dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Vanderwal et al. , 2015; Alexander et al. , 2017). This is also true in awake NHP imaging; for example, in the PRIME-DE NKI site, the mean FD for rest sessions was 0. 21 (SD = 0. 03), but 0. 14 (SD = 0. 07) during movie sessions (t = 2. 82, p = 0. 006, df = 128). Regarding motion-correction algorithms, those designed for human neuroimaging data perform similarly for NHP data. As such, most groups use SPM, AFNI, ANTs, or FSL software to estimate the motion parameters and remove motion artifacts. The estimates of the movement values can be used as regressors of no interest during the analysis of functional data, if desired. The grayplot, proposed by  Power (2017) , can be used to illustrate the motion and the de-noising effects. However, as with all neuroimaging data, image distortions or signal drop-out caused movement correction to be suboptimal.
Next Steps The PRIME-DE is an ongoing data-sharing consortium stewarded by INDI, which has shared more than 15,000 human imaging datasets over the past decade. As such, we invite new contributions from all investigators in the NHP imaging community, not just those involved in the consortium at the time of the initial release. It is our hope that future contributions will help to capture and promote emerging trends in the NHP community, such as the increasing ability to image during awake states and usage of high-field scanners (e. g. , 7. 0T), as well as the growing range of species being examined (e. g. , marmosets). Additionally, we hope that other data modalities obtained in the NHP community (e. g. , electrophysiology) will be shared with higher frequency. Similar to other INDI-based efforts, PRIME-DE is intended to take the first step-establishing a culture for sharing.
ACKNOWLEDGMENTS We thank  Cameron Craddock  for his creation and open sharing of the  Preprocessed Connectome Project (PCP) Quality Assessment Project (QAP) , which was used here (http://preprocessed-connectomes-project. org/quality-assessment-protocol/index. html). Primary support for the work by M. P. M. and the INDI team was provided by gifts from  Joseph P. Healy  to the  Child Mind Institute  as well as by the  BRAIN Initiative  ( R01MH111439 ). M. P. M. is a  Randolph Cowen and Phyllis Green Scholar. Primary support for the work by C. S. is provided by the  BRAIN Initiative  ( R01MH111439 ) and the  Sylvio O. Conte Center ''Neurobiology and Dynamics of Active Sensing '' ( P50MH109429 ). Primary support for the work by D. S. M. is provided by the  Max Planck Society. Primary support for the work by T.
Primary support for the work by Z. W. is provided by the  National Natural Science Foundation of China  ( 81571300 ,  31771174 ). Primary support for the work by F. H. -B. , M. M. , and C. G. is provided by the  French National Research Agency  ( ANR-14-CE13-0005-1  and  ANR-15-CE37-0003 ) and the  NEURODIS Foundation. Primary support for the work by the  Icahn School of Medicine at Mount Sinai  was provided by a  Charles H. Revson Senior Fellowship in the Biomedical Sciences  to  P. L. C. Primary  support for the work by M. G. B. and J. H. M. provided by  NIA   P01AG016765. Primary support for the work by K. M. , D. A. R. , and A. S. from  McGill University  is provided by a grant from the  Brain Canada Foundation. Primary support for the work by the  NIMH  is provided by the  Intramural Research Program  of the  NIMH  ( ZICMH00289 ).
Hirschl /  Monique Weill-Caulier Trusts Award ; a  Pew Scholar Award  in the Biomedical Sciences; a  McKnight Scholars Award ; a  Human Frontier Science Program Research Grant ; the  New York Stem Cell Foundation ; the  National Eye Institute ; the  NIMH ; the  NSF Science and Technology Center for Brains, Minds, and Machines ; and the  National Science Foundation. Primary support for the work by E. P. , C. R. E. W. , and C. E. is provided by  Agence National de la Recherche ,  Fondation Neurodis , the  LabEx CORTEX   ANR-11-LABX-0042 ,  Fondation de France ,  Human Frontier Science Program , and  Fondation pour la Recherche Medicale. Primary support for the work by E. Y. and N. H. is provided by the following grants:  R01-NS081118 ,  R01-NS085188 ,  P41-EB015894 ,  P30-NS076408 , and the  University of Minnesota Udall Center   P50NS098573.
