiles of the glider descents were used for the calculations because the ascending profiles are likely to be affected by the glider turbulence on ascent.