dataesr/scientific-paragraphs-categorization huggingface dataset:
PD patients, candidates to STN-DBS, were included in PREDISTIM 1 , an ongoing prospective, multicentric cohort study sponsored by the University Hospital of Lille, conducted in 17 PD excellence centers from the clinical research network in France (NS-Park/F-Crin), approved by the CPP Nord Ouest-IV Ethical Committee (CPP 2013-A00193-42) and registered in the ClinicalTrials. gov website (NCT02360683). The objective of PREDISTIM is to identify risk factors of disease progression after STN-DBS in PD. Briefly, patients undergoing STN-DBS in one of the participating centers were consecutively included into the study between 11/2013 and 09/2019. Inclusion criteria were a diagnosis of PD according to the UK Brain Bank diagnostic criteria for PD [18], disease duration ³ 5 years, age between 18 and 75 years and indication of STN-DBS.
ACKNOWLEDGMENTS We thank the participants and their families for their cooperation. The authors are grateful for financial support from the  France Parkinson charity, French Ministry of Health  ( National PHRC 2012 ), support from the  French clinical research network NS-Park/F-Crin  and the  Fédération de la Recherche Clinique du CHU de Lille  (with  Alain Duhamel ,  Maeva Kheng ,  Julien Labreuch ,  Dominique Deplanque ,  Edouard Millois ,  Nolwen Dautrevaux ,  Victor Laugeais ,  Maxime Caillier ,  Aymen Aouni ,  Pauline Guyon ,  Francine Niset ,  Valérie Santraine ,  Marie Pleuvret ,  Julie Moutarde  and  Laetitia Thibault ).
Funding: The study was funded by the  France Parkinson charity and French Ministry of Health  ( PHRC National 2012 ) and promoted by  CHU of Lille  (coordinated by  Pr Devos and Pr Corvol)  with the support of the  French network NS-Park. This is an ancillary study to protocol 2013- A00193-42 ; ClinicalTrials. gov: NCT02360683. The research leading to these results has received funding from the program " Investissements d'Avenir "  ANR-10-IAIHU-06.
In addition, two IFM SM70 0 0 electromagnetic flowmeters are positioned at the inlets of the circuits with a measuring precision of 0,3 l/min in our range of use. These measuring instruments are plugged in a TC08 Picotechnology data logger. All signals (flow rates, temperatures) are recorded with a one second sampling period. The analog data are converted into numerical data through a PicoLog 6 logger software. The flowrate variations during the experiments stay below 1. 5%, as can be seen in a typical recording shown in Fig. 2 a. Initially, the two fluids flow through both circuits at a fixed temperature. Flow and temperature measurements start once the heat exchanger is in steady state. The exchanger is activated in transient thermal mode, by increasing the temperature set point of the hot bath.
Structural, morphology and composition properties of 50B24h sample As seen in Fig. 1a , the 50B24h powder is composed of nanowires, cubes and multipods. Meanwhile, the EDX analyses of the 50B24h powder prove that the nanowires, cubes and multipods are HZTO, CaZr y Ti 1Ày O 3 (CZTO) and BCZT phases, respectively (Fig. 1b, c ). The XRD pattern of 50B24h powder is illustrated in Fig. 1g , where the presence of BCZT, HZTO, and CZTO and Ca 3 (Zr y Ti 1Ày ) 2 O 7 (C 3 (ZT) 2 O 7 ) phases is observed. The XRD pattern can be indexed to monoclinic (C2/m), orthorhombic (Pbnm), orthorhombic (Ccm2 1 ) and tetragonal (P4mm) for HZTO, CZTO, C 3 (ZT) 2 O 7 and BCZT phases, respectively. The amounts of BCZT, HZTO, CZTO and C 3 (ZT) 2 O 7 phases were determined using the Rietveld quantification tool in High-Score Plus software and reference structure data from the Crystallography Open Database (COD) 2020 and were found to be 58, 7, 18, and 17%.
Acknowledgements The authors gratefully acknowledge the generous financial support of the  European Union Horizon 2020 Research and Innovation actions MSCA-RISE-ENGIMA  (No. 778072 ) and  MSCA-RISE-MELON  (No. 872631 ). Z. K. , B. R. , D. V. , and M. S. acknowledge the  Slovenian Research Agency  program  P1-0125  and  P2-0091.
