dataesr/scientific-paragraphs-categorization huggingface dataset:
The significance of such examination is that the development of a new Qubit is underway within a European Project (SIQUOS). The aim is to develop a scalable technology, which is fully compatible with existing Si-technology platforms, materials, and processes. The targeted Qubit will consist of a MOSFET with a superconducting source and drain. One of the superconducting materials in the study is Si:B, where Si is doped much above the solubility level by methods summarized in the paper. The new program is distributed free of charge from web site https://public. ek-cer. hu/~labar/Strain_4D-ED. htm (accessed on 1 March 2023). Its new lattice fitting approach even facilitates measurement of the position of diffraction spots, which may not be excited in all of the diffraction patterns of the 4D-ED dataset.
Instruments and Materials TEM lamellae were prepared with a ThermoFisher Scios 2 Dual Beam microscope (Eindhoven, The Netherlands) with EasyLift TM nanomanipulator. Experimental data, input to the Strain4DED program were collected in a Titan Themis G2 200 (Thermo Fisher Scientific, Waltham, MA, USA) STEM equipped with X-FEG gun and 4 k × 4 k CETA 16 CMOS camera (Thermo Fisher Scientific, Waltham, MA, USA). The collection of the diffraction patterns for the 4D-ED dataset was controlled in "microprobe STEM mode" by TIA software (FEI, Eindhoven, The Netherlands). This mode provides a nanometer-sized, almost parallel electron beam (convergence angle 0. 24 mrad), scanned over a selected part of the TEM lamella. Although the spatial resolution of the STEM high-angle annular dark-field (HAADF) image is almost an order of magnitude poorer in this mode than in usual STEM, the appearance of the diffraction patterns (that contain small spots in contrast to extended discs) is similar to that of selected area electron diffraction (SAED) patterns.
This fact means that the strain maps produced by the program can have ~1 nm spatial resolution. TEM images were recorded with the same CETA camera as the diffraction patterns. STEM Z-contrast images were recorded with a HAADF detector (E. A. Fischione Instruments, Inc. , Pennsylvania, PA, USA) controlled by Velox software (version 3. 3, FEI, Eindhoven, The Netherlands). The usual dwell time while the beam is in a position that corresponds to a pixel of the HAADF image is 100 ms. Spot size 8 results in strong enough peaks in the diffraction pattern to make processing unambiguous. Even though the peaks may be saturated, their intensity is below the level that could damage the camera. Application of a hybrid pixel detector in other instruments facilitates recording even higher intensities. Pixel size (controlled by binning) and camera length must be selected in a way that the first 2 to 4 orders of diffraction spots extend over the entire camera.
We used only a quarter of the camera with binning 4 (resulting in 256 × 256 pixels for a diffraction pattern). Calibrated camera length appropriate for measurement of Si close to the [110] zone orientation proved to be 262 mm. The size of the area for the 4D-ED experiment is determined by selecting a part of a larger image simultaneously with selecting the number of columns and rows for the STEM. They also yield the size and the spatial resolution of the HAADF image (and the corresponding strain map at the end). A strain map with 200 × 100 pixels (resulting in 20,000 diffraction patterns to process) seems to be satisfactory. It corresponds to about 200 nm × 100 nm mapped area with our spatial resolution. The computer code for the Strain4DED program was IId in MS Visual Basic and runs under the Windows operating system on PCs.
The self-contained executable program can be downloaded free of charge from https://public. ek-cer. hu/~labar/Strain_4D-ED. htm (accessed on 1 March 2023). Instructions for installation are also provided on the same home page. Either the program or the home page does not collect any data from the visitors or users, maximally satisfying GDPR requirements. The sample used here as an example of operation of the program originates from the SIQUOS project (Quant-ERA program Grant Agreement No 101017733 of the EU). The sample in this paper is monocrystalline Si:B layers epitaxied on bulk (100) Si. In order to induce superconductivity, extreme doping levels are needed, more than three times the solubility limit (n solubility ~4 × 10 20 cm -3 ), so that a non-equilibrium technique, such as nanosecond laser doping, is required.
