dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluorescence acquisition was measured with excitation at 440 nm and emission at 580 nm (green fluorescence) and also with excitation at 440 nm and emission at 660 nm (red fluorescence). Several controls were also tested with the same program. Pre-treatments included mixing Aβ42-HiLyteFluor-555 with 1 µg of blocking antibodies (α-Aβ42-Ab, Anaspec, Fremont, CA) for 15 min at room temperature prior to adding viral particles or pretreating the HSV-1-gB-GFP for 30 min at room temperature with 1% NP40 diluted in PBS, pH 7, to disrupt the viral envelope, before mixing with Aβ42-HiLyteFluor-555. Controls included negative controls without virus or Aβ42-HiLyteFluor-555, virus alone, and Aβ42-HiLyteFluor-555 alone. At the end of the program, the thermocycler software gave fluorescence values for green and red fluorescence for each well.
FUNDING KB, EHF, GD and TF were supported by grants from the  Canadian Institutes of Health Research (CIHR)  (No. 106634 ), the  Université de Sherbrooke , the  Société des Médecins de l'Université de Sherbrooke (SMUS)  and the  Research Center on Aging. SR was supported by  Ikerbasque  (The  Basque Foundation for Science) , by  GV-AI-HEALTH , the  Basque Government  through the  BERC 2018-2021 program , by the  Spanish State Research Agency  through  BCAM Severo Ochoa  excellence accreditation  SEV-2017-0718  and through project  RTI2018-093860B-C21  funded by ( AEI/FEDER, UE)  with acronym " MathNEURO ". MD  and SR acknowledge the support of  Inria  via the  Associated Team "NeuroTransSF ".
Acknowledgements This work was financially supported by the  Max Planck Society , the  ANR-DFG NLE  Grant  GRANAO  by  DFG 431450789 , and by the  Italian Ministry of Research  through the  PRIN grant   HAR-VEST. K. M. acknowledges a fellowship from  Gutenberg Research College ,  Johannes Gutenberg University Mainz. We acknowledge  Xinshun Qin  and  Shixuan Du  for support with the STM measurements. C. -A. P. acknowledges the  National Science Foundation of China  (no. 11974403  and  Sino-German  Project no. 51761135130 ). Computational time on the Mar-coni100 machine at  CINECA  was provided by the  Italian ISCRA program. Open Access funding provided by the  Max Planck Society.
Background: With the advent of immuno-oncology compounds in randomized trials, we observe more and more survival curves crossing. From a statistical standpoint this corresponds to violation of the proportional hazard assumption. When this occurs, the hazard ratio from the Cox regression is not reliable as an estimate. Herein, we aimed to identify the most appropriate IObased therapy for metastatic renal cell carcinoma applying an alternative method to overcome the issue of hazard assumption violation for metaanalyses. Methods: Pubmed, EMBASE, Web of Science and Scopus databases were searched. Only phase III randomized clinical trials on IO-IO (nivo-ipi) or IO-TKI combinations were included. An algorithm to obtain survival data from published Kaplan-Meier curves was used to reconstruct data on overall survival (OS), progression-free survival (PFS) and duration of response (DoR).
Search strategy The protocol for this systematic review and meta-analysis was registered in PROSPERO (registration number: CRD42021241421). PubMed, EMBASE, Web of Science, Scopus databases were searched for studies indexed from January 1, 2016 to March 20, 2021. The following keywords were used to identify potential reports: (renal cell carcinoma OR kidney cancer OR renal cancer) AND (metastatic or advanced) AND [(systemic OR first-line) AND (therapy OR treatment)] AND (RCT OR randomized clinical trial). References from commentaries, editorials, conference publications, review articles, and from included studies were hand-searched and cross-referenced for completeness. Conference abstracts reporting unpublished data were included. Titles and abstracts of manuscripts were used to screen for initial study inclusion.