METHOD DETAILS Criteria for Data Contributions PRIME-DE welcomes contributions from any laboratory willing to openly share multimodal MRI datasets obtained from non-human primates, including but not limited to functional MRI, diffusion MRI and structural MRI. Contributors are responsible for ensuring that any data collected and shared were obtained in accordance with local ethical and regulatory requirements. There are no set exclusion criteria. We encourage the sharing of all data, independent of quality. This decision is based on the realizations that: 1) there is no consensus on acceptable criteria for movement in functional MRI or diffusion MRI data, 2) high motion datasets are essential to the determination of the impact of motion on reliability, and 3) new approaches continue to be developed to account for movement artifacts.
Introduction The introduction of Matrix Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has had a great impact on the identification of anaerobic bacteria isolated from human clinical specimens, providing more timely and accurate identification compared with phenotypic methods  [3, 4, 16]. Several studies have been performed on the performance of MALDI-TOF MS for the identification of anaerobic bacteria. The general conclusion is that the databases of the MALDI-TOF MS systems need optimization for the identification of anaerobic bacteria  [4, 9, 17]. To accomplish this we formed the European Network for the Rapid Identification of Anaerobes (ENRIA). Within this project, seven European laboratories, with expertise in anaerobic bacteriology, collaborated in order to collect clinical isolates for addition to the MALDI-TOF MS database.
As a quality requirement, the ENRIA group set itself the goal of obtaining at least five main spectral profiles (MSPs) for each species  [16]. The ENRIA group felt that it was vital for the project to benefit the microbiology community worldwide and therefore we setup a collaboration with Bruker Daltonics, Bremen, Germany. At the time of going to press the first batch of MSPs of ENRIA strains have been added to the db6903 database (V6 database) of the Bruker MALDI-TOF MS Biotyper system. In this study we present the validation of this optimized database for the identification of anaerobic bacteria. Genera/species that require further optimization are also highlighted.
Bacterial strains Over a period of six months all anaerobic bacteria isolated from human clinical specimens were identified using the db5989 database (V5 database) and the V5 database plus ENRIA MSPs, created and supplied by Bruker Daltonics. The latter consisted of two parts: the confirmed ENRIA MSPs (the ones included in the last update of the Biotyper V6 database) and all ENRIA MSPs (the confirmed ENRIA MSPs and the MSPs nominated for adding to the Biotyper database). An overview of these MSPs is shown in the supplemental data, Table  1. This was performed by all 'expertise laboratories': University Medical Center Groningen (UMCG), Groningen, The Netherlands; Center Hospitalier Universitaire de Montpellier, Montpellier, France; Odense University Hospital, Odense, Denmark; UK Anaerobe Reference Unit (UKARU), Public Health Wales Microbiology, Cardiff, United Kingdom; University of Szeged, Szeged, Hungary and University Hospital Brussels, Brussels, Belgium.
Data interpretation The obtained identifications were divided into 3 groups. No reliable identification (or no identification), identification with low confidence and an identification with a high confidence. The latter two were interpreted as a reliable genus identification and a reliable species identification, respectively. For several species an identification to the subspecies level was given by the Biotyper, for example for Fusobacterium nucleatum and Actinomyces neuii. However, for the data interpretation only the identification to the species level was considered. Furthermore, we are aware of the fact that several species of Veillonella are difficult to separate from each other by MALDI-TOF MS and 16S rDNA gene sequencing, namely, Veillonella dispar, Veillonella parvula, Veillonella denticariosi and Veillonella rogosae.
Distribution of bacterial strains The optimized MALDI-TOF MS database was validated using 6309 anaerobic strains isolated from human clinical specimens. The distribution of the genera is shown in Table  1. The most prominent genus is Bacteroides, which represented 14. 8% of the total number of strains analyzed. The genera Cutibacterium (formerly Propionibacterium  [15] ) and Prevotella, represented 10. 3% and 9. 2% of the total isolates respectively. Other notable genera which were included in sizeable numbers include those that belong to the gram-positive anaerobic cocci (GPAC) (Finegoldia, Peptoniphilus, Parvimonas, Anaerococcus, Peptostreptococcus, Murdochiella and Peptococcus) accounting for 21. 9% as a group of all the analyzed anaerobes.