Statistical Analysis Results were expressed as mean ± standard error. Gene expression obtained with TaqMan technology was analyzed with DataAssist software (Thermo Fisher Scientific). Otherwise, all statistical analyses were performed using R software (version 3. 6. 1). The normality of the variables was assessed by the Shapiro-Wilk test and their homoscedasticity by Bartlett's test. Comparisons between two groups were made by the Student test or the Mann-Whitney test when normality was rejected. Values with p < 0. 05 were considered significant.
p. m. For each set of measurements, one biosensor exposed to the 0 nM concentration was used as a background reference. Sensorgrams were processed using a single reference subtraction and analyzed using the Octet Data Analysis software 8. 2 (FortéBio). Fitting was performed with a 1:1 binding fit model. Additionally, optimum elution conditions of the affinity ligand were screened by BLI, using biotinylated Nanofitin-coated Streptavidin sensors. For the preparation of the BLI sensors, Streptavidin-coated SAX sensors were conditioned in phosphatebuffered saline (PBS) and loaded with the biotinylated Nanofitin (30 nM in PBS) for ~120-150 s until a layer thickness of 1. 5 nm was reached. Then, after washing with PBS, any residual Streptavidin was quenched by dipping the sensors in 100 mM Tris-buffered 50 mM biotin solution, pH 8.
94% in AC process; (ii) reusability of the column over 35 regeneration cycles keeping capacity above 15 mg/ml; (iii) limited efforts were required to scale-up the manufacturing of the custom AC medium;(iv) finally, both ligand and resin manufacturing are animal-free and have been developed within cGMP requirements. Custom affinity column (up to 0. 5 L) and the developed downstream process were assessed in a technical run within an industrial setup. Process yield has been improved more than five times with increased purity of the final SLO product. Finally, the simplified downstream process was reduced from five to three steps: harvest and clarification, AC and final polishing, and formulation delivering the final product with purity and quality requirements. This study was supported by H2020 EU-funded project "Sustainable downstream processing of vaccines through incorporation of nanobiotechnologies: novel affinity ligands and biomimetic membranes" (Project number 635770).
This paper presents the analysis, design and simulation of DC distributed power supply controlled with power and energy balance control technique. The proposed system consists of 5 connection structures of DC to DC converters which are parallel, cascade, source splitting, load splitting, and stack. The system mathematical model is analyzed by the average smallsignal method. The proposed power balance control technique is used in the instance where the main sources supply the load of the system. The energy balance control technique is used in the case that the load at the 24 V and 12 V bus is backed up with a battery. The DC Distributed system is simulated by MATLAB/Simulink. The system has 1 kW power rating and contains 6 voltage bus with voltage ratings of 380, 100, 60, 48, 24, and 12 V. The simulation results show that, with the proposed power balance design and control technique, the system provides good dynamic responses and stability.
463 The mathematical model of the modules in the system has been derived to simulate and study the dynamic response obtained from MATLAB/Simulink, as well as to analyze the compatibility for connecting the modules in all proposed connection structures in the DC distributed power supply. If 1 = u is the ON state of the switching, 0 = u is the OFF state of the switching, and 1-= uu , the inductor current equation of the flyback converter is: 1 2 1 ( ) ( ) ( )   =-     L in o N i s uV s uV s sL N (6) The output capacitor voltage equation of the flyback converter is: 1 2 1 ( ) ( ) ( )   =-     C L R N V s uI s I s sC N (7) The inductor current equation of the buck converter is:   1 ( ) ( ) ( ) =- L in o I s uV s V s sL (8) The output capacitor voltage equation of the buck converter is:   1 ( ) ( ) ( ) =- C L R V s I s I s sC (9).
RESULTS AND DISCUSSION Figure  7  shows the simulation results of the proposed system. The transient response effectiveness of the system is investigated by MATLAB/Simulink to verify the control algorithm and evaluate the performance of the system, using the calculated specification shown in Table  1. Figure  7  (a) shows the responses of bus voltage and load current at the 380 V, 100 V, 60 V, 48 V, 24 V, and 12 V bus. Figure  7 (b)  shows the state of charge, current, and voltage of the battery. From 0-0. 1s, the system initialization starts and has full load at 100% as designed. At 0. 1s, a sudden step down of the system's load from 100% (1 kW) to 10% (100 W) occurs. At 0. 2s, a sudden step up of the system's load from 10% (100 W) to 100% (1 kW) occurs. Then, at 0. 3s, a sudden cut off of the system main sources occurs, then the bidirectional converter with energy balance control technique connected to the battery maintains the stability of the voltage at the 24 V and 12 V bus.
