dataesr/scientific-paragraphs-categorization huggingface dataset:
Several trials with stage IV malignancies found 22. 5 Mio IU as recommended L19-IL2 dose as monotherapy  [32, 43, 44]  or combined with dacarbazine  [43, 44]. We recently received, based on personal communication, the safety results of L19-IL2 in the phase I trial (NCT02086721) given 15 Mio IU after SABR, as the combination of 22. 5 Mio IU L19-IL2 with SABR resulted in more grade 3 toxicity. Therefore, the 15 Mio IU will be used in the current phase II trial protocol: ImmunoSABR (NCT03705403), designed to test the activity and efficacy of L19-IL2 (+ ICI treatment if SOC) following SABR in oligo-metastatic and conventional radiotherapy in poly-metastatic (maximum 10 metastatic sites) in NSCLC patients. A maximum of 10 lesions was defined, as patients without widespread metastases have low or acceptable toxicity when irradiating maximum 5 out of 10 lesions, and still have an active immune system to gain the best response on L19-IL2.
Study population The trial will consist of one cohort of 126 adult patients with stage IV NSCLC. ImmunoSABR will act as 1st, 2nd or maximum 3rd line treatment, and will be different for patients with oligo-metastatic NSCLC (max 5 metastases  [11] ) and patients with poly-metastatic NSCLC (6-10 metastases). As SOC and OS differ between oligo-metastatic and poly-metastatic patients  [7] , oligo versus poly will be used as a stratification factor for randomisation. Also, ICI maintenance treatment, centre, histology (squamous versus non-squamous), gender and driver mutation (equally divide previously TKI treated patients with oncogenic drivers) will be added as stratification factors. Patients will be randomised into 2 arms, using randomisation by minimisation (ALEA software): E-arm or C-arm. The algorithm uses a random factor to prevent the possibility of upfront knowledge about the randomisation result.
Immune-related toxicity from standard of care pembrolizumab is rare, but can be serious and life-threatening  [63]. Knowing that metastatic NSCLC is a mortal disease in the short-term, the potential burden seems proportional to the potential gain. An independent Data Safety Monitoring Board (DSMB) has been established and will monitor patient recruitment, adverse effects reporting and data quality during the trial. A first safety analysis is planned after the first 15 patients treated with triple therapy. All Serious Adverse Events and Suspected Unexpected Serious Adverse Reactions (SUSAR) will be reported to the manufacture and following country specific and European Medicine Agency (EMA) guidelines.
Radiomics is one of the most promising techniques that have the potential to improve cancer prognostication  [65, 67]. In Radiomics, large numbers (1500+) of quantitative features such as tumour image intensity, (multi-scale) texture and the shape and size of a tumour are extracted from standard medical images (CT, PET, MRI) using (semi)automatic software. Radiomics enables identification of quantitative imaging biomarkers (QIBs) to quantify and classify tumour phenotypes and other disease parameters non-invasively. An exploratory correlative analysis will be conducted on the available PET-CT / diagnostic and simulation CT / MRI data obtained prior and during this trial. All the scans will be deidentified and collected every year and at the end of the trial  [74]. We have three main hypotheses: (A) Studies suggest that the mutational landscape in tumours influences the response to immunotherapy, as higher non-synonymous mutation burden in tumours was associated with an improved objective response, durable clinical benefit and PFS  [46, 47].
Acknowledgements The authors would like to thank the other members of the ImmunoSABR team:  Floor Franssen ,  Natascha Thieme ,  Rianne Herben ,  Simone Moorman ,  Mieke Denys ,  Vanessa Parrein  and  Serena Bettarini , and acknowledge support from  Janita van Timmeren ,  Evelyn de Jong  and  Cecile Wolfs.
Funding The authors would like to acknowledge financial support from the  European Program H2020  (ImmunoSABR -n°733008). Authors also acknowledge financial support from  ERC  advanced grant (ERC-ADG-2015, n°694812 -Hypoximmuno). None of these funding bodies have and had influence on this study, protocol or manuscript. Their role was to make this one of a kind multicentric, investigator initiated, phase II trial possible. 1 The  D-Lab and The M-Lab ,  Department of Precision Medicine, GROW -School for Oncology and Developmental Biology, Maastricht University, Maastricht , The Netherlands. 2  Department of Radiation Oncology (MAASTRO) ,  GROW -School for Oncology and Developmental Biology, Maastricht University Medical Center , Maastricht, The Netherlands. 3  Department of Radiotherapy and Radiation Oncology, Faculty of Medicine  and  University Hospital Carl Gustav Carus at Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany.