Validation All results are per genus presented in Table  2. Detailed results per genus are presented in the "Data in Brief"  [19]. Of the 6309 analyzed strains, 4485 (71. 1%) strains were identified with high confidence, 1064 (16. 9%) with low confidence and 760 (12. 0%) could not be identified, using the V5 database. Adding the confirmed ENRIA MSPs increased the amount of strains identified with high confidence to 4718 (74. 8%) and decreased the number of strains identified with low confidence and with no identification to 937 (14. 9%) and 654 (10. 4%), respectively. For 19. 1% of the strains a higher log score was obtained. Adding all ENRIA MSPs increased the amount of strains identified with high confidence even further, to 4999 (79. 2%), whilst substantially decreasing those that identified with low confidence to 852 (13.
5%). The number of strains with no identification decreased to 458 (7. 3%). Therefore, a higher confidence of identification was obtained for 35. 2% of the strains upon adding all ENRIA MSPs. The observed increase in genus/species identification differed by genus. Bilophila wadsworthia was only represented in the V5 database at the genus level as 'Bilophila spp'. The addition of MSPs of clinical isolates resulted in an increase of the number of strains with a log score 2 from 2 strains to 17 strains, respectively. The addition of MSPs of two species of Lachnoanaerobaculum, namely Lachnoanaerobaculum orale and Lachnoanaerobaculum umeaenense, ultimately resulted in a high confidence identification of all tested strains, whereas previously only 5 of the 9 strains (55. 6%) could be identified.
Using the V5 database, 79. 2% of the Prevotella strains were identified with high confidence. This increased to 85. 7% and 91. 0% by adding the confirmed ENRIA MSPs and all ENRIA MSPs, respectively, partly due to the addition of MSPs of species that were not represented in the database. 69. 2% of the strains belonging to the GPAC genera were identified with high confidence using the V5 database. Adding confirmed ENRIA MSPs and all ENRIA MSPs resulted in a high confidence identification for 76. 5% and 86. 4% of the strains, respectively. This increase was due in particular to the addition of MSPs of species (valid and non-valid) not yet represented in the database, such as Peptoniphilus duerdenii, Peptoniphilus tyrrelliae, 'Peptoniphilus rhinitidis' and Peptostreptococcus stomatis. Increasing the number of MSPs of Anaerococcus vaginalis gave a significant increase for strains identified with high confidence, from 12.
1% to 85. 0% using the V5 database and all ENRIA MSPs, respectively. With the latter database, a higher log score was obtained for 100% of the A. vaginalis strains. The amount of Porphyromonas strains identified with high confidence also increased mostly due to the addition of P. asaccharolytica/uenonis ENRIA MSPs (from 53. 6% using the V5 database to 80. 0% by including all ENRIA MSPs). Several less common species could now be identified using the V5 database optimized with all ENRIA MSPs, including strains of Dielma fastidiosa, Moryella indoligenes, 'Fenollaria massiliensis', Filifactor alocis and Odoribacter splanchnicus.
16S rDNA gene sequencing Of the 6309 analyzed strains, 458 were not identified using MALDI-TOF MS. To assess the reason for this, 193 of these strains were sequenced by the originating laboratories. Species for which no identification was obtained for more than one strain are shown in Table  3. A portion of these strains gave no spectrum during MALDI-TOF MS analyses and these were mostly species belonging to the genus Actinomyces and Propionibacterium/Cutibacterium (data not shown). For other species no identification was obtained due to the fact that they are not represented in the MALDI-TOF MS database; e. g. Eisenbergiella tayi, Anaerovorax odorimutans and Akkermansia muciniphila. Other species are represented in the database, but not identified by MALDI-TOF MS; e. g. Dialister pneumosintes, Eggerthella lenta, Atopobium minutum and Anaerococcus prevotii.
Discussion In this study, we validated the Bruker MALDI-TOF MS database, optimized for anaerobic bacteria by utilizing a large set of anaerobic strains isolated from human clinical specimens. Several European laboratories specializing in anaerobic bacteria collaborated as part of the validation, allowing us to include strains of species less commonly encountered in human infections from within the collaborators private collections. Identifications with high confidence increased by 3. 7%, from 71. 1% to 74. 8%, using the V5 database and confirmed ENRIA MSPs. Using V5 and all ENRIA MSPs, identifications with high confidence increased by 8. 1%, from 71. 1% to 79. 2%. The three most prevalent genera were Bacteroides (13. 5%), Cutibacterium (9. 4%) and Prevotella (8. 7%). For the first two genera,   [20]  demonstrated that optimizing the Biotyper database for the identification of Prevotella species resulted in a high confidence identification of clinical isolates of Prevotella of 89.
