dataesr/scientific-paragraphs-categorization huggingface dataset:
PET data acquisitions commenced 50 min after injection of a mean of 4 MBq/kg weight of [ 18 F]-Florbetapir. Radiochemical purity of [ 18 F]-Florbetapir was superior to 99. 5%. Regional standard uptake value ratios (SUVRs) were generated from semi-automated quantitative analysis with the whole cerebellum used as the reference region. Cortical-to-cerebellar SUVRs (cortical-SUVRs) were obtained using the mean signal of the predefined cortical regions: frontal, temporal, parietal, precuneus, anterior cingulate and posterior cingulate as described previously  [34]. Other brain regions independently assessed were the anterior cingulate, anterior putamen, caudate, hippocampus, medial orbitofrontal cortex, occipital cortex, parietal cortex, pons, posterior cingulate, posterior putamen, precuneus, semioval centre and temporal cortex.
To explore the role of apolipoprotein E (ApoE) ε4 genotype, the major risk factor for sporadic SD  [40] , on the association between 25(OH)D and cortical Aβ (both as continuous variables) we ran a linear regression analysis with the introduction of an interaction term between ApoE ε4 genotype and 25(OH)D. There was no correction for multiple comparisons: p < 0. 05 was considered statistically significant. Statistical analyses were performed using SAS software version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
In addition, we considered several cofounders, including a possible interaction between vitamin D and ApoE ε4 status. The limitations of our study included the cross-sectional design, which precluded the examination of the relationship between plasma 25(OH)D and change in cerebral Aβ load over time. PET scans were also performed throughout the 3-year period of the MAPT; therefore the study design was not truly cross-sectional. The inclusion of the time interval between baseline 25(OH)D measurements and the PET scan as a confounder in the linear regression models probably served to mitigate this bias. Furthermore, we only had data availability for vitamin D status in later life which might perhaps not be the optimal time window for studying the associations between vitamin D and cerebral Aβ.
Funding The MAPT was supported by grants from the  Gérontopôle of Toulouse , the  French Ministry of Health  ( PHRC 2008 (PHRC , 2009)),  Pierre Fabre Research Institute  (manufacturer of the omega-3 supplement),  Exhonit Therapeutics SA, Avid Radiopharmaceuticals Inc. and in part by a  "Investissements d'Avenir" grant  from the  French National Agency for Research  (n°ANR- 11-LABX-0018-01 ). The promotion of this study was supported by the  University Hospital Center of Toulouse. The data-sharing activity was supported by the  Association Monegasque pour la Recherche sur la maladie d'Alzheimer (AMPA)  and the  UMR 1027 Unit INSERM-University of Toulouse III. The sponsors had no role.
Viruses All experiments with replication competent EBOV and MARV were performed in the biosafety level 4 (BSL-4) laboratory at the Texas Biomedical Research Institute (San Antonio, TX). The wild-type EBOV variant Mayinga (NCBI accession number NC_002549) and MARV strain Musoke (NCBI accession number NC_001608) were obtained from the virus repository at the Texas Biomedical Research Institute, San Antonio, TX. The recombinant EBOV variant Mayinga expressing enhanced GFP (EBOV-eGFP) was kindly provided by Heinz Feldmann (NIH, Hamilton, MT). The viruses were amplified in Vero cells in DMEM containing 2% FBS for 7 days. The culture supernatants containing the viruses were clarified of cell debris, overlaid over a 20% sucrose cushion in PBS, and centrifuged at 4 °C at 28,000 rpms for 2 h. The pellets were resuspended in PBS.
The titer of EBOV-eGFP stock was determined by incubating serial dilutions of the stock on Vero cells for 24 h. Infected cells were fixed with 10% buffered formalin (Sigma, St. Louis, MO) for 24 h, and then stained with Hoechst 33342 dye (Thermo Fisher Scientific, Waltham, MA) to identify cell nuclei. Cells were photographed using a Nikon Ti-Eclipse microscope running high content analysis software (Nikon, Tokyo, Japan). The numbers of cell nuclei and infected cells were counted using Cell Profiler software (Broad Institute, Boston, MA) with pipelines developed by the authors (available upon request). To determine titers of the wild-type EBOV and MARV, Vero cells were incubated with 10-fold serial dilutions of the viruses at 37 °C for 1 h. After the virus was removed, the cells were overlaid with DMEM containing 2% FBS and 1.