CONCLUSION This work presents the design and simulation of distributed power supply in DC architecture with power and energy balance control technique for the proposed 1 kW distribution system that contains 6 voltage bus with voltage ratings of 380, 100, 60, 48, 24, and 12 V, consisted of 5 connection structures. The proposed average small-signal analysis and control technique is able to simplify the parameters design of the PI controller and solve the basic control limitations of the DC-to-DC converters. The system has been simulated by MATLAB/Simulink in various scenarios including normal operation with full load, operation with sudden load change including step down from 100% to 10% at 0. 1s and 0. 4s. Step up from 10% to 100% at 0. 2s and 0. 5s. And in the event that the main sources stopped supplying power to the system at 0.
ACKNOWLEDGEMENTS This work was supported in part by an  International Research Partnership "Electrical Engineering   -Thai French Research Center (EE-TFRC )" under the project framework of the  Lorraine Université   d'Excellence  (LUE) in cooperation between  Université de Lorraine  and  King Mongkut's University   of Technology North Bangkok  and in part by the  National Research Council of Thailand (NRCT)  under  Senior Research Scholar Program  under Grant No. N42A640328 , and in part by  National Science, Research and Innovation Fund (NSRF)  under  King Mongkut's University of Technology North Bangkok  under Grant no. KMUTNB-FF-65-20.
Numerical Details Figure  2  shows the schematic of calculation model and mesh structures. The computational domain was divided into hexahedral grids with approximate 750 000 cells. the current simulation are listed in Table  2. The dimension of the SEN is shown in Fig. 3. The CFD code ANSYS-FLUENT was used to solve both the mass and the momentum governing equations of each phase. For the computational inlet, the velocity of the molten steel and argon flow rate was given based on the casting speed and argon flow rate. The volume fraction of the molten steel and argon gas were set at the inlet. The increase in volume during heating of the injected argon gas was considered in the simulation and the expansion coefficient was 6. 05 according to the study of Liu et al. 38)  For the computa-tional outlet, a constant pressure outlet at the bottom of the calculation domain was applied and the normal gradient of all variables was zero.
Two forms of MAF mRNA have been described in humans, the variant MAF-201 referred to as the small transcript and the MAF-202 variant referred to as the long transcript. The small transcript MAF-201 (2 646 bases) was made up of the 2 exons of the MAF gene after splicing of the intron (4 231 bases) which was not spliced in the long MAF-202 transcript (Figure  4A ). The intron retention in MAF-202 has resulted in the introduction of a premature stop codon in the open reading frame leading to the production of a short protein isoform (c-MAF b), whereas the small transcript MAF-201 encoded a longer protein isoform (c-MAF a). The c-MAF a and c-MAF b proteins from these transcripts comprised 403 and 373 amino acids, respectively. A putative third protein and mRNA (referred to as medium variant MAF-203) have been described in the Ensembl database, however, neither the mRNA nor the protein have been reported in experimental data.
The library pools were re-quantified with qPCR and the optimal concentration of the library pools was used to generate the clusters on the surface of a flow cell before sequencing on a Nextseq500 instrument using a High Output sequencing kit (2 x 50 cycles) according to the manufacturer's instructions (Illumina). The quality of the Raw. fastq files was analyzed using the bcl2fastq software (Illumina), with a Q-score greater than 30. Reads mapping to the reference genome (GRCh37/hg19) were performed on quality-checked reads using STAR 2. 4. 1c. The reference annotation used was Ensembl_75. The overlap of reads with annotation features found in the reference gtf file was calculated using featureCount. The output computed for each sample (raw read counts) was then used as the input for Edge R analysis.
The polyclonal rabbit c-MAF Ab (ProteinTech) at 0,7 µg/mL in TBST containing 1% of fat dry milk and the membrane was incubated in this solution for 2 hours at room temperature. The secondary antibody used was a peroxydase conjugated donkey anti-rabbit IgG (H+L) Ab (GE Healthcare) at 0,2 µg/mL in 1% of fat dry milk TBST during one hour at room temperature. The HRP-Rabbit polyclonal to GAPDH (Abcam) in TBST containing 1% of BSA and the membrane was incubated overnight. The PVDF membranes were revealed by chemiluminescence (Immobilon Fort Western HRP substrate, Merck Millipore) by using the ChemiDoc ™ XRS system (Biorad, Quantity One ® Software).
