dataesr/scientific-paragraphs-categorization huggingface dataset:
Workflow The workflow developed for the validation and the evaluation of the TPS can be summarized as follows: (i) phantoms creation, (ii) simulation of the SPECT projections, (iii) attenuation map generation and tomographic reconstruction, (iv) creation of the input file for the MC simulation, (v) conversion of the GATE output file in absorbed dose images, (vi) calculation of absorbed dose images by convolution of voxel S-values (the latter calculated independently from the TPS tested here) for comparison and validation purposes. The software packages (and respective references) used for each step are reported in the corresponding subsection.
Phantoms Creation The voxelized phantoms used in this study were constructed starting from a real patient CT (512 × 512 matrix with a pixel size of 1. 367 × 1. 367 mm 2 and a spacing between slices of 3. 27 mm, high quality full diagnostic scan). CT contouring was performed with ITK-SNAP software  [34] , using a semiautomatic segmentation technique based on 3D active contours. The contoured volumes of interest (VOIs) were liver, soft tissue, lungs, adipose tissue, cortical bone, trabecular bone, air, breast prosthesis. Activity maps, with the same voxel and matrix size of the CT scan, were also constructed using ITK-SNAP, which allows the definition of the activity concentration ratio among different regions.
SPECT Simulation 99m Tc-SPECT projections were simulated with the SIMIND MC code  [35]  using the contoured CT and the activity map created with ITK-SNAP as input. The main parameters chosen for the simulation are reported in Table  1. These parameters mimic a MEDISO Anyscan gamma camera, which MCID was initially optimized for. The simulated activity was 100 MBq (as suggested in  [1] ) and an acquisition time for projection angle of 20 s was simulated. Poissonian noise was added to the simulation in order to obtain a more realistic functional image. A resampled CT (128 × 128) was also obtained.
Convolution with Voxel S-Values Calculated Independently For validation purposes, absorbed dose images were also obtained applying convolution technique on the reconstructed SPECT images. The voxel S-values used in the convolution were calculated with the EGSnrc code  [17] , independently from the tested TPS, considering a 4. 1 mm voxel size and ICRU soft tissue  [38]  as medium (density = 1. 04 g/cm 3 ). The S-values calculation was set as described in  [17]. The source description was the same as that used for the GATE simulation in the TPS, to increase comparability of dosimetric results. Absorbed dose images obtained by convolution were used to validate and test the TPS. Since the convolution technique is based on the application of MIRD approach at the voxel level, the results obtained with convolution will be labeled as "MIRD" hereinafter.
Remaining discrepancies could be related to some differences between the MC codes used for calculating the OLINDA-EXM sphere S-factors and the updated code used in this work, and to possible mismatching in source description. Relative differences for the BS and the MS are in agreement with the results shown in  [41] , where the authors presented an analogous comparison for various sphere diameters: their smallest sphere had a 27 mm diameter, resulting in RD = -5%, so the RD here obtained for SS (20 mm diameter, see Table  6 ) also appears reasonable and comparable. Dosimetry in lesions with size equal (or less) to the maximum 90 Y β -range (around 12 mm in soft tissue) should be treated carefully, also due to limited resolving power of SPECT imaging. An analysis of dosimetry in small lesions and a correction factor for the MIRD standard equation are proposed in  [42].
Source data are provided as a Source Data file. deficits were also reported in progranulin haploinsufficient mice, a mouse model of FTD  57. To determine whether Fus ΔNLS/+ mice have social behavioral deficits, we first performed the residentintruder test specific for evaluating sociability in mice. Interestingly, 4-months-old Fus ΔNLS/+ mice showed a trend towards longer interaction with the intruder mouse as compared with Fus +/+ mice (p = 0. 07) (Fig. 2a ), that was significant at 10 months of age (Fig. 2b ) and persisted until 22 months of age (Fig. 2c ). Aggressive behavior was only observed at 4 months of age, and not affected by the Fus genotype (attack duration:  To further characterize the social behavioral impairment, we used a modified version of the three-chamber social paradigm.