To define cell cytoplasm and nuclei, samples were stained with HCS CellMask Blue Stain (Thermo Fisher Scientific, Waltham, MA). Z-stack immunofluorescence imaging was done using 100x lens on 20 cells in each sample. The images were subjected to deconvolution by AutoQuant X3 software (MediaCybernetics, Rockville, MD) and then 3D Fig. 2. Retro-2 inhibits EBOV entry. (A) To assess the effect of Retro-2 on EBOV transcription and/or replication, 293T cells were transfected with EBOV minigenome RNA encoding firefly luciferase gene and plasmids pC-NP, pC-VP30, pC-VP35, and pC-L encoding the EBOV polymerase complex. Twenty-four hours later, cells were treated with DMSO, Retro-2 (20 μM), or cycloheximide (1 μg/mL). Luciferase activity was determined after another 24 h. Three independent experiments were performed, and the averages ± SDs are reported.
(B) HeLa cells were preincubated with DMSO or Retro-2 (20 μM) for 1 h and then transduced with HIV-1 particles pseudotyped with EBOV-GP, MLV10A1-GP, or VSV-G, in the presence of the compound. Luciferase activity was measured after 48 h (RLUs). An unpaired two-tailed t-test was used to assess a statistical difference between infection efficiencies of EBOV-GP-pseudotyped virus in DMSO and Retro-2-treated cells. A P value < 0. 05 was considered statistically significant and was determined to be < 0. 01. (caption on next page) reconstruction using Imaris software (Bitplane, Zurich, Switzerland). The experiment was repeated 3 times. The number of VLPs localizing to NPC1-positive compartments was determined by manual counting. The percentage of virions colocalizing with NPC1 was calculated as a ratio of VLPs found in NPC1-positive compartments to total number of VLPs using Excel software (Microsoft).
Acknowledgements We are grateful to members of our laboratory groups for helpful discussions. O. S. , Y. S. , A. N. R. , and R. A. D. were funded by  Defense Threat Reduction Agency  grant  HDTRA1-12-1-0002  and the  Douglass and Ewing Halsell Foundations. R. N. , J. -C. C. , D. G. and J. B. were funded by the  French National Agency for Research (ANR)  under Contract  ANR-11-BSV2-0018  RETROscreen, the  Joint ministerial program  of R&D against CBRNE risks and CEA.
In vivo magnetic resonance imaging Following surgery, all animals were subjected to a first MR imaging of their brain (1h delay). Several other post-lesion time points were assessed (6h, 24h, 48h, 1 week and 2 weeks). Since each MRI scan required animal anesthesia, rats were not imaged at each delay in order to limit the time spent under anesthesia for each individual. This resulted in the sampling shown in Fig  1. Not all data points were available for every animal because sixteen rats were not used exclusively for this study  [12]. Furthermore, we limited the number of anesthesia required to perform MR imaging sessions to a maximum of 6 per rat. Unless stated otherwise, the analyses documented in the present work concern the lesion group only. All experiments were performed on a BRUKER pharmascan 70/16 system (BRUKER Biospin, Ettlingen, Germany) equipped with a 7-Teslas magnet and 16-cm horizontal bore size.
For each 40 μm brain section considered (NeuN and Cresyl violet staining) and each MR image, the lesion (histology) or hyperintensity (MRI) area was manually outlined using the Free-D software, a 3D reconstruction and modeling software which generates, processes and analyzes 3D point and surface models from stacks of 2D images  [21]. The lesion volume / hyperintensity were then estimated by taking into account the distance between two successive sample slices (160 μm for NeuN / Cresyl violet stained slices, 300 μm for MR images). Additionally, the volume of the medial habenula nuclei was also estimated using the same method. This later verification was done in order to check for any difference between NeuN and Cresyl violet stained tissues in terms of variation of volumes related to a potential tissue shrinkage.
Statistical analysis Cresyl violet staining and NeuN immunostaining statistical comparisons were performed using two-tailed t-tests, which were paired or unpaired depending on the specific comparison (specified in text). Comparisons regarding lesion extent measured using NeuN immunolabelling vs MR imaging at different time points were done using t-tests for dependent samples. Finally, differences in estimated lesion volumes at the different time points of MRI were assessed using a repeated measures analysis of variance (ANOVA) Type VI sums of squares (a specification which takes into account missing values in the ANOVA design), with Newman-Keuls (SNK) post-hoc comparisons. Data are presented as means ± SEM. Statistical analyses were performed using Statistica software.