The nal diagnosis of glioma recurrence/progression at 6 month or for further assessment of survival was blinded from the 18 F-FDopa PET results and based on current guidelines for which a recurrence/progression is the result of any new tumor brain lesion at MRI and/or clear increase in tumor size or in contrast enhancement, and/or signi cant clinical deterioration, with all of these criteria not being attributable to other non-tumor causes and not due to steroid decrease  [12] [13] [14]. Patients with lesions which were classi ed as therapy-related changes due to the course of the disease did not receive any additional therapy beyond the standard therapy due to suspected recurrence. Neuropathological data were additionally used for this diagnosis in 4 patients having undergone stereotactic biopsy or open resection.
Analyses of PET images Regions of interest (ROIs) were placed on the static PET images using a dedicated software (DOSIsoft, Cachan, France). A spherical ROI of 2 cm diameter, centered on the maximum lesion uptake, was used for determining maximum and mean Standardized Uptake Values (SUV max and SUV mean , respectively). Tumor-to-striatum (TSR) and tumor-to-normal-brain (TBR) ratios were computed as SUV mean or SUV max of the lesion uptake divided by the SUV mean of the striatum (TSR mean and TSR max ) or of normal brain (TBR mean and TBR max ). The SUV mean from the striatum was obtained from the contralateral basal ganglia, which was segmented using a threshold of 65% of maximal uptake, while the normal reference brain SUV mean was obtained with a crescent shape ROI (2 × 8 cm) positioned on the semi oval center of the unaffected contralateral hemisphere, including white and grey matter  [2].
When no abnormal 18 F-FDopa uptake was detected, the ROIs of the potential residual tumor were placed at the site of maximal MRI abnormalities with a fused display of PET and Fluid Attenuation Inversion Recovery (FLAIR) MRI images  [5]. As previously described  [16] , the metabolic tumor volume (MTV) was obtained through a 3D autocontouring process with a threshold corresponding to the SUV mean of the contralateral striatum. In addition, time-activity curves, representing the evolution of the TBR mean as a function of time (TAC ratio ), were extracted with the PLANET® Dose software (DOSIsoft, Cachan, France) and with the ROIs previously placed on static images (see above). Each dynamic frame was previously registered on the CT images, in order to take into account potential patient movements during acquisitions  [17].
For this purpose, survival probabilities were calculated according to the Kaplan-Meier method, with the log-rank test used to compare survival curves. Analyses were performed with SPSS (SPSS Statistics for Windows, Version 20. 0. Armonk, NY: IBM Corp). P-values obtained in univariate analysis as well as in survival analysis were adjusted using Benjamini-Hochberg correction in order to reduce the risk of false discovery  [19].
Patient characteristics and follow-up data The study population included 51 patients with a median age of 50. 8 [44. 4-59. 0] years, 23 of whom were women. According to the 2016 WHO classi cation for gliomas  [20] , 8 gliomas (16%) had been initially classi ed as IDH-mutant astrocytomas (including 2 with anaplastic component), 6 (12%) as IDH-wildtype astrocytomas (including 2 with anaplastic component), 12 (24%) as IDH-mutant and 1p/19q co-deleted oligodendrogliomas (including 4 with anaplastic component), 22 (43%) as IDH-wildtype glioblastomas (GBM) and 3 (6%) as IDH-mutant GBM. Median delay times from the date of surgery to the 18 F-FDopa PET exam and from the date of the non-contributive MRI to the PET exam were 12. 7 [5. 9-23. 5] months and 16  days, respectively. After the subsequent 6-month follow-up period, 34 patients were ultimately considered as having a recurrent or progressive glioma  (.
We introduce and describe the multiconfigurational time-depenent Hartree for indistinguishable particles (MCTDH-X) software, which is hosted, documented, and distributed at http://ultracold. org. This powerful tool allows the investigation of ground state properties with time-independent Hamiltonians, and dynamics of interacting quantum many-body systems in different spatial dimensions. The MCTDH-X software is a set of programs and scripts to compute, analyze, and visualize solutions for the time-dependent and time-independent many-body Schrödinger equation for indistinguishable quantum particles. As the MCTDH-X software represents a general solver for the Schrödinger equation, it is applicable to a wide range of problems in the fields of atomic, optical, molecular physics as well as condensed matter systems.