FLASH MRI datasets for Fus ΔNLS/+ mice and Fus +/+ littermates were processed for volumetric quantification using an in-house developed script  58  , aimed at registering the MRI images to a template derived from the Allen Brain Atlas reference and then at parcellating the cerebral structures into hierarchically arranged volumes of interest, which can be interrogated for the volume of any region or group of regions (Fig. 4a, b ). The overall intracranial volume (ICV) was comparable in Fus ΔNLS/+ and Fus +/+ mice (Fig. 4c ). However, upon normalization for the ICV, the volume of the brain parenchyma was significantly decreased in Fus ΔNLS/+ (by ~1. 5%; average normalized volume was 98. 52% for Fus +/+ and 97. 14% for Fus ΔNLS/+ ; Fig. 4d ). Visual inspection of the MRI images revealed a substantial increase in the volume of lateral ventricles, which was confirmed by the registration algorithm and quantitated as an almost doubling of ventricular volumes (Fig.
To understand the molecular basis of altered behavior in Fus ΔNLS/+ mice, we performed RNAseq on frontal cortex of 5-and 22-months-old Fus ΔNLS/+ mice and their wild-type littermates. Principal component analysis showed a clear separation between Fus ΔNLS/+ mice and their wild-type littermates at 22 months of age, while clustering was imperfect at 5 months of age, suggesting an exacerbation of the transcriptional differences between genotypes with age (Supplementary Fig. 3a ). Using a stringent analytical pipeline (FDR < 0. 05), we did not identify differentially expressed genes between Fus ΔNLS/+ and Fus +/+ mice at 5 and 22 months (Supplementary Fig. 3b ). To ensure that the absence of differentially expressed genes was not due to the stringent calibration of p-values, we compared the 5months and 22-months-old Fus +/+ mice RNAseq datasets to probe age-related alterations.
We were able to detect more than 2000 genes differentially expressed between 5-and 22-monthsold wild-type mice, at a 5% false discovery rate, demonstrating that this approach can reliably detect changes in gene expression (Supplementary Fig. 3b ). To place gene expression changes in a systems-level framework, we performed weighted-gene coexpression network analysis (WGCNA) across all available Fus ΔNLS/+ and Fus +/+ datasets, including 5 and 22-months RNAseq, as well as 1 and 6 months RNAseq datasets from Sahadevan et al. 52. Potential batch effects were removed using a negative binomial regression model to estimate batch effects based on the count matrix  59  (Supplementary Fig. 3c ) and allowed clustering between genotypes (Supplementary Fig. 3d ). WGCNA analysis allowed us to identify two mRNA modules significantly correlated with the genotype condition in cortex and labeled as turquoise and yellow modules according to the WGCNA conventions (Bonferronicorrected P < 0.
2-1-54-2532-11-2016, two-photon microscopy) and by "Regierungspräsidium Tübingen" (animal license number 1431, MRI). Behavioral tests were done during the light phase (between 9 am and 5 pm) of their light/dark cycle except for indicated experiments. Until the mice reached the age when the behavioral tests were performed mice were group-housed. Once mice were single housed for the behavioral task they were kept individually for only a period necessary to finalize the set of behavioral experiments and in order to minimize possible negative effects of isolation, afterwards cohorts were sacrificed and processes for downstream analyses. Male mice of 4, 10, and 22 months of age were subjected to behavioral studies and data were analyzed blind to genotypes. The sex of the animals studied is indicated in each figure legend.
0140 vs corresponding wild-type fraction; ### p < 0. 0001 vs corresponding cytoplasmic fraction of the same genotype. Gabra1: **p = 0. 0012 vs corresponding wild-type fraction; ### p < 0. 0001 vs corresponding cytoplasmic fraction of the same genotype. Gabrb1: **p = 0. 0029 vs corresponding wild-type fraction; ### p < 0. 0001 vs corresponding cytoplasmic fraction of the same genotype. Grid2: ### p < 0. 0001 vs corresponding cytoplasmic fraction of the same genotype. Ctnnd2: no significant differences observed (p > 0. 05). Source data are provided as a Source Data file. level and 28 cm apart. The number of cage crossing was recorded automatically and was used to determine or score the spontaneous locomotor activity. The experiment began at 17. 00 pm and after 2 h of habituation continued for 3 consecutive days for a complete 24 h nictemeral cycle (12 h dark and 12 h light).
Open field. The general exploratory locomotion and anxiety in a novel environment were tested during 15 min long sessions in the open field arena (72 × 72 × 36 cm) located in a test room and lit by a 600 lux for background lighting, according to published protocol  99. The open maze was divided by lines into sixteen squares (18 × 18 cm). Each mouse was placed in the center of the arena and allowed to freely move while being video recorded. The recorded data were analyzed offline with EthoVision XT software system (Noldus Information Technology). The time spent in the center (four central quadrants) vs. the perimeter (12 peripheral quadrants) was used to measure anxiety, while the total distance traversed in the arena and average moving speed (mean velocity) was used to evaluate locomotor activity.