2%. More recently Gürsoy et al. [6]  validated the identification of species of oral origin and found that all strains were correctly identified at the low confidence level (log scores 1. 7e2) and to a high confidence species level for 88. 6%. Similar rates of high confidence identification for Prevotella species were observed in this study. After optimization with confirmed ENRIA MSPs the level of high confidence identifications increased from 79. 2% to 85. 7% and finally to 91. 0% when all ENRIA MSPs were added to the V5 database. Veloo et al. [17]  demonstrated that the number of GPAC strains identified with high confidence increased from 53. 6% to 82. 1% when the Biotyper database was optimized for GPAC species Table  3  An overview of species, of which more than one strain was encountered, which could not be identified using MALDI-TOF MS.
Species b All three strains were only identified at the genus level with a log score 2. and this was partly due to the addition of MSPs of species that were not yet represented in the database. Similarly in this study a significant increase was observed in the number of A. vaginalis strains which could, due to optimization, be identified with high confidence as opposed to not being identified at all. After adding all ENRIA MSPs to the V5 database, 86. 4% of the GPAC could be identified with high confidence, a similar proportion to the previous study. No studies have hitherto been performed on the validation of MALDI-TOF MS specifically for the identification of Porphyromonas species isolated from human clinical specimens, with the exception of Porphyromonas gingivalis, as this is an important periodontal pathogen  [14].
Therefore, commercial identification systems where the identification tables are not kept up to date are unlikely to cover these species. In order to successfully monitor the clinical relevance of these often unvalidated species we chose to add these to the ENRIA database. An example is 'Fenollaria massiliensis', originally isolated from an osteoarticular sample  [12]. This species is not validated and its clinical relevance is unknown. During our study we encountered seven strains of this species, which would previously have gone unrecognized in clinical laboratories. This example demonstrates that it is also important to have unvalidated species validated, especially if we wish to gain insight into the clinical relevance of such unusual new species. The importance of including less common species within the databases of MALDI-TOF MS systems is also emphasized by a study by Bernard et al.
[2]. They encountered eight isolates of E. tayi, isolated from blood cultures, concluding that this species is a potential pathogen. Creating MSPs for these strains ensured that this species can be identified using MALDI-TOF MS. The validation of the MALDI-TOF V5 database expanded using clinical isolates collected within the ENRIA project clearly demonstrated that an increase in the percentage of strains identified was achievable. It should be noted that not all species can be differentiated from each other, especially certain species of the genus Veillonella. We recommend the continual expansion of the MALDI-TOF MS database with MSPs of less common species, valid and unvalid, in order to gain insight into their clinical relevance.
[13]MALDI-TOF MS results of the validation using the optimized Biotyper database for anaerobic bacteria. inamatching hint of the software), and MSPs of these species are included in the collection of all ENRIA MSPs. As previously described by Pedersen et al. [13], the MALDI-TOF MS is not able to distinguish between B. vulgatus and B. dorei or between B. ovatus and B. xylanisolvens. Therefore these species are referred to as B. vulgatus/dorei and B. ovatus/ xylanisolvens. Wybo et al.
(230) 52 92 86 32 61 137 83 39 165 (continued on next page) A. C. M. Veloo et al. / Anaerobe 54 (2018) 224e230the number of identifications with high confidence did not increase significantly, as they were already well represented in the MALDI-TOF MS database. Within the V5 database the Bacteroides species B. dorei and B. xylanisolvens are not represented (they are however mentioned as 'cross-matching'.
Methods and results The model aims to be an electrical analog of the physiology of the eye, including various details regarding structural mechanics and fluid dynamics. Interestingly, pressure values inside the eye exhibit a static component, mainly due to the balance of aqueous humor (AH) inflow and outflow, and a dynamic component, mainly due to blood flow oscillations. We considered both static and dynamic components, and in particular, we investigated and quantified the influence of tissue (vitreous humor, cornea, and sclera) deformability and blood flow pulsatility on the dynamic measurements of IOP. Model calibration and validation have been performed using published data. The model has been implemented using the open access software OpenModelica (Open Source Modelica Consortium: Linköping, Sweden) in order to facilitate further model extensions and connections with other models already available for different parts of the eye.
