dataesr/scientific-paragraphs-categorization huggingface dataset:
SRCD Measurements The influence of AGuIX ® on the secondary structures of HSA was determined by performing SRCD measurements at pH 7. 4 and 37 • C. The SRCD spectra of pure HSA and HSA mixed with AGuIX ® at 37 • C are reported in Figure  2. The absorption of AGuIX ® at this wavelength range is negligible (Supplementary Figure  S1 ). The spectrum of pure HSA at 37 • C was found similar to the results reported in the PCDDB (Protein Circular Dichroism Data Bank) data base (Reference number PCDDB-CD 0005114000). The two negative peaks observed at 224 and 210 nm correspond respectively to the n → π* and π → π* (parallel) electronic transitions of the peptide bond  [38, 39]. Thanks to the wide spectral range (170-250 nm) available at the DISCO beamline-SOLEIL synchrotron (Gif/Yvette, France), a positive peak was recorded at 192 nm which is associated with the π → π* (perpendicular) transition  [40].
The melting temperature of pure HSA and HSA mixed with AGuIX ® was determined by recording SRCD spectra at temperatures ranging from 23 to 98 • C. The spectra obtained with the ratio 22:1 is presented in Figure  3A. Supplementary Figure  S3A-E  show the spectra obtained with the other ratios. The melting temperatures of the protein mixed or not with AGuIX ® were determined as follows. The intensities of the peaks at 192, 210 and 224 nm were collected and the values obtained at 192 nm are reported in Figure  3B  (the values obtained at 210 and 224 nm are reported in Supplementary Figure  S4A, B ). The relative intensity curves were fitted using the Boltzmann type Equation (1) with the software OriginLab  [32] , y = A + (B -A)/(1 + e (x-x 0 )/dx ) ( 1 ) where y is the relative intensity at the temperature x.
A and B correspond to the maximum and minimum intensities, respectively. x 0 corresponds to the melting temperature of the protein and is the midpoint of the relative intensity curve. dx is the width of the thermal transition. Supplementary Figure  S4 A, B ). The relative intensity curves were fitted using the Boltzmann type Equation (  1 ) with the software OriginLab  [32] , = + B -A / 1 + / ( 1 ) where y is the relative intensity at the temperature x. A and B correspond to the maximum and minimum intensities, respectively. x0 corresponds to the melting temperature of the protein and is the midpoint of the relative intensity curve. dx is the width of the thermal transition. The melting temperature found for pure HSA at 47 µM (3100 µg/mL) is close to 58 • C. This value is in agreement with results reported by Das et al.
[62], T is the absolute temperature (Kelvin) and R is the universal gas constant. K was plotted as a function of 1/T (Supplementary Figure  S7 ). The unfolding enthalpy variation ∆H • and the entropy variation ∆S • of the system were determined (see details in Supplementary Materials) from the slope and intercept (see Table  S2 ) of the Van't Hoff's plot, respectively. The data are presented in the Table  1. This analysis shows that ∆H • and ∆S • values increased up to 10% with AGuIX ® concentrations. This excludes option 2 where a decrease of the entropy variation is expected. Hence, the increase of the enthalpy variation and the entropy variation may be explained only by options 1 or 3. To distinguish between these two remaining mechanisms, isothermal titration calorimetry (ITC) and fluorescence spectroscopy were performed.
Spectra Acquisition The SRCD measurements were performed at the DISCO-beamline, SOLEIL Synchrotron (Saint-Aubin, France). Thanks to the photon flux and beam size at the synchrotron facility  [81] , the measurements were performed with a good signal to noise ratio (200) down to 175 nm, which is difficult to reach with a conventional CD set up  [82]. (+)-camphor-10-sulfonic acid (CSA) was used to calibrate the amplitudes and wavelength positions to perform optimized SRCD experiments. We acquired the spectra of our samples from 250 down to 175 nm every nanometer using a 1 nm bandwidth and 1. 2 s integration time. Raw data (θ) was measured in millidegrees (mdeg), which were converted to molar circular dichroism values (∆ε), after averaging, baseline subtraction and calibration with CSA. For the molar circular dichroism values (∆ε, M -1.