Complete QC report will be made publicly available. Raw reads were mapped to the mouse reference genome GRCm38 with STAR version 2. 7. 0 112 and default parameters using Ensembl gene annotations (version 87). Gene-level abundance estimates were estimated using the option-quantMode geneCount in STAR. We filtered the lowly expressed genes wherein each gene was required to have at least 15 counts across all samples and used both exonic and intronic reads. The filtered set of genes was used for the PCA plot and differential expression analysis. Differential gene expression analysis was performed with the ARMOR workflow  113  and a cut off FDR value of 0. 05 was set in both datasets. RNA samples sequenced in the present study from 5-6-months-old mice (n = 10) were pooled to the samples from Sahadevan et al.
Module hubs were defined by calculating module membership (kME) values, which are the Pearson correlations between each gene and each ME. Genes with a kME < 0. 7 were removed from the module. Network visualization was done with the igraph package in R. Module definitions from the network analysis were used to create synthetic eigengenes for the 1-month, 6 months and 22-months timepoint and were used to understand the trajectory of various modules across timepoints. Enrichment analyses using single-cell experiment data. To reduce false positives, we used FDR-adjusted P-values for multiple hypergeometric test comparisons. For cell-type enrichment analysis, we used an already published single-cell mouse brain dataset  116. Finally, genes in network modules were characterized using EnrichR (version 1.
Acknowledgements We thank the  Imaging Platform of the CRBS (PIC-STRA UMS 38, Inserm, Unistra)  and the  Plateforme Imagerie In Vitro de Strasbourg  for their help in performing imaging for this study. This work was funded by  Agence Nationale de la Recherche  ( ANR-16-CE92-0031  to A. L. B. and L. D. ,  ANR-16-CE16-0015  to L. D. ,  ANR-19-CE17-0016  to L. D. ), by  Fondation pour la recherche médicale (FRM ,  DEQ20180339179 ),  Axa Research Funds (rare diseases  award  2019 , to L. D. ),  Fondation Thierry Latran (HypmotALS , to L. D. and F. R. and TRiALS to F. R. ),  Radala Foundation  (F. R. ),  MNDA  ( Dupuis/Apr16/852-791  to L. D. ),  ALSA  ( 2235 ,  3209 , and  8075  to L. D. and C. L. T. ),  Target ALS  (to C. L. T. ),  NINDS/ NIH   R01-NS108769  (to C. L. T. )  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy  within the framework of the  Munich Cluster for Systems Neurology-EXC 2145 SyNergy-ID 390857198  (S.
Real-time PCR (qPCR) qPCR was performed using the SsoFast EvaGreen Supermix (Bio-Rad) supplemented with 0. 4 μM specific primer pairs (sequences of primers are listed in Table  S2 ) and a CFX96 cycler (Bio-Rad). Each qPCR reaction was performed in technical duplicate. All experiments included a standard curve for each primer pair used. For the copy number variation assay, copy number of the target sequence was determined using the comparative Ct (ΔΔCt) calculation method and the Top3 gene as reference sequence. All results were analyzed using Bio-Rad Quantity One analysis software.
RNA extraction and real-time reverse transcription PCR Total RNA was isolated using the RNeasy Plus kit (Qiagen) according to the manufacturer's instructions. RNA was subsequently treated with a TURBO DNase (ThermoFisher Scientific). RT-qPCR was performed in one step using 10 ng of total RNA and the iTaq Universal SYBR Green One-Step kit (Biorad) supplemented with 0. 3 μM specific primer pairs (sequences of primers are listed in Table  S2 ) and a CFX96 cycler (Bio-Rad). Each RT-qPCR reaction was performed in technical triplicate. All experiments included a control reaction without reverse transcriptase. Copy number of the target sequence was determined using the comparative Ct(ΔΔCt) calculation method and the GAPDH gene as reference sequence. All results were analyzed using Bio-Rad Quantity One analysis software.
DNA was counterstained with DAPI (Sigma-Aldrich). Metaphases were captured on a confocal laser microscope (FV1000 Olympus) and images were analyzed using Image J software (NIH). Quantitation to measure for SCE between α satellite sequences (C-SCE) was done by counting the number of CO-FISH signals showing C-SCE over the total number of CO-FISH signals observed for each metaphase.