Statistical analysis RMST up to 18, 24 and 36 months for OS, PFS and DoR in patients treated with IO-IO or IO-TKI combinations was estimated employing a reconstructed survival data approach to obtain pooled survival probability curves. RMSTs and associated 95% confidence intervals (CI) up to 18, 24 and 36 months were derived from the quantification of the area under the survival probability curve through the trapezoidal rule. To support the use of RMST instead of traditional hazard ratios, we tested the assumption of proportionality of hazards on the reconstructed datasets, using the Grambsch and Therneau test  (20). OS, PFS and DoR were compared between IO-IO and IO-TKI combinations. Therefore, reconstructed data were grouped based on the category of treatment received: sunitinib, IO-TKI or IO-IO.
Being 18 months the common maximum follow-up time across all treatment groups, we estimated 18-month RMST to perform comparisons of outcomes over the maximum common study period (21). Moreover, 24-and 36-month RMSTs were also assessed, considering that a considerable proportion of patients in each treatment group reached longer follow-up times. Indirect comparisons between IO-TKI and IO-IO combinations were also made, using a network meta-analysis approach (22). Finally, a sensitivity analysis was performed in the intermediate/poor risk population, using reconstructed survival data extracted from KM curves. Being nivolumab plus ipilimumab (nivo-ipi) the major first-line option for these patients, it was compared with the other treatments for which KM curves were reported in the intermediate/poor risk groups.
Overall survival KM curves depicting OS of patients enrolled in the included RCTs, divided into treatment groups (sunitinib, IO-TKI and nivo-ipi), using reconstructed data are reported in Figure  2A. OS probabilities of patients treated with nivo-ipi at 18, 24 and 36 months were 77%, 71% and 59%, respectively. Similarly, at 18, 24 and 36 months, OS probabilities of patients receiving IO-TKI combinations were respectively 79%, 72% and 59%, while OS probabilities of patients treated with sunitinib were 72%, 65% and 54%, respectively. When Grambsch and Therneau test was employed on reconstructed datasets from included RCTs, a p value of 0. 02 was obtained for the comparison between IO-TKI and sunitinib, proving violation of the proportional hazard assumption. Table  3  displays the results of the differences in RMST up to the time point of interest.
Progression-free survival KM curves depicting PFS of patients enrolled in the included RCTs, divided into treatment groups (sunitinib, IO-TKI and nivo-ipi), using reconstructed data are reported in Figure  2B. PFS probabilities of patients treated with nivo-ipi at 18, 24 and 36 months were 43%, 37% and 33%, respectively. Similarly, at 18, 24 and 36 months, PFS probabilities of patients receiving IO-TKI combinations were respectively 49%, 40% and 22%, while PFS probabilities of patients treated with sunitinib were 34%, 26% and 18% respectively. When Grambsch and Therneau test was employed on reconstructed datasets from included RCTs, a p value less than 0. 001 was obtained for the comparisons between both nivo-ipi versus sunitinib and IO-TKI versus sunitinib, proving violation of the proportional hazard assumption.
Duration of response KM curves with numbers at risk for DoR were available for all included RCTs except for JAVELIN Renal 101, for which reconstructed survival data was not possible. KM curves depicting DoR of patients enrolled in the included RCTs, divided into treatment groups (sunitinib, IO-TKI and nivoipi), using reconstructed data are reported in Figure  2C. DoR probabilities of patients treated with nivo-ipi at 18 and 24 months were 71% and 65%, respectively. Similarly, at 18 and 24 months, DoR probabilities of patients receiving IO-TKI combinations were respectively 60% and 51%, while DoR probabilities of patients treated with sunitinib were 52% and 39% respectively. When Grambsch and Therneau test was employed on reconstructed datasets from included RCTs, a p value less than Overall survival (A), progression-free survival (B) and duration of response (C) of mRCC patients using reconstructed survival data derived from five individual studies with immune-oncology based combination therapy.