Protecting against vulnerabilities must encompass protecting against both software and hardware attacks. In order to prove our results and ease their reproducibility, we are publishing our source codes, our data etc. on a git repository available at https://gitlab. inria. fr/rlasherm/ARMv7M-underattacks. Licenses are MIT for software and CC-BY-4. 0 for nonsoftware. This paper is organized as follows. A review of other works on similar topics is presented in section 2 to show how our own work is articulated with them. Then the fault injection process is presented in section 3: the experimental setup, how data is recovered and the assumed fault model are discussed. The core of our work is presented in section 4. The results of fault attacks achieving different effects are presented and discussed.
Whereas the experimental side evaluates the fault models and validate some theoretical attacks in practice  [9]. Fault attacks have been used in the past targeting chips similar to our target, namely Cortex-M3 (or closely related Cortex-M4) microcontrollers and mainly targeting cryptographic algorithms. Underfeeding, clock glitches and Electromagnetic fault injection (EMFI) rely on the same fault mechanism, timing violation. In  [3] , the authors characterize this mechanism on a FPGA with underfeeding. Barenghi et al. demonstrated a fault attack on an AES running on Linux with a constant underfeeding in  [2]. By lowering the feeding voltage, faults started to appear. They showed that some instructions are more sensitive than others. In their case, LOAD instructions were more easily faulted.
e. never but for vulnerability assessment) is not mandatory. Other synchronization possibilities include sniffing communications with the target or measuring its EM emissions to find a relevant pattern. The location of the probe on the chip was chosen after a scan that determined the most sensitive area on the chip. The same location was kept for all experiments. Synchronization is the main experimental difficulty with our target. An irreducible latency of 600ns is added by our fault injection platform between the input trigger and the EM pulse. The Sparkbench  [12]  software was used for the fault injection (open source, MIT license, see in reference). It is in charge of controlling the apparatus, orchestrating the commands to the target, retrieving the results and processing them. It includes a way to reset the target upon a crash and more generally to deal with errors that occur during a fault injection campaign.
Instrumentation The targeted chip is compatible with OpenOCD  [1] , a tool that allows a debug access to the chip through a JTAG interface. In particular, it is possible to set breakpoints (the chip halts if a particular memory address is about to be executed), and to arbitrarily read in memory. This tool was used to infer the behaviour of the chip upon a fault. Unfortunately, only partial information is available to us, there is no way to trace all previously executed instructions to the best of our knowledge. When temporally scanning the chip (the injection timing sweeps over a predefined range) crashes occur frequently. OpenOCD allowed us to verify that these crashes were always explainable by software modifications e. g. reading in memory outside of the RAM boundary (invalid address) or jumping to an address not corresponding to a valid program.
Sparkbench was configured to issue a report upon a crash. This report include the timing of the crash, the Stack Pointer (SP), and the Program Counter (PC) causing the software fault (in the ARM hard fault meaning). It was used to get a feedback on the memory location of the fault, but approximately, since the software fault often occurs after the hardware one. E. g. if the stack is corrupted by a fault injection (hardware fault), the program may still run for a few instructions before reaching a point where the corrupted stack provokes an incorrect value to be loaded into the PC (software fault).
The results show that the canary has been modified in numerous cases, thus the attack failed in these cases. But faulty results were achieved nonetheless. Using OpenOCD, the key value was observed: the first word was replaced by either 0x08000000 or 0x00008000 depending on the fault injection timing. This is not the result of a misplaced buffer, but the exact cause of this error is still unknown. It is reminded that the targeted code does not handle memory at the key address, it cannot be explained by a fault at the decode stage on a load instruction.
Characterization Techniques Scanning electron microscopy (SEM) (ZEISS, Oberkochen, Germany) was carried out on a ZEISS GeminiSEM 500 microscope with a resolution of 5 nm. The samples were deposited onto a double face graphite tape in order to avoid charging effect during the analysis. Elemental analyses were conducted on an inductive coupled plasma mass spectrometry (ICP-MS). A powder X-ray diffraction study was run on a Bruker D-8 Advance (Bruker Ins. , Wissembourg, France) with a CoKα radiation in a θ/2θ mode. The nature of the crystalline phase in the sample was indexed using the data base of the Joint Committee on Powder Diffraction Standards (JCPDS). The average particle size was calculated according to the Scherrer equation (B (2θ) = Kλ/Lcos θ, where B is a Bräggs angle (2θ), L is the crystal size and λ is the X-ray radiation)).