(The significance of that approach is elaborated in relation to Figure  2a. ) The achieved sub-pixel precision ensures the required precise measurement of strain (relative shift in position of lattice points). Since the measurement is relative (to that measured in an unstrained region) any small distortion of the diffraction pattern (that might be caused by the lenses of the TEM) can only cause second-order effect in measured strain. Even this second-order error can be corrected for with one of the methods published by Lábár  [18] [19] [20] ). the nearly parallel (α = 0. 24 mrad) beam from an area of 1 nm diameter (the size of the beam in this mode). A total of 20,000 such patterns form the 4D-ED dataset.
(The significance of that approach is elaborated in relation to Figure  2a. ) The achieved sub-pixel precision ensures the required precise measurement of strain (relative shift in position of lattice points). Since the measurement is relative (to that measured in an unstrained region) any small distortion of the diffraction pattern (that might be caused by the lenses of the TEM) can only cause second-order effect in measured strain. Even this second-order error can be corrected for with one of the methods published by Lábár  [18] [19] [20] ). In our usual workflow, first, we record a TEM image (Figure  1a ) to show an area with good spatial resolution. A bright field (BF) image gives visual enhancement of defects (Figure  1b ). Next switch to "microprobe STEM mode" (select the beam current not to destroy the camera but provide enough count in the diffraction spots, mark the area for data collection) and collect the 4D-ED dataset.
We record a poor resolution HAADF image with this nearly parallel beam (Figure  1c ) simultaneously recording the 4D-ED dataset. This shows accurately which area was used for the data collection. Our usual experimental parameters produce strong enough peaks in the diffraction pattern (Figure  1d ). Even the poorer spatial resolution of the HAADF image is enough to identify the area in the previously recorded good resolution images. For FIB-lamellae the presence of the Pt protecting layer on top of the sample further helps exact identification of the location of the examined area. In CMOS cameras (like ours) the pixel size is controlled by binning. The binning in CMOS cameras is software driven (in contrast to the hardware solutions in CCDs). CMOS cameras can be indirectly coupled (via optical fiber or mirror) or direct electron detection.
A bright field (BF) image gives visual enhancement of defects (Figure  1b ). Next switch to "microprobe STEM mode" (select the beam current not to destroy the camera but provide enough count in the diffraction spots, mark the area for data collection) and collect the 4D-ED dataset. We record a poor resolution HAADF image with this nearly parallel beam (Figure  1c ) simultaneously recording the 4D-ED dataset. This shows accurately which area was used for the data collection. Our usual experimental parameters produce strong enough peaks in the diffraction pattern (Figure  1d ). Even the poorer spatial resolution of the HAADF image is enough to identify the area in the previously recorded good resolution images. For FIB-lamellae the presence of the Pt protecting layer on top of the sample further helps exact identification of the location of the examined area.
Concentration of Substitutional Element Concentration calculation is based on the assumption that the distortion is caused by substitutional element (one element is dissolved in the other where both pure elements have the same crystal structure, in our case virtual cubic B, see below). In such binary solid solutions, Vegard's law can be applied: = • + (1 -) • (4) where is the corresponding lattice constant and is the atomic fraction of the solute element. The matrix is Si and aSi = 0. 5431 nm is taken from XRD database card #04-002-0118. Elementary B is not available in cubic form, however, an Si-1%B is found in XRD database NIMS_MatNavi card #4295421637_1_2 with aSi-1%B = 0. 54166 nm. From this value, a virtual pure B cubic crystal was extrapolated resulting in asolute = aB= 0. 40009 nm. It is between the extreme values for cubic B lattice values reported in the literature (from aB = 0.
Concentration of Substitutional Element Concentration calculation is based on the assumption that the distortion is ca substitutional element (one element is dissolved in the other where both pure e have the same crystal structure, in our case virtual cubic B, see below). In such bina solutions, Vegard's law can be applied: = • + (1 -) • where is the corresponding lattice constant and is the atomic fraction of th element. The matrix is Si and aSi = 0. 5431 nm is taken from XRD database card 0118. Elementary B is not available in cubic form, however, an Si-1%B is found database NIMS_MatNavi card #4295421637_1_2 with aSi-1%B = 0. 54166 nm. From th a virtual pure B cubic crystal was extrapolated resulting in asolute = aB= 0. 40009 nm. tween the extreme values for cubic B lattice values reported in the literature (fr 0.