ACKNOWLEDGEMENTS We would like to thank  J. Chen  ( University of Texas, MD Anderson, USA ) for providing the wtBRCA1-complemented HCC1937 cells,  T. Yasuhara  ( University of Tokyo, Japan ) for providing the ΔRad52 U-2OS cells and  R. Crouch  ( NICHD, NIH, Bethesda, USA ) for providing the plasmid expressing the GFP-RNase  H1. We are grateful to the  Imaging Core Facility TRI at IPBS , in particular E. Naser. This work was supported by the  Ligue Contre le Cancer Midi-Pyrénées  and the  ANR PRC-2019  (to F. L. ) and by the  Ligue Nationale Contre le Cancer  ( Labellisation 2018  to P. C. ).
Study Design and Data Source In our longitudinal, population-based, observational registry study, we assembled information from 11 population-based cancer registries (PBCRs) in 10 SSA countries (Fig. 1 ). We collected data on the presentation, diagnostic workup, patterns of care, and factors influencing survival of patients diagnosed with PCa between 2010 and 2015. The participating PBCRs included the Registre des Cancers d'Abidjan (Côte D'Ivoire), the Addis Ababa City Cancer Registry (Ethiopia), the Registre des Cancers du Mali (Bamako, Mali), the Registre des Cancers de Brazzaville (Congo), the Bulawayo Cancer Registry (Zimbabwe), the Cotonou Cancer Registry (Benin), the Eldoret Cancer Registry (Kenya), the Kampala Cancer Registry (Uganda), the Maputo Cancer Registry (Mozambique), the Nairobi Cancer Registry (Kenya), and the Namibian National Cancer Registry.
A minimal sample size of 700 would produce a 2-sided 95% confidence interval (CI) with a width equal to 0. 075 if the sample proportion of patients with adequate care were 0. 5. We drew a simple random sample of 60 to 100 patients per registry (International Classification of Diseases, Tenth Revision code C61) who were registered within a 2-year period (Supporting Table  1  and Supporting Fig. 1 ). For Cotonou and Addis Ababa, we used all patients registered because there were fewer than 60. Patients discovered to be duplicates in the database, patients who had relapses with a date of incidence before 2010, and patients falsely registered as having PCa were excluded. Patients with additional information for diagnostics, TNM stage, therapy, or outcomes were labeled the traced cohort and were further evaluated in Kaplan-Meier survival and Cox regression analyses.
Data Collection The PBCRs collect information on sociodemographic, clinical, and pathological characteristics, therapy, and vital status according to AFCRN's Standard Procedure Manual. 13 Between September 2016 and May 2018, local staff from the PBCRs visited the health institutions to update the information of each randomly selected patient via medical charts and pathology reports. In cases without additional information traced, the patients or their relatives were called. The types of clinical data considered in Cancer November 15, 2021 our study included the following: PSA level at diagnosis, Gleason score, physical examination (ie, digital rectal examination [DRE]), imaging methods for staging, Eastern Cooperative Oncology Group (ECOG) performance status (PS), and TNM stage. The types of treatment data included surgery, radiotherapy, and endocrine therapy.
Statistical Analysis We used the Statistical Package for the Social Sciences (version 25) from IBM. We calculated overall survival (OS) by using the time between the date of diagnosis and the date of last known follow-up or death. We computed 1-to 5-year Ederer II age-standardized relative survival (ASRS) with Stata software (version 15) from StataCorp LLC, and we included World Health Organization life tables and adopted Corazziari et al's International Cancer Survival Standard 1 age standard for PCa. 16 We used the Kaplan-Meier method and a multivariable Cox proportional hazards model to analyze longitudinal data. We first assessed for the condition of "missing at random" (uninformative censoring) by performing a reverse Kaplan-Meier analysis. We restricted the Cox and Kaplan-Meier analyses to patients with survival longer than 3 months to allow time for the initiation of therapy and to account for bias from missing treatment through early death.
RESULTS A cohort of 693 patients (median age, 70 years; interquartile range, 64-77 years) with PCa (the total populationbased cohort) was assembled from 11 PBCRs. Medical records for the extraction of additional sociodemographic and clinical data were located for 365 of the patients (52. 7%; the traced cohort). For the remainder of the total population-based cohort, basic registry data could not be augmented because no additional information was retrieved by the original sources reporting the cancer diagnosis. The traced cohort (n = 365) represented 17. 6% of the 2068 patients with PCa registered in the time period of random sampling in the included PBCRs (Supporting Table  1 ).