Thermal Denaturation Thermal denaturation experiments were carried out by collecting SRCD spectra at temperatures ranging from 23 to 98 • C using a 3 • C ramp and a 5 min settling time. Three spectra were collected for each temperature. Each measurement lasted for 3 h. In order to assure that over this time no additional phenomena (radiation damage or drying) affected the spectral amplitude, spectra were also recorded for 3 h without changing the temperature. This showed that the protein was not susceptible to radiation damage and that the photon flux and beam size were appropriate (~10 11 photons/s at 2 × 2 mm 2 ). Single value decomposition indicated two principal components (spectra) suggesting a two-state transition. Three separated data collections with fresh sample preparations were carried out for consistency and repeatability.
Spectra Analysis Data treatment including average, baseline subtraction, smoothing, scaling and standardisation were performed with the CDtool software  [83]. Secondary structures content was determined using the ContinLL and SELCON3 program  [83] , SP175 as reference setting and additionally using the BestSel web server  [42]. Normalized root-mean-square deviation (NRMSD) indicated the most accurate fit for each spectrum. Values of <0. 15 were considered significant. Calibrated and normalized spectra were submitted to the publicly accessible PCDDB database  [84]  under reference CD0005114000.
Funding: This research was funded by  Université Paris Saclay  via the " Inititative de Recherche Stratégique" program , grant  IRS   NanoTheRad  project. The research was also funded by the  European Union Seventh Framework Programme  [PEOPLE-2013-ITN- ARGENT  project] under grant agreement n • 608163. It was also supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-05.
Caradot et al. (a)  found that the probability to correctly inspect a pipe in poor condition is close to 80-85% and thus the probability to overestimate the condition of the pipe is close to 15-20% (false negative). Uncertainties in sewer condition assessment are not only due to the inspection procedure but also to undocumented rehabilitation (i. e. the rehabilitation of segments has not been documented in the database) or inspections done for a specific purpose and leading to an incorrect inventory of defects; for example, to identify house connections. Since the definition of rehabilitation strategies is limited by the lack of information about sewer condition and remaining life, deterioration models have been developed to forecast the evolution of the system according to its current and past condition.
Deterioration models can be used (i) to presume the condition class of non-inspected pipes and (ii) to forecast the evolution of the system condition. Such model outputs provide key information to operators and municipalities for the scheduling of inspection programs (i. e. the detection of sewers in critical condition) and the planning of rehabilitation budgets (i. e. the comparison of different sewer rehabilitation scenarios and the evaluation of necessary investment rates). A wide range of modelling approaches have been developed over the past 20 years  (Mashford et   Sewer construction year is generally the key variable for the calibration and prediction of deterioration models. However, for many utilities the construction year of pipes has not been systematically recorded in the database and remains unknown for a large part of the network.
METHODOLOGY Input data The city of Sofia is the capital city of Bulgaria. The urban area has a population of 1. 5 million inhabitants. The sewer network is operated by Sofiyska Voda (SV). It has a length of about 1,729 km and is composed of 49,930 pipes. The system is mainly combined (88% of the network) and most pipes are made of concrete (75% of the network). The construction of the network started at the beginning of the 20th century and evolved slowly until 1950. The expansion of the network accelerated after 1950 to cope with the increasing demography of the city. Most pipe characteristics are available in the GIS database: sewer type, material, shape, length, diameter and depth. Pipe construction year is missing for 30% of the pipes. In 2019, the CCTV database contained 685 inspected pipes for a sewer length of 31.
4 km. Inspected pipes have been selected randomly among the entire network. For the project's scope, pipes with known construction year, material, type and profile were randomly selected in order to get a representative sample of the network. The relevance of having a randomly selected sample of inspection data has already been highlighted by several studies  (Duchesne et   CCTV inspections have been carried out since 2012 and inspection reports are systematically encoded using the European standard EN - (). Structural condition assessment is performed using the French RERAU methodology (Le Gauffre et al. ); the scores range from 1 to 4, with 4 being the worst condition. Inspections with inconsistent defect coding, without age (inspection year or construction year is missing) or that could not be linked to a specific sewer pipe have been discarded from the database.