(B) Schematic representation of c-Maf protein isoforms. The scale used is 1 cm for 50 amino acids (AA). The long protein (c-MAF a) is composed of 403 AA and the small protein (c-MAF b) is composed of 373 AA. The entire bZIP (basic leucine zipper) domain is indicated on each isoform within violet, the basic motif of 25 AA, and in orange the leucine zipper of 21 AA. All these data are extracted from databases "Ensembl" (ensemble. org) and "Uniprot" (uniprot. org). (C) Schematic representation of post-translational modifications of the c-MAF proteins, using the long protein as template. Post-translational modification sites are indicated by arrows: the sumoylation sites are in yellow, GSK3 phosphorylation sites are in blue, other phosphorylation sites are in dark blue, and ubiquitination sites are in red.
Finally, the sample was quenched down to room temperature and slowly decompressed. The recovered pellet comprised of a mixture of transparent greenish-yellow crystals with diameters from 0. 005 to 0. 2 mm. The morphology and elemental composition of the samples were examined using a LEO Supra 50 VP (Carl Zeiss) high resolution scanning electron microscope equipped with an energydispersive X-ray detector. The single crystal and powder X-ray diffraction studies were performed using STADIVARI PILATUS100K equipped with a semiconductor detector. The collected patterns were analyzed with WINPLOTR software. The cathodoluminescence (CL) spectra were measured with a custom-made setup equipped with a pulsed "electron gun" and vacuum cryostat (510 -7 mm Hg). The spectra were recorded using a diffraction grating spectrometer (dispersion 5 Å/mm) and synchronous detector at temperatures of 77 and 300 K.
However, for non-stationary applications, such as for transportation, the dynamic behavior of the cell remains an open issue. Several models have been developed in the literature to describe the fuel cell dynamics. Shekhar Das set up a dynamic model  [3]  of the fuel cell FC that includes the double-layer charging effect, which is due to the two charged layers of opposite polarity formed between the membrane and the cathode. These layers behave like supercapacitors and are known as electrochemical double layer. The characteristics of the PEMFC were then modeled in MATLAB/Simulink® incorporating temperature, partial gas pressure, and the effects of the double layer capacitor. Rabbani and Rokni  [4]  proposed a dynamic model suitable to investigate the behavior and transient 1407 response of fuel cell for automotive domain.
The problem of these models is that the Warburg impedance cannot easly be implemented on MATLAB/Simulink  [8]. In addition, identification of parameters is based on electrochemical impedance spectroscopy, which is not always available. Papadopoulos et al. [9]  proposes an improvement of the model proposed in  [10]. The transfer function proposed in  [10]  is adapted to describe the slow dynamics of the PEMFC, however Papadopoulos presented an enhanced model by adding a second order transfer function to describe the transient dynamics of fuel cell. The model was evaluated using experimental results. The selection of an appropriate model for the online parameter identification process, in different applications, especially, automotive ones is crucial because the number of parameters and sensors required, as measurement inputs to the model, has a meaningful impact on the computation time the accuracy of the model, The different models and studies presented in this review and in the literature in general, involve different complex physical phenomena such as transport of water, electrochemical reactions, heat exchange of the PEMFC, ice creation in the cathodic catalytic layer.
Dynamic model In (1) to (  5 ) and (  8 ) define a PMEFC model that is suitable for stationary applications while considering the temperature variations. To use this model in power management applications, the fuel cell's dynamic behavior should be included. In the proposed model, the dynamic behavior is considered using transfer functions. The simplification of the dynamic model of the fuel cell using transfer functions has already been proposed by Papadopoulos in  [9]. His model is given by  (9). 𝑉 𝑐𝑒𝑙𝑙 = 𝐸 nerst -𝑉 𝑜ℎ𝑚 -𝑉 𝑎𝑐𝑡 -𝑉 𝑐𝑜𝑛𝑐 -λ 𝑒 𝜏 𝑒 𝑠 𝜏 𝑒 𝑠+1 𝑑 𝑎𝑠 2 +𝑏𝑠+1 (9) In this model, the dynamic behavior is considered as a voltage that was subtracted from the electrochemical equations. The author has carried out the parameters identification using MATLAB/Simulink estimation toolbox and then by tuning manually these parameters following a guideline that it has established.