Concentration of Substitutional Element Concentration calculation is based on the assumption that the distortion is caused by substitutional element (one element is dissolved in the other where both pure elements have the same crystal structure, in our case virtual cubic B, see below). In such binary solid solutions, Vegard's law can be applied: a solution = c•a solute + (1 -c)•a matrix ( 4 ) where a is the corresponding lattice constant and c is the atomic fraction of the solute element. The matrix is Si and a Si = 0. 5431 nm is taken from XRD database card #04-002-0118. Elementary B is not available in cubic form, however, an Si-1%B is found in XRD database NIMS_MatNavi card #4295421637_1_2 with a Si-1%B = 0. 54166 nm. From this value, a virtual pure B cubic crystal was extrapolated resulting in a solute = a B = 0.
Acknowledgments: Microscope facility provided by  VEKOP-2. 3. 3-15-2016-00002project  of ropean Structural and Investment Funds was used for this study. The authors are indebte vente Illés for the preparation of the TEM lamellae. The authors wish to thank  Dominique  for support and discussions. Acknowledgments: Microscope facility provided by  VEKOP-2. 3. 3-15-2016-00002project  of the European Structural and Investment Funds was used for this study. The authors are indebted to  Levente Illés  for the preparation of the TEM lamellae. The authors wish to thank  Dominique Débarre  for support and discussions.
Funding: This work was supported by the  QUANTERA  project  SIQUOS  and by  National R Development, and Innovation Office  under the contract  2019-2. 1. 7-ERA-NET-2022-000QuantERA  II Program has received funding from the  European Union 's  Horizon 2020 resea innovation program  under Grant Agreement No 101017733. Funding: This work was supported by the  QUANTERA  project  SIQUOS  and by  National Research, Development, and Innovation Office  under the contract  2019-2. 1. 7-ERA-NET-2022-00032. The Quan-tERA II Program has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No  101017733.
Funding information Centre Méditerranéen de l'Environnement et de la Biodiversité , Grant/Award Number:  ANR-10-LABX-04-01 ;  Centre National de la Recherche Scientifique , Grant/Award Number:  EC2CO  grant " MULTIVERS ";  Laboratoire d'Excellence TULIP , Grant/Award Number:  ANR-10-LABX-41.
, 2023; Garcia et al. , 2022). Owing to their frequency content, all three events, S1222a, S1094b, and S1000a have been classified as broadband by the MarsQuake Service (MQS)  (Ceylan et al. , 2022; Kawamura et al. , 2022). Figure  1  shows filtered time-domain seismograms of S1222a compared to the two confirmed impacts, arranged by increasing epicentral distance. The signal is shown filtered into two main frequency bands of 0. 05-1 Hz and 1-8 Hz, in order to demonstrate both the comparative low-frequency and high-frequency content of the three events. These bands are chosen as they are close to those used by MQS to classify events on Mars (InSight Marsquake Service, 2023). Spectrograms of these events are also shown in Figure  2. Due to the power constraints faced by the InSight lander during this time, all atmospheric sensors were off at the time of S1222a and hence neither pressure nor wind data exist.
Medium-Resolution Images (1-10 m/Pixel) Near-total coverage of the source region was achieved by multiple medium-resolution instruments (MoRIC, HRSC, and THEMIS in its near-infrared band). Some CTX data in the region of interest was also available. Footprints of these images are all shown in Figure  3. No new or unusual features were found in the visible bands, and no thermal anomalies (e. g. , those associated with a blast zone due to dust removal and/or surface darkening) were identified in the near-infrared. Note. Minimum possible instrument pixel scales are also included-note that all images used in this study may not be at exactly this scale. Data for MOM's MCC (spacecraft no longer operational) and Tianwen's HiRIC were examined, but did not contain any images of the region of interest after the date of S1222a.
X-ray diffraction study A single crystal of compound 3 was analysed by X-ray diffraction at 160(1) K on a Rigaku OD XtaLAB Synergy, Dualflex, Pilatus 200 K diffractometer using a single wavelength X-ray source (Cu K α radiation: λ = 1. 54184 Å) from a micro-focus sealed X-ray tube and an Oxford liquid-nitrogen Cryostream cooler. The selected suitable crystal was mounted using polybutene oil on a flexible loop fixed on a goniometer head and immediately transferred to the diffractometer. Pre-experiment, data collection, data reduction, and analytical absorption correction  [37]  were performed with the program suite CrysAlisPro. [38] Using Olex2,  [39]  the structure was solved with the SHELXT  [40]  small molecule structure solution program and refined with the SHELXL2018/3 program package  [41]  by full-matrix least-squares minimization on F.