G. G. and C. P. -H. thank the TRAINER project (Catalysts for Transition to Renewable Energy Future) of the "Make our Planet Great Again" program (Ref. ANR-17-MPGA-0017) for support. The Italian team would also like to thank MIUR through the PRIN 2015 Project SMARTNESS (2015K7FZLH) for support to this work. Y. Liu acknowledges the financial support funded by the NSFC of China (Nos. 21606243, 91645117, 21473223).
Acknowledgments: SICAT SARL is gratefully acknowledged for providing SiC foam samples and  C. Pham  for helpful discussion. SEM analyses are carried out at the  ICPEES-IPCMS  ( UMR7515  and  UMR7504   CNRS-University of Strasbourg ). O. Ersen  from  IPCMS  ( UMR 7504 ,  University of Strasbourg ) is gratefully acknowledged for running TEM measurements.
Funding: G. G. and C. P. -H. thank the TRAINER project (Catalysts for Transition to Renewable Energy Future) of the "Make our Planet Great Again" program (Ref. ANR-17-MPGA-0017) for support. The Italian team would also like to thank MIUR through the PRIN 2015 Project SMARTNESS (2015K7FZLH) for support to this work. Y. Liu acknowledges the financial support funded by the NSFC of China (Nos. 21606243, 91645117, 21473223).
In addition, three proteins were significantly upregulated in ABA compared with LFA: glyoxalase domain-containing protein 4 (GLOD4, Spot C), pyruvate dehydrogenase E1 component subunit beta, mitochondrial (PDHB, Spot D) and adapter molecule Crk (CRK, Spot H, Table  1  and Fig. 2 ). At d17, six proteins were significantly upregulated in ABA mice compared with Controls: EF-hand domain-containing protein D2 (EFHD2, Spot N), tropomyosin alpha-3 chain (TPM3, Spot P), 2-oxoglutarate dehydrogenase, mitochondrial (OGDH, Spot Q), gamma-enolase (ENO2, Spot R), dynamin-1 (DNM1, Spot   1. Identified proteins altered at d10 and/or d17 in the hypothalamus. Proteins identified at d10 (A-L), at d17 (M-T) and both d10/d17 (U and V). a MASCOT, algorithm that uses mass spectrometric data to identify proteins.
4 ). All these proteins are involved in various biological pathways. By using STRING, UniProt-GO Biological Process, KEGG and PANTHER databases, functional clustering (Fig. 5 ) has been performed and revealed several clusters including protein involved in cellular process, cellular component organization, immune system process, response to stimulus, dopamine metabolism, energy metabolism, glycolysis, tricarboxylic acid cycle, lipid and carbon metabolism, oxidative stress, catabolic process, vesicle trafficking or endocytosis. Interestingly, among these altered proteins, 10 are involved in energy metabolism and were upregulated in ABA mice at d10 and/or d17 (p < 0. 05, Fig. 5 ). For instance, G6PDX involved in the pentose phosphate pathway and ACLY, ACO2, PDHB involved in the tricarboxylic acid (TCA) cycle were upregulated at d10 (Fig.
Thus, p-DNM1L/DNM1L increased at d17 in ABA mice. By contrast, markers of mitochondrial fusion, mitofusin 1, mitofusin 2 and optic atrophy 1 (OPA 1), were not affected in LFA and ABA groups (Supplemental Fig. S5 ). In addition, mitochondrial biogenesis seemed to be unaffected at day 17 since PGC-1α and TFAM expression remained unchanged (Supplemental Fig. S6 ). All these data suggest an increase of mitochondrial fission in ABA mice at d17 while mitochondrial fusion and biogenesis remained unaffected. To further investigate mitochondria, we assessed in the hypothalamus the Citrate Synthase (CS) Activity, a marker of the mitochondrial membrane surface. We did not observe significant difference between Control, LFA and ABA groups (Table  2 ) that was illustrated by the immunostaining of VDAC1, expressed at the outlier membrane of mitochondria (Supplemental Fig.
S7 ). As previous data reported a role for both DNM1L and mitochondria fission in the regulation of autophagy  31  , we thus analysed autophagy in hypothalamus of ABA mice. Hypothalamic autophagy in ABA model at d17. To determine the activation of autophagy at d17, we performed western blots directed against autophagy markers LC3II and LC3I. As shown in Fig. 7a , LC3I expression was significantly decreased in ABA group (p < 0. 05), whereas LC3II had a tendency to increase. Consequently, LC3II/ LC3I ratio, commonly used as a marker of autophagy, was significantly upregulated in ABA mice compared with Controls (p < 0. 05). We also observed a trend for an increase in ABA compared with LFA but difference did not reach significance (p = 0. 06; Fig. 7a ). In addition, BNIP3 expression, a marker of mitophagy, was increased in ABA mice compared with Controls (Fig.