In particular, it can be used to study light-matter interactions, correlated dynamics of electrons in solid states, as well as some aspects related to quantum information and computing. The MCTDH-X software solves a set of non-linear coupled working equations based on the application of the variational principle to the Schrödinger equation. These equations are obtained by using an ansatz for the many-body wavefunction that is a time-dependent expansion in a set of time-dependent, fully symmetrized bosonic (X=B) or fully anti-symmetrized fermionic (X=F) many-body basis states. It is the time-dependence of the basis set, that enables MCTDH-X to deal with quantum dynamics at a superior accuracy as compared to, for instance, exact diagonalization approaches with a static basis, where the number of basis states necessary to capture the dynamics of the wavefunction typically grows rapidly with time.
We provide a complete set of input files and scripts to redo all computations in this paper at http://ultracold. org/data/tutorial_input_files. zip, accompanied by tutorial videos at https://www. youtube. com/playlist?list=PLJIFUqmSeGBKxmLcCuk6dpILnni_uIFGu. Our tutorial should guide the potential users to apply the MCTDH-X software also to more complex systems.
Below, we provide a tutorial-type introduction to the MCTDH-X software with a focus on simplicity and instructiveness. The MCTDH-X software, however, can do way more than the examples we introduce below. The MCTDH-X software can deal with indistinguishable particles with internal degrees of freedom like spin  [38] , indistinguishable particles placed in a high-finesse optical cavity  [54, [57] [58] [59] [60] , indistinguishable particles with long-range dipolar interactions  [55, 56, 61, 62]  and Hubbard (lattice) models  [38]. Moreover, the MCTDH-X software provides the possibility for an in-depth analysis of the computed solutions of the TISE and TDSE via full distribution functions  [56] , variances and quantum fluctuations  [51, 52, 56]  of observables and correlation functions  [2, 51, 56, 63] ; the MCTDH-X has been benchmarked against exact results  [1, 2] , verified against experimental predictions  [52]  and was recently reviewed in Ref.
II. STRUCTURE OF THE MCTDH-X SOFTWARE A. Objective and main functionality The objective of the MCTDH-X software is to numerically solve the TISE or TDSE for a given many-body Hamiltonian, which describes N interacting, indistinguishable bosons or fermions subject to a confining potential and to analyze the computed solutions. A general Hamiltonian has the form Ĥ = N i=1 - 1 2 ∂ 2 xi + V (x i ; t) + N i<j W (x i , x j ; t), (1) that can be either explicitly dependent on time or not. Here, x i is the coordinate of the i-th particle, -1 2 ∂ 2 x is the kinetic energy operator, V (x; t) is a general, possibly time-dependent, one-body potential and W (x, x ; t) is a general, possibly time-dependent interparticle interaction operator. All the quantities are given in dimensionless units. The length scale L can be chosen to appropriately represent the physical problem; the corresponding time and energy scales are then determined as mL 2 / and 2 /(mL 2 ), respectively.
The details about these equations of motion and their derivation can be found, for instance, in Refs. [3, 4, 26, 27]. The MCTDH-X software hosted at http://ultracold. org solves these equations of motion using the so-called constant mean-field integration scheme (see, for instance, Refs. [21, 26] ). The constant mean-field scheme features an adaptive time step for which the coefficients and the orbital's equations of motion are decoupled. We note that the equation for the orbitals contains the inverse of the matrix elements of the one-body density matrix. In cases where the reduced one-body density matrix has zero eigenvalues and is not invertible, the orbital equations are therefore undefined and problematic. In almost all practical cases and, particularly, for the computations we present below, the regularization strategy documented in Ref.
B. Quantities of interest Once the coefficients C n (t) and the orbitals φ k (x, t) are computed, the MCTDH-X software can analyze the solution and calculate several quantities of interest. These include, respectively, the real-space and momentum-space density distributions ρ(x) = Ψ| Ψ † (x) Ψ(x)|Ψ /N, (5) ρ (k) = Ψ| Ψ † (k) Ψ(k)|Ψ /N, (6) the Glauber one-body and two-body correlation functions, g (1) (x, x ) = Ψ| Ψ † (x) Ψ(x )|Ψ N ρ(x)ρ(x ) , (7) g (2) (x, x ) = Ψ| Ψ † (x) Ψ † (x ) Ψ(x ) Ψ(x)|Ψ N 2 ρ(x)ρ(x ) , (8) and the natural orbitals φ (NO) i and the orbital occupations ρ i , which, respectively, are the eigenfunctions and eigenvalues of the reduced one-body density matrix, ρ (1) (x, x ) = 1 N Ψ| Ψ † (x ) Ψ(x)|Ψ = i ρ i φ (NO), * i (x )φ (NO) i (x). (9) The natural orbitals are ranked in the order that the first orbital has the highest occupation while the last orbital has the lowest ρ 1 ≥ • • • ≥ ρ M.