IDENTIFICATION METHOD USING THE PSO (PARTICLE SWARM OPTIMIZATION) ALGORITHM The parameters identification is done using particle swarm optimization algorithm. The implementation of the algorithm has been implemented on Matlab. PSO is a stochastic optimization technique that imitates the social behavior of a swarm of animals, such as birds and fish. By initializing the PSO with a random population and an iterative procedure based on movement and intelligence processes in a scalable system, the algorithm succeeds in finding an optimal global solution  [19]. Several methods have been used for fuel cell parameter identification in the literature, such as the genetic algorithm  [20] -  [21] , the teaching-learning based optimization algorithm  [22]  and quantum-based optimization algorithm  [11] ,  [19] ,  [23] -  [25].
According to the manufacturer's datasheet, the voltage range is 18V-28V, the maximum current is 65A and the maximum supported current variation is 15A/s. The ambient operating temperature ranges is from 5 °C to 45 °C. The photograph of experimental setup and the synoptic of the test bench are presented in Figure  4. Measurements of current, output voltage and fuel cell temperature were captured using a control unit that recorded the data with a sampling frequency of 1 Hz and 10 Hz, while safety checks and data display were done via a dedicated HMI. A programmable electronic load was used to sink several current values. The output voltage was recorded using a voltage divider bridge with a factor of 0. 23, and the current signal was captured by a current clamp. Temperature was measured at the output of the controller using three thermocouples at different points of the fuel cell system.
Model validation Once all fuel cell's parameters have been identified, the validation of the complete model was realized based on experimental data from different tests carried out on the test bench. Two current profiles were applied to Aircell 1 kW fuel cell system. First, the current profil employed for validation is shown in Figure  6(a). This profil was obtained by applying a current profile starting with the current limit of the fuel cell, which is 62 A, and decreases by steps of 5A every 5 min. Once the 0 A value is attained, the current increases in steps and in the same way, up to 62 A the maximum allowable current for this fuel cell. The acquisition frequency has been 1414 lowered to 1 Hz , since this frequency still allows to have a proper data base and this is a validation test, so there is no need for a very large database to evaluate the model.
Characterization methods The structure of the organo-metal lead perovskite films was characterized by a PANanalytical X-Pert high-resolution X-ray diffractometer (XRD) operated at 40 kV and 45 mA and using the CuKα radiation with λ = 1. 5406 Å. The morphology of perovskite thin films was measured using a field-emission SEM equipment (Zeiss Supra 40) in the in-lens mode. Glow-Discharge Optical Emission Spectrometry (GD-OES) analyses were performed using a HORIBA Jobin Yvon GD Profiler 2 with Quantum Software. The instrument configuration included a RFgenerator (at 13. 56 MHz), a standard HORIBA Jobin Yvon glow discharge source with a cylindrical anode of 4 mm internal diameter and two optical spectrometers (a polychromator and a monochromator) for fast-optical detection. No loss in time resolution was ensured thanks to the detection system, composed of independent photomultiplicators (PM), which guarantee simultaneous detection of the signals corresponding to multiple wavelength with spectral resolution of 12 pm.
The precursor or perovskite layer was mounted on an O-ring at one side of the plasma chamber and used as a cathode. The total emission intensity depends on the erosion rate and emission yield. The data treatment and analysis were made in the hypothesis of a constant emission yield. Taking into account that the erosion rate can vary with chemical composition and microstructure inside the layer and between the layers, all the signals were normalized by the total emitted light. Only the normalized signals are shown and discussed in the paper. The J-V curves were recorded by a Keithley 2410 digital sourcemeter, using a 0. 1 V. s -1 voltage scan rate. The solar cells were illuminated with a solar simulator (Abet Technology Sun 2000) filtered to mimic AM 1. 5 G conditions (100 mW/cm 2 ). The illuminated surface was delimited by a black mask with an aperture diameter of 3 mm.
Acknowledgements D. Z. acknowledges the  CSC-Paristech program  for  Ph. D scholarship funding  (grant number  201806310126 ). The  National Natural Science Foundation of China  is thanked for the funding support No. :  22209117. The  Agence Nationale de la Recherche (ANR)  is acknowledged for financial support via the  Moreless  project  ANR-18-CE05-0026  and the  ChemSta  project  ANR-21-CE05-0022.