7c ). NDP52, another factor involved in mitophagy, was not significantly affected (Fig. 7c ). At d17, proteomic analysis also revealed that Dynamin-1 (DNM1) was enhanced in ABA mice (Fig. 3 ). DNM1 plays a key role in early endosome formation leading to autophagolysosome formation. We confirmed the increase of DNM1 by western blot at d17 (Fig. 7b ). All these data showed that autophagy, particularly mitophagy, was increased in the hypothalamus of ABA mice. Increased protein synthesis rate in ABA model at d17. Despite the presence of hypothalamic autophagy in ABA mice, hypothalamic protein levels were similar in the three groups (Fig. 8a ). We thus evaluate protein synthesis by SUnSET method. Puromycin incorporation significantly increased in ABA group compared with Control group (p < 0. 05; Fig.
Briefly, ABA mice were placed in cages with an activity wheel connected to Running Wheel ® software (Intellibio, Seichamps, France), while both LFA and Control mice were placed in standard cages. From day 6, LFA and ABA mice had a progressive limited access to food from 6 h/day at day 6 to 3 h/day at day 9 and until the end of experiment. Food access was given at the beginning of the dark phase (10:30 AM). All mice had free access to water. For ethical reasons, if weight loss exceeded 20% during 3 consecutive days, mice were euthanized. At the end of experiment, mice were injected with lethal dose of ketamine/largactil (40 and 1 mg. kg -1 , respectively) at day 10 (n = 8/group) or at day 17 (n = 8/group) corresponding to an initial and a persistent weight loss, respectively. All mice were sacrificed at the beginning of the dark phase before food distribution.
Two-dimensional gel electrophoresis was performed in triplicate for six samples per group (n = 6/group at each time) to minimise variations in migration, and each set of 3 gels was subjected to image analysis to confirm the nonappearance of statistically differential spots within the set of gels. Analysis and comparisons of 2D gel images. Silver stained gels were scanned with an ImageScanner II (GE Healthcare) previously calibrated by a greyscale marker (Kodak) and 2D images were captured with Labscan 6. 00 software (GE Healthcare). Differential analysis was realized by using Progenesis SameSpots v4. 1 software (Nonlinear Dynamics Ltd, Newcastle, U. K. ) for spot detection, background subtraction, spot volume normalisation and expression. The most representative silver-staining gels (gel migration, spot definition, and spot number) of each sample (n = 6 per group) was used to identify differential hypothalamic protein expression.
Protein identification by LC-ESI-MS/MS. For protein identification, MS/MS peak list were extracted and compared with MASCOT Daemon version 2. 2. 2 (Matrix Science, Boston, MA) server, using the following parameters: enzyme specificity, trypsin; no restriction on protein molecular weight; one missed cleavage permitted; no fixed modification; variable modifications, methionine oxidation, cysteine carbamidomethylation, serine, tyrosine, and threonine phosphorylation; monoisotopic; peptide charge, 2+ and 3+; mass tolerance for peptide, ±20 ppm (Q-TOF) or ±1,5 Da (Ion Trap); mass tolerance for fragment ions, ±0. 06 Da (Q-TOF) or ±0. 5 Da (Ion Trap); ESI-QUAD-TOF and/or ESI-TRAP as instrument; taxonomy, mus musculus; database, UniProtKB/ Swiss-Prot. Protein matching probabilities were validated if proteins hits satisfied one of following criteria: MASCOT score of >32 (p < 0.
01) or >22 (p < 0. 05) with identification confidence ≥3 top-ranking peptides (number of matching/the coverage of protein sequence). False discovery rate (FDR) assessment was determined using the "decoy" option of MASCOT (target database, SwissProt mus musculus). Results were considered relevant if the FDR was 0%. Gene ontology (GO)-based analysis. STRING 10. 0 software was used as search tool for the retrieval of interacting genes/proteins database. UniProt GO (Biological Process) annotation, PANTHER (Protein ANalysis Through Evolutionary Relationships) classification and KEGG (Kyoto Encyclopedia of Genes and Genomes) Pathway were used to categorise proteins on the basis of their biological process.