C. Workflow The MCTDH-X software mainly consists of two programs: 1. The main program, MCTDHX, computes the numerical solution of the TISE or TDSE. 2. The analysis program, MCTDHX analysis, analyzes the found solution. Here and in the following, we use the verbatim font to refer to code, including executable commands, files, statements, and variables. To set up a numerical task, the user modifies and chooses the parameters via the text input file MCTDHX. inp. A detailed description of the available options is given in the manual  [74]  of the MCTDH-X software. The file Get 1bodyPotential. F is used to specify custom one-body potentials [V (x i ; t) in Eq. (  1 )], while the file Get Interparticle. F allows for custom two-body potentials [W (x i , x j ; t) in Eq. (  1 )]. For custom (initial) states, the Get Initial Coefficients.
F and Get Initial Orbitals. F files can be used to specify the (initial) coefficients C n (t = t 0 ) [cf. C n (t) in Eq. (  4 )] and orbitals {φ k (x; t = t 0 ), k = 1,. , M } [cf. φ j (x; t) in Eq. (  4 )], respectively. The workflow and structure of the MCTDH-X software follows naturally from its main objectives to determine a numerical solution to the TISE or the TDSE and then to extract desired quantities of interest from the solution. This workflow can be summarized in the following steps, which are also visualized in Fig. 1:  1. Determine the initial state. (a) Default: compute the ground state of some Hamiltonian Ĥ by running MCTDHX and configuring the numerical task ("relaxation mode", Hamiltonian, integration procedure etc. ) in the input file MCTDHX. inp. (b) Advanced: manually set the coefficients and orbitals that determine the initial wavefunction via the files Get Initial Coefficients.
F and Get Initial Orbitals. F The scripts in the MCTDH-X software generally fall into two categories: either scripts to automate computations, i. e. parameter scans or scripts to visualize data in plots and videos. A series of tutorial videos illustrating the workflow step-by-step is available at https://www. youtube. com/playlist?list=PLJIFUqmSeGBKxmLcCuk6dpILnni_uIFGu. A discussion on the convergence, particularly in orbital number, can be found in the supplementary material  [75]  (including Refs. [76] [77] [78] [79] [80] [81] ). For support and documentation, the website of the MCTDH-X software, http://ultracold. org [37] , the MCTDH-X manual  [74]  and the email address mctdhx@ultracold. org are available. Feature requests should be directed towards the support email address and/or be discussed on the web forum.
< l a t e x i t s h a 1 _ b a s e 6 4 = " Z Q S 3 q D H j f S e a 9 S l J z r k D x j d m 9 9  use the MCTDH-X software to reveal the static and dynamical properties of the building blocks of lattice systems, i. e. periodic structures of potential wells. Such periodic structures are particularly interesting since they are commonly seen in nature and also synthesized in laboratories. Cold atom systems serve as one of such convenient platforms for the observation of quantum phase transitions  [85] , since ultracold gases offer an astonishing flexibility -for instance, the lattice depth and the inter-particle interaction strength can be varied almost at will. Choosing a minimal example like the double well reduces the computational difficulty and improves the clarity of our presentation. Nevetherless, some phenomena emerging in large lattices cannot be captured correctly, like for instance the Peierls transition, the SSH model  [86, 87]  and more.
If the number of orbitals M is smaller than the number of lattice sites, two lattice sites have to "share" the same orbital, the emergence of Mott-type correlations between these two sites thus cannot be captured correctly. With a sufficient number of orbitals, the coherence and incoherence between sites are accessible through quantities like the correlation functions [Eqs. (7) ] or the reduced density matrix and its eigenvalues, which are straightforwardly available in simulations with the MCTDH-X software. Even though the Bose-Hubbard model is a successful model, it is quite simplistic and cannot capture all the rich physics that might emerge. MCTDH-X is able to capture the physics beyond the Bose-Hubbard physics, by virtue of the used general basis set which is time-dependent, variationally-optimized and not necessarily localized at sites.