2 mg/mL. After 4 h of incubation at 37 °C, the fluorescence signal of resorufin product was measured (λ ex : 540 nm and λ em : 590 nm) in a BioTek®. IC 50 values were then calculated using GraphPad Prism software. Each experiment was performed in duplicate and an average IC 50 value (in μM) was reported with a standard deviation.
Acknowledgements This work was financially supported by an  ERC   Consolidator Grant PhotoMedMet  to  G. G. ( GA 681679 ), by the  ANR  ( COSETTE  project) and has received support under the program Investissements d'Avenir launched by the  French Government  and implemented by the  ANR  with the reference  ANR-10-IDEX-0001-02 PSL  (G. G. ). Y. W. thanks the  China Scholarship Council  for financial support. Financial support by the  Austrian Science Fund (FWF)  is acknowledged (grant number  I5721-N  to T. L. M).
NanoLC-MS/MS analyses of peptides eluted in each fraction were performed using an Ultimate 3000 RSLCnano coupled to a Q-Exactive HF (Thermo Fisher Scientific) using a 240-min gradient. For this purpose, the peptides were sampled on a precolumn (300 μm × 5 mm PepMap C18, Thermo Scientific) and separated in a 75 μm × 250 mm C18 column (Reprosil-Pur 120 C18-AQ, 1. 9 μm, Dr. Maisch). The MS and MS/MS data were acquired by Xcalibur (Thermo Fisher Scientific). Peptides and proteins were identified by Mascot (version 2. 6. 0, Matrix Science) through concomitant searches against the Uniprot database (Homo sapiens taxonomy, October 2019 version), a homemade database containing the sequences of classical contaminant proteins found in proteomic analyses (bovine albumin, keratins, trypsin, etc. ), and the corresponding reversed databases.
Trypsin/P was chosen as the enzyme, and two missed cleavages were allowed. Precursor and fragment mass error tolerances were set, respectively, at 10 ppm and 25 mmu. Peptide modifications allowed during the search were: Carbamidomethyl (C, fixed), Acetyl (Protein N-term, variable) and Oxidation (M, variable). The Proline software  [19]  was used for the compilation, grouping, and filtering of the results (conservation of rank 1 peptides, peptide length ≥ 7 amino acids, peptide-spectrum-match score ≥ 25, false discovery rate of peptidespectrum-match identifications < 1% as calculated on peptide-spectrum-match scores by employing the reverse database strategy, and minimum of one specific peptide per identified protein group). Proline was then used to perform MS1 label-free quantification of the identified protein groups.
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [20]  partner repository with the dataset identifier PXD036883. Statistical analysis was then performed using the ProS-taR software  [21]. Proteins identified in the contaminant database and proteins detected in less than three replicates of one condition were removed. After log2 transformation, abundance values were normalized by median centering, before missing value imputation (slsa algorithm for partially observed values in the condition and DetQuantile algorithm for totally absent values in the condition). Statistical testing was then conducted using limma, whereby differentially expressed proteins were sorted out using a fold change cut-off of 1 and a p value cut-off of 0. 005, leading to a FDR < 1% according to the Benjamini-Hochberg estimator.
There are large individual differences in arithmetic skills. Although a number of brain-wide association studies have attempted to identify the neural correlates of these individual differences, studies have focused on relatively small sample sizes and have yielded inconsistent results. In the current voxel-based morphometry study, we merged six structural imaging datasets of children and adolescents (from 7. 5 to 15 years) whose levels of arithmetic skills were assessed, leading to a combined sample of n = 536. Controlling for individual differences in age, gender, as well as language, and intelligence, we found a unique positive relation between arithmetic skill and gray matter volume in the left inferior frontal gyrus (IFG) and middle temporal gyrus (MTG). Our results suggest that individual differences in arithmetic skills are associated with structural differences in left fronto-temporal areas, rather than in regions of the parietal cortex and hippocampus that are often associated with arithmetic processing.