Simulation module: deterioration and rehabilitation models The simulation module is based on (1) a statistical deterioration model and (2) a rehabilitation model that considers the influence of annual rehabilitation strategies on a series of indicators. Both modules are implemented in the software prototype RELIABLE SEWER. The simulation module predicts the impacts of fixed rehabilitation strategies over a set of network output indicators. These indicators give a strategic overview of the rehabilitation strategy and its financial and technical impact on the network: annual condition distribution, mean age and remaining life of the network; number of annual rehabilitation actions and annual CAPEX and OPEX. The deterioration model relies on the statistical model GompitZ. It is based on the theory of Non-Homogeneous Markov Chains, with transition probabilities derived from the Gompertz distribution (Le Gat ; Caradot et al.
Results indicate that there was no visible evidence of progressive deterioration of the liners: while some defects were noted in the samples or the associated CCTV inspections, it is believed that most of these defects were created at the time of installation and do not represent a degradation of the liner with time. Current datasets do not allow conclusion about the average expected lifetime of CIPP liner and main degradation factors. However, from a series of studies in Germany, France and the USA, it appears that the original design life of 50 years should be met and could potentially be longer (Hoppe ; Allouche et al. ; IKT ). Within the RELIABLE SEWER prototype, survival curves for liners have been defined using the available CCTV data of a German city and the results of an extensive literature review on liner condition and deterioration (EPA ; As for other materials, they follow a Gompertz distribution.
Simulation of rehabilitation scenarios The simulation module has been deployed to simulate the current rehabilitation scenario of Sofiyska Voda (SV) over the next 20 years. This scenario allocates an annual replacement budget of 2 million € and an annual repair budget of 250,000 €. These values have been estimated as the average of the annual CAPEX and OPEX planned for the coming years. In the period 2012-2017, the average annual investment budget for sewer network rehabilitation ranged between 1 and 2 million euros. The business plan for the period 2018-2021 foresees an average annual investment of 2 million euros. Figure  5  shows simulation results for the reference scenario (S1). In 2019, 28% of the pipes are in poor condition and require rehabilitation and the mean age of the network is 36 years (28% also corresponds to the proportion of pipes in poor condition in the inspection dataset, considering that pipes have been randomly selected).
CONCLUSIONS A new simulation module has been proposed to support long-term sewer rehabilitation planning and deployed on the sewer network of Sofia in Bulgaria. The module is based on (1) a statistical deterioration model to simulate the evolution of the network condition and (2) a rehabilitation model to assess the influence of annual rehabilitation strategies on a series of indicators. The modelling outcomes provide tangible proofs of the investments needed for sewer rehabilitation and support the utility in the negotiation with the municipality regarding rehabilitation budgets. Since age is a key variable for sewer deterioration modelling and the information was missing for 30% of the pipes, a new methodology has been proposed to estimate missing construction years in the utility database with a prediction error of less than 7 years.
This methodology could be useful in other cities to support the establishment of an exhaustive sewer database as a starting point for the simulation of asset management strategies. The simulated scenarios in Sofia are relevant to highlight some trends given by the simulation module. The reference scenario quantifies the influence of the actual rehabilitation scenario applied in Sofia over the evolution of the network. It indicates that the current rehabilitation rate (0. 15%) is very low and not sufficient to avoid the deterioration of the network. A massive increase of CAPEX replacement would have a positive impact on the condition, age and rehabilitation rate of the network. However, an increase from 2 million to 20 million € would be necessary to avoid the deterioration and aging of the network and reach stability of the condition distribution.
The numerical optimization of a mechanical part requires a balance between computation time and model accuracy. The work presented herein aims at accelerate global optimization problem by using the framework of Bayesian optimization on a quantity of interest with multiple levels of fidelity. These multi-fidelity data are generated from a quality-driven model-order reduction framework: the LATIN Proper Generalized Decomposition. Within this framework, a reducedorder basis is generated on-the-fly and re-exploited to reduce the computational cost of observations. This strategy is tested on two elasto-viscoplastic test cases: a rocket damper and an aircraft blade and gives significant speedups.