Moreover, hazard ratios may be arduous to interpret by clinicians, as they provide a measure of therapeutic efficacy of a certain treatment only compared to an alternative without being informative on its absolute clinical benefit. In light of these considerations, after verifying the violation of the proportionality of hazards assumption for the reconstructed dataset derived from RCTs, we used an approach based on RMST to investigate differences in treatment response. OS, PFS and DoR at 18, 24 and 36 months were used as surrogate for therapeutic efficacy. In addition to a reduced risk of misinterpretation of the survival impact of different therapies, individual survival data methodology is the sole to allow for the evaluation of DoR, by reconstructing timeto-event data. Despite its strengths, this study is not devoid of limitations.
Nitrogen Adsorption-Desorption Isotherms This method quantifies the pore volume and pore size distribution from the data of the nitrogen isotherms. The evaluation of the specific surface area from the nitrogen isotherm is based on the Brunauer, Emmett and Teller (BET) method  [57]. This method is based on the assumption that the quantity of gas absorbed (through the monolayer, multilayer and capillary condensation) is related to the surface area and the porous structure of the material  [58]. The test was performed using a TriStar 3000 analyzer from Micromeritics Instrument Corp. (Micromeritics Instrument Corporation, 2016, Norcross, GA, USA). The sorption/desorption isotherm was measured at 77. 35 K on a typical sample of mass equal to 0. 5 g. The data were studied with Hasley and Fass's BJH model (0.
X-ray Diffraction Figure  9  reports the four diffraction patterns for the MIX, LIG, TAN and ABS powders. The analysis was not considered suitable for specimens with fibers (WOOL, CIT, and GRA) due to the larger dimensions of the fibers and their inability to interact with the mineral composition. The peaks in the spectrum were identified using the Profex software databases (version 4. 3. 5) and the literature references in Poppe et al. [67]. The peaks in ABS diffraction patterns clearly showed the presence of smectite (6. 01 shows a strong reduction, probably due to the additive's ability to fill pores and aggre soil particles. The use of fibers does not modify the nanopore distribution and only sli reduces the specific surface area.
Acknowledgments: The authors would like to thank  Roberto Losi  for his contribution to the  Catalysis and Catalytic Processes Laboratory, Politecnico of Milan, Italy , where Mercury Intrusion Porosimetry and Nitrogen sorption isotherm were executed. The authors also acknowledge  Imran Raman Abdul  for his collaboration during mechanical tests in the geotechnical laboratory, LPM of  Politecnico of Milan. The financial support for the Doctoral Program of research was given by  Make Our Plane Great Again and Universite Savoie Mont Blanc, France. The work also benefited from the  OLIMP and Smart-Reno  projects financial support. The authors would like to thank  Minerali Industriali S. r. l. , Dal Zotto S. r. l. , Silvateam S. p. a, Soliani-Italcol S. p. a. and  The Wool Biella Company  for the materials supplied to run the experimentation.
Single particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox -2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach.
15  In addition to this, another factor that affects the correct estimation of physical quantities is the model fitting methodology employed and the number of Mean Squared Displacement points to employ in the data analysis pipeline, which require careful consideration on an ad-hoc basis for each dataset. [16] [17] [18]  Numerous toolboxes and algorithms have been produced to fulfill the need to identify and track single particles,  [19] [20] [21]  and analyse their motion employing conventional mean squared displacement analysis,  22  machine and deep learning  [23] [24] [25]  and other methods. 26, 27  Oftentimes the source code is shared by the authors and is readily available, and while in some cases they are not platform-agnostic and require licensed software to be executed, on the other hand, e.
We further elaborated on the motivation of the choices made in the production of the code, to make clarity on the driving ideas behind the design of TRAIT2D. The underlying code has been also modified to improve more functionalities in the GUI, correct bugs that were affecting it, and provide expanded functionality on the tracker side. At the release of this manuscript, no additional tracking algorithms are provided, but the authors are working on additional functionalities to add new features to the code. More details on the changes are present in the responses to the referee reports, which we invite the reader to consult. An updated version of the software (1. 2) has been submitted concurrently with the release of this version following the submission of this version of the paper, which includes many of the changes suggested by the reviewers.