A recent study investigating associations between GMV and math skills in a relatively large sample of children (n = 224) already suggests that this might be the case. Indeed, after adjusting for total brain volume, the authors found no concurrent associations between GMV and math skills at age 7 and only a unique association with GMV in the left superior temporal cortex at age 13  28. Yet, most children included in that study were born preterm, and even that sample size remains modest for a BWAS  26, 27. To address the power and reliability issue of BWAS of arithmetic skills, we gathered here data from six different datasets  [29] [30] [31] [32] [33] [34]  , leading to a combined sample size of n = 536. Each dataset included structural brain imaging of children or adolescents whose levels of arithmetic skills were measured outside of the scanner (as part of a comprehensive behavioral testing session).
Samples Demographic information about the six datasets included in the present study is shown in Table  1. These datasets are hereafter referred to as set #1  29  , set #2  30  , set #3  31  , set #4  32  , set #5  33  and set #6  34. Participants from set #1 and set #2 come from the Lyon area in France while participants from set #3 to set #6 come from the greater Chicago metropolitan area in the United States (US). Note that different models of scanners were used for data acquisition at these two sites (see "Methods"). Our final sample includes 536 children and adolescents from age 7. 5 to age 15 (mean = 10. 58, SD = 1. 60).
Behavioral results Results of descriptive analyses for all behavioral data, including control variables of sex, scanning site, attention deficit hyperactivity disorder (ADHD) status, and age can be found in Table  2. Because different instruments were used for measuring children's skills in the six datasets (see "Methods"), raw scores were  z-transformed within each dataset and are shown in z-standardized form for the full sample (n = 536) in the table. Separate information for the six datasets with the original mean and standard deviation of the measurements are shown in the supplemental file (see Tables  S1-6 ). Correlations between measures of verbal and non-verbal IQ, vocabulary, reading, and arithmetic skills were medium to large across the whole sample. Scanner site was only associated with age and ADHD status, mainly because (1) sets #3 to #6 were collected on children who were older than in sets #1 and #2 and (2) only sets #3 to #6 included children diagnosed with ADHD.
VBM results For each participant and each dataset, average GMV was extracted from seven regions of interest (ROIs) that have been found associated with mental arithmetic in previous studies (see "Introduction"). All ROIs were defined anatomically to avoid circularity in analyses  37  (see "Methods"). These ROIs were the bilateral IPS, the left AG, the left MTG, the left IFG, and the bilateral hippocampus (see Fig. 1 ). For each ROI, GMV was entered in a linear mixed-effect model to analyze its relation with arithmetic skill, while taking into account the nested structure of the data. Fixed-effects covariates were total intracranial volume (TIV), age, sex, and ADHD. Because sets differed with respect to the scanning site (France versus US) and arithmetic test (WJ-III versus CMAT), these were considered random effects.
However, it is important to acknowledge a number of limitations. First, arithmetic skills were defined based on the calculation subtest of the WJI-III  54  for four of the datasets. Because the test merges different types of arithmetic operations (addition, subtraction, multiplication, division), it is not possible to evaluate whether the link between GMV and arithmetic skills changes with the type of operation. Second, we assessed individual differences in brain structure using VBM. It is possible that associations between arithmetic skills and other brain regions might be found with other types of measures (e. g. , deformationbased morphometry  25  ; surface-based analyses  21  ). In fact, we performed an exploratory analysis of the relation between cortical thickness (CT) and arithmetic skills using the CAT12 toolbox on the same sample of participants (including the same covariates as in our main VBM analysis).
METHODS Sample Across all six datasets (see Table  1 ), exclusion criteria included hearing deficit, magnetic resonance imaging (MRI) contraindication, history of neurological and psychiatric disorders, prematurity less than 36 weeks, and medication affecting central nervous system processing. Participants with a diagnosis of ADHD were only excluded from set #1 and set #2, but not from set #3 to set #6 (see Tables S2 to S7 for proportion). Note that, although set #6 included a relatively high proportion of children and adolescents with a clinical diagnosis of ADHD, participants were instructed to not take stimulant medication for at least 24 h prior to the testing sessions  34. Also note that although set #4 and set #6 originally included 188  32  and 79  34  participants, three participants from set #4 and 11 participants from set #6 had to be excluded from the present analyses because of missing data of interest.