II B. All input files and scripts of this section are hosted at http://ultracold. org/data/tutorial_input_files. zip and a detailed description on how to do the computations is given in the supplementary material  [75]  and a series of tutorial videos at https://www. youtube. com/playlist?list=PLJIFUqmSeGBKxmLcCuk6dpILnni_uIFGu.
C. Conclusion and Discussions In this work, we have described and demonstrated the workflow, usage and structure of the MCTDH-X software hosted, documented and distributed through http://ultracold. org. In a step-by-step tutorial, we show how to examine particle correlations and fluctuations using the MCTDH-X software. We exhibited applications of MCTDH-X to solutions of the time-dependent and time-independent many-body Schrödinger equation for systems of bosons and fermions in a double-well potential. We have computed the many-body wavefunction and calculated various quantities of interest, including correlations, real-and momentum-space density distributions and single-shot experiments. We highlight that our paper demonstrates the first application of MCTDH-X to obtain single shot images for fermions, to analyze correlations via single shot counting statistics, to investigate the dynamical behavior of the correlation order parameter, and to directly compare the dynamical behavior of bosons and fermions using single-shot images and the correlation order parameter.
Before features extraction (couple of m/z values and retention time), the spectral background noise was removed. The extracted features were aligned with an in-house R script with a maximum m/z tolerance of 3 ppm and retention time tolerance of 0. 5 min and variables absent from more than 80% of the samples were removed from the analysis. Features fragmentation was performed using the Scheduled Precursors List MS/MS function. The fragmentation was performed at two different collision energies: 20 and 35 eV. Parent ions and fragments were submitted to different databases through the massTRIX interface (http://masstrix. org) (Suhre & Schmitt-Kopplin, 2008)  and YMDB 2. 0 (http://www. ymdb. ca) and both compositional (obtained isotopic profile) and structural information were used to annotate compounds with high confidence level.
Data Analysis All experiments were performed at least in triplicate and if not specified, results were expressed as average ± standard deviation for the triplicate. The basic data mining and data visualization were performed with R software v. 3. 5. 1. Nonparametric Kruskal-Wallis rank sum tests and Spearman tests were used for median comparison and correlation estimation, respectively. Curve fitting was performed with OriginPro 2017 (b. 9. 4. 0. 220). After alignment, features that were positively correlated (Spearman correlation, ρ > 0. 1) with the addition of the quinone were extracted. They were considered to be associated with compounds which had reacted with quinones. The nucleophile-quinone derivative is the result of the combination of n*4MeC (1 ≤ n) with m*nucleophiles (1 ≤ m ≤ n+2) leading to m*Nu + n*4MeC addition products  (Ma et al.
, 2019). Thus, a specific m*Nu + n*4MeC derivative carrying the m/z information of the corresponding free nucleophile and the combination of all the derivatives will be used to further express the nucleophilic fingerprints of the YDs. The SmartFormula tool from the DataAnalysis software  (v. 4. 3, Bruker, Germany)  allows attribution of a raw formula based on the detected m/z and the isotopic profile (Supplementary information 5). Since sulfites were used to quench the derivatization reaction, nucleophilic addition of x*HSO3 (0 ≤ x) on 4MeC moiety can be observed to form the nucleophilic addition product m*Nu + n*4MeC + x*HSO3. The putative raw formula (and thus the corresponding neutral mass) of the free nucleophiles was calculated by subtracting the raw formula of n*4MeC + x*HSO3 -2*(n + x + m -1)*H from the total raw formula of the quinone derivative.
8). In addition to the nucleophilic fingerprint, the high resolution of the UHPLC-Q-ToF-MS has been used to putatively annotate the nucleophiles. Based on the derivatization method, the nucleophilic addition reaction could occur between one or several nucleophiles with one or two electrophilic sites of the 4MeQ  (Ma et al. , 2019; Nikolantonaki et al. , 2012 Nikolantonaki et al. , , 2014;; Nikolantonaki & Waterhouse, 2012; Romanet et al. , 2020). Table  1  summarizes the putative annotations of the quinone derivatives detected in negative ionization mode, which were positively correlated to the addition of 4MeQ. Almost 60% (26/44) of the nucleophiles detected were not found in online databases, whatever the combination of 4MeC or HSO3-4MeC used. Of the 41 attributed elemental formulas, 21 could be putatively assigned to sulfur containing compounds.