Additional functionalities that have been suggested have not been implemented at present, but are nevertheless under study and should be included in future releases. A considerable effort has been put by the additional author, B. Vogler, on code review and on improvement of the analysis software performance to improve the user experience. Any further responses from the reviewers can be found at the end of the article tools available on open-source platforms, such as FIJI  28  or ICY,  29  are often more focused on the particle tracking, rather than the data analysis itself. The TRAIT2D (Tracking Analysis Toolkit -2D version), hereby presented, aims to supply the scientific community with an accessible, open-source, and platform-agnostic tool for particle tracking, simulation and analysis in two dimensions.
Particle tracking The particle tracking is developed to support a quantitative analysis module which requires extracted trajectories. We introduce a tool which allows users to adjust tracking parameters, preview results and save final trajectories. The pre-processing of the image sequence is optional and can be applied prior to the tracking algorithm. The tracking is implemented as a two-step process. Firstly, the particles are detected using spot enhancing filter (SEF)  32  and sub-pixel localisation is estimated by the radial symmetry centre approach. 31  Secondly, the detected particles are linked based on their spatial and temporal locations. The current tool was developed for the iSCAT imaging technique and the pre-processing step is built in accordance to the technique requirement. To distinguish the molecules of interest from the background, the image sequence is divided by a flat field.
Mean squared displacement analysis In this and the following section, we will briefly discuss the analysis methods implemented in the TRAIT2D software package. The first method, which we refer to in the code as "MSD analysis", consists of the approach to determine the physical parameters of diffusion dynamics based on the behaviour of the mean squared displacements against the corresponding time interval. In the case of MSD analysis, the MSD is initially calculated according to the definition: MSDðt n Þ ¼ 1 N À n À 1 X NÀnÀ1 i¼1 jrðt iþn Þ À rðt n Þj 2 (3) whereby t n = nt 0 indicates the n-th time point from the initial time t 0 , and rðt n Þ indicates the two-dimensional position vector at time t n. Notice that this notation, as well as the entire analysis pipeline hereby implemented, supposes that the time interval between localisation is constant.
D μ is the microscopic diffusion coefficient observed in the aforementioned confinement zones, which can also be expressed as D μ ¼ L 2 12τ ( 15 ) where L is the average confinement zone size. 10  It is apparent that while this data analysis pipeline appears more complex, it however readily provides an efficient way to directly extract relevant physical parameters from the particle trajectories. We point out that, although the code we hereby describe comes with the aforementioned built-in models, chosen according to the authors specific research interests, a documented and simple procedure is in place to define a new model, according to the end user-specific wishes. The only restriction is that the custom-defined models can only be used when the analysis module is imported in a Python script.
Instead, the theoretical displacement ffiffiffiffiffiffiffiffiffiffiffi ffi D Ã dt p is multiplied by a random number ("rand") extracted from a normal distribution of mean μ = 0 and σ = 1. The hop diffusion motion was simulated according to the code used for,  44  kindly provided by Dr. J. Keller-Findeisen as a Matlab script, and transcribed into Python by the authors. First of all, a virtual surface of arbitrary dimension is split into an arbitrary number of compartments as a Voronoi diagram with random seed. To each of the compartments, a unique identifier is assigned. A particle is then generated in the centre of the plane, and its motion is simulated in the same way as the Brownian diffusion case. However, an additional condition is in place to simulate the hopping motion. A probability is assigned to the particle of jumping between one compartment and the other, called hopping probability (HP).