Further developments have already made on this subject  [11, 12, 13]  and have already been applied on structural optimization  [14, 15]. Model reduction techniques constitute an efficient way to generate lowfidelity data by seeking the solution of the problem in a reduced-order basis (ROB), whose dimension is much lower than the original vector space. Using a model-order reduction database as a low-fidelity data is quite recent  [16]  and uses a posteriori approch which usually consists of defining this ROB by the decomposition of the solution of a surrogate model relevant to the initial model. A priori methods like Proper Generalized Decomposition (PGD)  [17, 18]  follow a different path by building progressively an approximation of the solution in a separated-variable form, without assuming any basis.
Modes database is no longer imposed as in Proper Orthogonal Decomposition and the solution of the problem is sought at the same time as the best basis for its representation. The approximation performed is enriched until a given error criterion is reached. We assume that a PGD approximation of order m -1 has already been computed: U(t, M ) ≈ U 0 (t, M ) + m-1 i=1 λ i (t) • Λ i (M ) U c (t,M ) (32) with U 0 the elastic solution such as U 0 ∈ A d and ∀i, (Λ i , λ i ) ∈ U * × I. In the PGD approach, neither functions Λ k nor functions λ k are given initially, and both families are computed on-the-fly. At the global linear stage of the LATIN method, the correction of fields δU, δε, δσ can be find with a separated-variable form: δU = Λ • λ (33) And can be obtained by minimizing the global search direction  (19).
Algorithm 2 summarizes the different steps of the multiparametric strategy implemented. This strategy is tested on the turbine blade test case (see section 3. 4). A 3D solver implementation was developed for the test using MATLAB. To reach fast and parallel computation, LATIN operators are organized as nd-arrays and a parallel implementation of Einstein summation exploits variable broadcasting to deal with time-and spatial-dependent operators. This computational paradigm allows to be as fast as commercial softwares. ω k = φ(d(µ j , µ k )) Field interpolation: aim here is to see if using multi-fidelity bayesian optimisation with the multiparametric strategy is adapted and reduces time computation to obtain a good estimation of the global optimum. U j (t, M ) ← j-1 k=1 ω k U k (t, M ) Generation of the common spatial base Concatenation of spatial modes: [Ξ, Σ, V] Thin SVD ←------[Ξ 1 ,.
Conclusion The aim of this work is to accelerate the global optimization of mechanical structures to allow the use of more complex and accurate models within the design offices. To achieve this goal, the bayesian optimization process has been improved to the use of multi-fidelity data by considering an adapted acquisition function. The surrogate model generation step also uses an improved version of the multi-fidelity kriging method Evofusion which adding an estimated variance on low-fidelity data. The multi-fidelity data are not obtained by calling a given quality model, but by using a solver allowing the computation of mechanical fields with an adapted level of fidelity. The proposed solver is based on the LATIN-PGD framework, which has the triple advantage of a fast computation of the spatio-temporal fields in the PGD separated-variable form, giving an approximation of the complete solution and the amount of interest at each iteration, and allowing to start computation from an approximation interpolated from previous computations with the multiparametric strategy.
In summary, the preliminary results of the clinical trial reported in this paper demonstrate in patients that an intravenous injection of Gd-based nanoparticles is effective for enhancing different types of brain metastases in patients. These first clinical findingspharmacokinetic, passive targeting, and concentration in metastases-are in line with the observations obtained in previous preclinical studies in animal models of brain tumor and bode well for a successful translation of this theranostic agent from the preclinical to the clinical level. In addition to this, the preliminary results of the NanoRad phase 1 clinical trial indicate good tolerance of intravenous injection of AGuIX nanoparticle up to the 100 mg/kg dose selected for this study. All these results and observations make it possible to confidently start a phase 2 clinical trial on the same indication (NANORAD2, NCT03818386).