Therefore, the true population-based picture may even have a higher proportion of unstaged and untreated patients. We also believe that some records were lost at random because records are handwritten, the misspelling of names is common, and record-keeping systems are often poor. We also may have missed treated patients who had left the registration area to seek treatment elsewhere. However, such patients probably represent a small proportion of all patients because our study areas were major cities, which usually provide the best cancer care in countries. Second, our survival data may reflect some bias. The treatment effect was likely overestimated in the Cox regression analysis of our study: 1) treatment was not assigned at random (healthier patients were selected), 2) patients with early deaths did not receive therapy, 3) the date of diagnosis (and, therefore, the start of the survival time) had substantial variation due to delays of the system, and 4) the degree of guideline adherence was assessed only during the survival time and not before the survival time had started (an immortal time bias).
Differential accessibility of aSYN regions within fibrillar polymorphs to monoclonal antibodies We determined the accessibility of polypeptide stretches spanning aSYN primary structure to different antibodies in aSYN monomers, fibrils, ribbons, and fibrils 91. The different species were immobilized on nitrocellulose membranes that were exposed to monoclonal antibodies directed against aSYN amino acid stretch 1 to 15 (ASyM), 91 to 99 (Syn1), and 118 to 127 (10D2) (Fig. 1D ). ASyM bound all species except ribbons, which indicates that its epitope is involved in ribbon amyloid core, in agreement with solid-state NMR data we previously obtained  (22, 27, 29, 30). The antibody 10D2 bound all fibrillar polymorphs, indicating that aSYN C-terminal end is exposed to the solvent, again in agreement with previous solid-state NMR data  (22, 27, 29, 30).
Assessment of antibodies accessibility Native monomeric fibrillar aSYN polymorphs (2. 5 μM, 2 μl) were spotted in quadruplicate on a nitrocellulose membrane. The membrane was next blocked with 5% skimmed milk for 1 h and incubated with different antibodies as described above. For statistical analysis of the six replicates we performed, we assumed a Gaussian distribution and equal standard deviations for the intensities measured for each aSYN species and each antibody. We then performed one-way ANOVA followed by Dunnett's multiple comparisons test and compared the mean intensity obtained for each aSYN polymorph to the mean intensity obtained for monomeric aSYN, used as control, using GraphPad Prism (version 8. 0. 0 for Windows, GraphPad Software, www. graphpad. com). Significant (**** corresponding to a p-value < 0.
Mass measurements were performed in linear or reflectron modes for long and short polypeptides, respectively. External mass calibration was achieved using full-length aSYN or Pepmix4, a mixture of bradykinin 1 to 5 (573. 3149 Da), angiotensin II (1046. 5423 Da), Neurotensin (1672. 9175 Da), ACTH  (2465. 1989 Da), and bovine insulin chain B (3494. 6513 Da) from LaserBio Labs, in linear and reflectron mode, respectively. For long polypeptides, full-length aSYN present in the samples was further used for additional internal calibration. Identification of the peptides was performed using the GPMAW 8. 2 software and a minimum mass difference between measured and theoretical masses set at 100 ppm in reflectron mode and 300 ppm in linear mode. Three independent replicates were performed for each type of analysis.
All spectra were analyzed using peak view software. As the peaks of peptides with sodium adduct have a better intensity, we first confirmed their identity by further desalting directly on the MALDI plate and by CID fragmentation using MALDI-TOF-TOF (Fig. S4 ). We completed this identification by nanoLC-MSMS with a TripleTOF 4600 (ABSciex) mass spectrometer as previously described  (52). Peptides were identified using a database search with the Mascot search engine (Matrix Science, version 2. 4. 1) against the aSYN sequence and manual validation was performed. We then analyzed HDX of those peptide peaks. The amount of hydrogen to deuterium exchange was calculated as previously described  (51)  except that the baseline correction was done directly on peak view software, using the height centroid spectrum option.
Deuterium incorporation measured for the fully deuterated peptides was compared with the theoretical mass of the fully deuterated peptides. Using triplicate backexchange experiments and six pepsin-derived aSYN peptides  (5-17, 18-38, 39-54, 94-113, 114-124 , and 125-140), we calculated an average back-exchange of 43% with a standard deviation of 3%. All the experiments were done in triplicate. The data we present are those obtained after 30 min exchange in D2O when maximum deuterium incorporation is observed. The mean standard deviation is 4% and the maximum standard deviation measured is <12%.