The PSF can either be an experimentally acquired stack or a simulated, for example, using the external DeconvolutionLab2 plugin for FIJI/ImageJ with the microscope's configuration. 46  To consider the boundary effects, we use zero padding. Thus, the simulated volume is MðxÞ ¼ PSFðx, yÞ⊗ ð1 þ SNRÞ X N i¼1 δðx À m i Þ þ BðxÞ " # , ( 19 ) where ⊗ is the convolution product. Finally, i. i. d. Poisson noise is simulated for each pixel separately, using the value of M(x) as the Poisson distribution parameter. The Poisson noise is simulated using the python module random_noise from scikit-image. Thus we obtain M 0 ðxÞ $ Pðk; λðxÞ ¼ MðxÞÞ, (20) where Pðk;λÞ ¼ λe Àλ k! (21) The simulated movie is exported as a tiff stack or an avi file. Figure  3  is a static example of a simulated movie frame using a single simulated track.
Implementation TRAIT2D is implemented entirely in Python using available packages. The software components (simulation, tracking, analysis) have been split into separate modules that have graphical user interfaces (GUIs) available or, in the case of the simulation and analysis libraries, can be imported in Python scripts. TRAIT2D uses an object-oriented approach for its workflows: The simulation library uses Diffusion objects which hold the parameters and mathematical representation of a diffusion model. Specific models inherit from the base Diffusion class. Simulation results are stored in the simulator as a Python dictionary. In the analysis library, tracks are represented as Track objects, which can be created from such a dictionary, a Pandas data frame or a CSV file. Multiple tracks can be pooled in a ListOfTracks object.
Analysis is executed by calling the corresponding methods on these objects and results are stored in them. Central to the analysis library is, of course, the. msd_analysis() and. adc_analysis() methods of the Track and ListOfTracks classes. Through these methods, the user can fit a range of models (included with the library, or custom) to their data. The model fitting itself is ultimately performed using the. curve_fit() function of the scipy. optimize module, which uses a nonlinear least squares algorithm to fit the model function to the data. This function returns the optimal value of the parameters, and the relative covariance matrix. The square root of the diagonal elements of said matrix are thus taken as estimator for the fitting error of each parameter. A unique property of the analysis library is the ability to define and add new diffusion models, through a "singleton" class called ModelDB.
This functionality is only exposed when importing the analysis module from a Python script but not from the GUI. Operation TRAIT2D requires at least a Python 3 (recommended ≥ 3. 7) to run. The software also depends on additional packages: Graphical user interfaces are provided through PyQt (analysis) and Tkinter (simulation, tracking). Scipy, matplotlib and pandas are used for model fitting, plotting and loading data, respectively. These and other dependencies are automatically installed when installing from source using the supplied setup. py file or from the Python Package Index (PyPI).
Workflow The central component of the TRAIT2D software package is the analysis module, through which it is possible to extract physical parameters from the particle trajectories. There are three possible avenues for the user to analyse their data (Figure  1 ). By using the tracker module, the user can import their timelapses, as TIFF stacks, into the GUI and extract particle trajectories, which can then be exported as a formatted CSV file, ready to be imported in the analysis GUI. Alternatively, if the trajectories are already available to the user through a different particle tracking tool, they can be imported as well, on condition that their formatting is compliant with the format required by the analysis module. Finally, the user could also generate a control dataset through the simulator module, either through the GUI or as a script.
The simulated trajectories will then be exported as a properly formatted CSV file, or a compliant data structure in the case of the user script. The Analysis GUI will perform the data analysis following either the MSD or the ADC pipelines (see Methods section), as indicated by the user. We stress that while the GUI exclusively uses the predefined set of models (see Eq. 12, 13 and 14) for the analysis, the module itself allows the addition of more diffusion models as well as batch processing when used in custom scripts, adding an ulterior degree of flexibility.
Compatible file formats and data structures The analysis library and GUI expects track data to be supplied in a specific format although column names and units are adjustable. At the time of the first release, the trajectories can only be imported from CSV files. A track file must include at least three columns, which contain the localisation time t as well as the coordinates x and y. The order in which these columns appear is not important. A unique numerical identifier id must be added as a fourth column in files which contain multiple tracks. This identifier will match each localisation with a specific track. When working with the analysis library as an imported module inside a Python script, tracks can also be directly loaded from a dictionary or a Pandas data frame as long as they contain the keys described above.