Sequencing of SF3B1, NOTCH1, TP53, and BIRC3 Patients were first screened for mutations in SF3B1 exons 14, 15, 16, and 18 (NM_012433), NOTCH1 exon 34 (NM_017617), and BIRC3 exons 6 to 9 (NM_001165) by Sanger sequencing (BigDye Terminator cycle kit (Thermo Fischer Scientific, Waltham, MA) and ABI PRISM 3130xl Genetic Analyzer) after amplification of 50 ng genomic DNA. Sequences were analyzed with the Mutation Surveyor DNA Analysis Software V4. 0 (SoftGenetics, State College, PA). Patients not screened by Sanger sequencing were assayed by high-throughput sequencing (HTS) (Proton, Ion Torrent), using a panel that targeted the same regions. The TP53 gene was analyzed by HTS in all cases. The panels were designed on the AmpliSeq designer platform (www. ampliseq. com). Libraries were constructed using the Ion AmpliSeq Library kit 2.
Acknowledgments This work was supported by  "Ligue Nationale Contre le Cancer, équipe labellisée Ligue" and "Comité Orientation Recherche Cancer en Limousin" (CORC). We thank  Dr Jean-Baptiste Woillard  ( Pharmacology Department of Limoges University Hospital, France ) for having supervised the statistical analysis, and  Dr Jeanne Cook-Moreau  ( UMR CNRS 7276 ,  France ) for careful English editing.
2, 150 mM NaCl, 10 mM EDTA, 10 mM Na 4 P 2 O 7 , 2 mM Na 3 VO 4 , and 1% Triton X-100 supplemented with Protease Inhibitor Cocktail (Roche). The primary antibodies and dilutions used were: UCP1 (1/500, Abcam), Tubulin (1/ 10,000, Sigma); secondary horseradish peroxidase-conjugated antibodies were purchased from Sigma. Quantitation of protein signal intensity was performed by volume densitometry using the ImageJ 1. 47t software (National Institutes of Health). PKA activity was measured on tissue homogenates using the non-radioactive PKA Kinase Activity assay kit following manufacturers' instructions (Abcam).
Prior to incubation with the kinase reaction mix, the arrays were blocked with 2% BSA in water for 30 cycles and washed 3 times with the PK assay buffer. Kinase reactions were performed for 1 h with 2. 5 mg of total extract of the mature adipocyte and 400 mM ATP at 30 C. Phosphorylated peptides were detected with an anti-rabbit-FITC antibody that recognizes a pool of anti-phospho serine/threonine antibodies. The instrument contains a 12-bit CCD camera suitable for imaging of FITC-labeled arrays. The images obtained from the phosphorylated arrays were quantified using the BioNavigator software (PamGene International BV), and the list of peptides whose phosphorylation was significantly different between Cdkn2aþ/þ and Cdkn2aÀ/À was uploaded to IPA for pathway analysis. The BioNavigator software was used to perform the HPRD based upstream kinase determination that is shown in Figure  4C.
2. 9. RNA sequencing Total RNA was extracted from primary adipocytes 8 days after differentiation using the RNeasy Microkit (Qiagen) following manufacturer's instructions. RNA quality was verified using RNA 6000 nanochips on the Agilent 2100 bioanalyzer. Purified RNA (200 ng) with RNA integrity number !8 was subsequently used for library preparation (TruSeq Stranded mRNA Library Preparation Kit, Illumina) and sequenced on a HiSeq2500 (Illumina). Three biological replicates per condition were sequenced in paired-end. A mean of 54 million pairedend reads of 75 bp were generated for each sample. After initial checks and validation of sequence quality, RNA-seq reads were aligned to the mouse reference genomes (mm10) using TopHat2  [33]. Subsequently, both quantification and annotation of the reads were performed using Bioconductor package Rsubread  [34].
Seahorse experiments Oxygen consumption rate (OCR) was performed using the XF24e platform from Seahorse Biosciences. Primary adipocytes from Cdkn2aþ/þ and Cdkn2aÀ/À mice were grown and differentiated in a Seahorse plate. Growth medium was replaced with 37 C seahorse DMEM 1 h before the measurement to allow equilibrium. Measurement was performed at 37 C, using 3e3e3-min intervals. Basal respiration was measured then uncoupled, and maximal OCR were determined using oligomycin and carbonyl cyanide p-trifluoromethoxyphenylhydrazone (FCCP, 1 mM each), respectively. Complex I-dependent respiration was inhibited with rotenone (3 mM). OCR data were normalized using total protein or DNA content as indicated.