Study design This study is part of a prospective dose escalation phase I-b clinical trial to evaluate the tolerance of the intravenous administration of radiosensitizing AGuIX nanoparticles in combination with wholebrain RT for the treatment of brain metastases. This investigatordriven trial was sponsored by the Department of Clinical Research and Innovation of Grenoble Alpes University Hospital and performed in the Department of Radiotherapy of Grenoble Alpes University Hospital. Its Data and Safety Monitoring Board is composed of physicians who specialized in RT, oncology, and pharmacology. Approval was obtained from the Agence nationale de sécurité du médicament et des produits de santé (ANSM) (French National Agency for the Safety of Medicines and Health Products; EudraCT number 2015-004259-30) in May 2016.
The NanoRad trial (Radiosensitization of Multiple Brain Metastases Using AGuIX Gadolinium Based Nanoparticles) was registered as NCT02820454. The study began in June 2016 and was completed in February 2019. Here, we report the findings of the MRI protocol applied to the 15 recruited patients. The objectives assigned to this MRI ancillary study were (i) to assess the distribution of AGuIX nanoparticles in brain metastases and surrounding healthy tissues and (ii) to measure the T 1 -weighted contrast enhancement and nanoparticle concentration in brain metastases and surrounding healthy tissues after intravenous administration of AGuIX nanoparticles. Detailed information on the NanoRad trial is available in the paper from Verry et al. (29).
The 3D FLASH sequence is repeated several times with a different flip angle for computing T 1 relaxation times and contrast agent concentration. The SWI sequence is used for detecting the presence of hemorrhages. The FLAIR sequence is applied for monitoring the presence of inflammation or edema. Last, the DWI sequence can be applied for detecting abnormal water diffusion in tissue or brain metastases. The total acquisition time ranged between 30 and 40 min depending on patient-adjusted imaging parameters. The key features and the main acquisition parameters of these imaging sequences are detailed in the Supplementary Materials. Image processing and quantification pipeline MRI analyses were performed using an in-house computer program called MP 3 (https://github. com/nifm-gin/MP3) developed by the GIN Laboratory (Grenoble, France) and running under MATLAB software.
Image analyses include counting and measurements of metastases, quantification of contrast enhancement, relaxation times, and concentration of nanoparticles. Following RECIST and RANO criteria, solely metastases with longest diameter above 1 cm were considered as measureable and were retained in subsequent analyses. The MRI SE, expressed in percentage, was defined as the ratio of the difference between the amplitude of the MRI signal after the administration of the contrast agent and before the administration of the contrast agent over the amplitude of the MRI signal before the administration of the contrast agent, the MRI signal amplitude being measured in the 3D T 1 -weighted image dataset. The T 1 relaxation times were derived from the 3D FLASH images obtained at four different flip angles.
The concentration of nanoparticles in brain metastases was derived from the variations of T 1 relaxation times before and after contrast agent administration and from the known relaxivity of the nanoparticles. The details about the acquisition and the procedure for computing the T 1 values and the concentration are given in the Supplementary Materials. A 3D image rendering was performed using the BrainVISA/ Anatomist software (http://brainvisa. info) developed at NeuroSpin (CEA, Saclay, France). To better visualize the location of the different metastases, the Morphologist pipeline of BrainVISA was used to generate the meshes of both the brain and the head of each patient.
3 µJy/beam, § 2. 1), contiguous continuum map at 1. 2 mm in the HUDF  (González-López et al. 2020; Aravena et al. 2020). Using earlier data from the ASPECS-Pilot program on a smaller area of the sky,  Decarli et al. (2016b)  studied a sample of seven galaxies at z = 1 -3 (a subset of the sources studied in this paper), finding that the CO excitation conditions were overall lower than those typically found in starbursts, SMGs and QSO environments. This paper studies the CO excitation, atomic carbon emission and ISM conditions in a flux-limited sample of 22 CO and/or dust continuum detected galaxies at z = 1 -3 from the ASPECS Large Program (LP), supplemented by follow-up CO(1-0) observations from VLASPECS  (Riechers et al. 2020 ). The paper is organized as follows. We first present the ALMA and VLA observations and the physical properties of the galaxies in the sample ( § 2).