dataesr/scientific-paragraphs-categorization huggingface dataset:
Purpose We and others have demonstrated that MYC-amplified medulloblastoma (MB) cells are susceptible to class I histone deacetylase inhibitor (HDACi) treatment. However, single drug treatment with HDACi has shown limited clinical efficacy. We hypothesized that addition of a second compound acting synergistically with HDACi may enhance efficacy. Methods We used a gene expression dataset to identify PLK1 as a second target in MB cells and validated the relevance of PLK1 in MB. We measured cell metabolic activity, viability, and cycle progression in MB cells after treatment with PLK1-specific inhibitors (PLK1i). Chou-Talalay synergy calculations were used to determine the nature of class I HDACi entinostat and PLK1i interaction which was validated. Finally, the clinical potential of the combination was assessed in the in vivo experiment.
Immunoblotting Immunoblotting was conducted as published previously  [12]. Antibodies are listed in Supplementary Table  3. Images were acquired and enhanced with Azure c400 imaging system (Azure Biosystems Inc. , Dublin, CA, USA) and cropped using Inkscape 0. 92. 4 software (open source). Protein expression was quantified using Infan-View v4. 54 (Irfan Skilijan) and ImageJ v1. 52 (NIH, Bethesda, MD, USA). Protein expression was backgroundadjusted and normalized.
Gene expression profiling and gene set enrichment analysis Total RNA was isolated using RNeasy Mini Kit (Qiagen) after 6 h of 5 µM entinostat, 1 µM volasertib, or combinatorial treatment. Microarray analysis was done at the Genomics and Proteomics Core Facility at the German Cancer Research Center (DKFZ) using the Affymetrix Human U133 Plus 2. 0 GeneChip according to the manufacturer's instructions. Data was analysed as described in the Supplementary materials and methods.
The target PLK1 is expressed in MYC-amplified cell line models and primary tumors To investigate the PLK1 presence, we analyzed its mRNA and protein expression in both primary samples and cell models of MB. The analysis of PLK1 mRNA expression in publicly available MB gene expression data sets showed significantly higher expression in MBs compared to cerebellum in all 4 molecular groups, particularly in SHH and group 3 (Gilbertson, n = 76, Fig. 1e ). PLK1 mRNA expression levels were highest in group 3 subgroup ii (equivalent to gamma, Cavalli, n = 763, Fig. 1f , Pfister, n = 223, Fig. 1g ) associated with MYC-amplification  [2, 5]. Recurrent group 3 MB showed the highest PLK1 mRNA expression compared to other recurrent pediatric tumors analyzed in the INFORM database  [27]  (Fig. 1h ). A significant positive correlation of PLK1 to MYC mRNA expression with an r-value > 0.
Validation of PLK1 and class I HDAC inhibition in MYC-amplified MB The subsequent experiments focused on volasertib due to advanced clinical development (Suppl. Table  5 ). Volasertib Fig. 1  PLK1 is a relevant target in MB. a Volcano plot showing differential mRNA expression in HD-MB03 MB cells treated with 5 µM entinostat for 6 h, MYC target genes (HALLMARK_MYC_TAR-GET_V1 and V2  [26] ) are marked in dark red. b Target gene filtering scheme. c MYC, H3K27ac and RNAPolII ChIP signal peaks around PLK1 TSS in primary MB tumors  [12]. d MYC, H3K27ac and RNAPolII ChIP signal peaks around PLK1 TSS in HD-MB03 MB cells treated with solvent or 5 µM entinostat  [12]. e PLK1 mRNA expression in groups of MB (MB dataset: Gilbertson (n = 76)  [48] , cerebellum dataset: Kool (n = 10)). f PLK1 mRNA expression in three group 3 subgroups of MB (Cavalli (n = 763)  [2] , equivalent subgroup in parentheses as suggested previously  [6] ).
g PLK1 mRNA expression in MB group 3 and 4 subgroups of medulloblastoma (Pfister (n = 223)  [5] , difference from the reference subgroup II is not significant). h PLK1 mRNA expression in pediatric cancer entities in INFORM database  [27]  (yellow line: group 3 MB median value). i PLK1 and MYC mRNA expression correlation analysis in WNT, SHH, group 3 and group 4 of MB (Cavalli (n = 763)  [2] ; linear regression analysis with Pearson correlation coefficient). j Relative PLK1 protein abundance in Archer  [28]  and Forget  [29]  datasets (difference to the reference group 3a is not significant). k Overall survival (OS) differences between PLK1 mRNA high (above Q3), low  (   treatment of MB cell lines decreased the number of viable cells significantly in a concentration and MYC statusdependent manner (Fig.
Acknowledgements We thank  Carina Müller  and  Isabel Büdenbender  for excellent technical assistance,  Norman Mack ,  Dr. Julia Zaman  and  Dr. Stefan Pusch  for their scientific advice, the  Microarray Unit of the Genomics and Proteomics Core Facility ,  German Cancer Research Center (DKFZ) , for providing excellent  Expression Profiling services, Center for Preclinical Research of DKFZ, Flow Cytometry Core Facility of DKFZ and Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital , for their support during this study.
Genomic analysis allowed the identification of RNA editing for miR-BART6 and the non-coding gene BHFL1  [157]. On 29 November 2022, more than 1200 entire or partial EBV genomic sequence data were available in the public National Center for Biotechnology Information database  [155, 158]. This large number of sequences provides new opportunities for genomic comparison in which their frequent recombination could be the driver of viral evolution  [159]. Next-generation sequencing could facilitate the exploration of potentially pathogenic variants in EBV-associated diseases  [4, 160]. In NPC, whole genome sequencing revealed mutational hotspots in LMP-1 and EBNA-1, thus suggesting that these latent genes are crucial for the pathogenesis of this carcinoma  [161]. Recent studies have investigated the use of viral miRNA as potential biomarkers in EBV-associated diseases.
They are divided into two subclusters: subcluster one that includes miR-BART 1, 3-6, and 15-17, and subcluster two that consists of miR-BART 7-14 and 18-21. They are expressed in latency type III infections  [162]. A meta-analysis concluded that miRNA expression increased the mortality risk in NPC and GC patients by threefold (hazard ratio: 3. 168; 95% CI: 2. 020-4. 969), meaning they may be used as prognostic biomarkers. Based on the survival data of 711 NPC and 59 GC patients, the meta-analysis investigated a total of seven EBV miRNAs, which were all upregulated  [163]. A better understanding of EBV-miRNA expression in EBV-associated GC and NPC will open up new avenues for GC and NPC treatment  [163, 164]. Circular RNAs are another class of predominantly non-coding RNAs, which may play a role in the viral biology and pathogenicity of EBV-associated malignancies.
Climate. From the results above, we postulated that the winter of 2009 led to the failures for both the 2009 and 2010 cohorts. Consequently, we explored whether any environmental variables showed extreme values during that period (average of July-August 2009). Sea ice coverage was extensive during that winter, but did not show the highest value of the dataset (Fig. 5 ). All other environmental variables showed approximately average values during the winter period. Finally, it is noteworthy that all variables were average in February during both years, when foraging behavior was assessed (Fig. S2 ). Air temperatures at the colony during the winters 2009 and 2010 were also slightly below average (2009 = -0. 22 °C below average, 2010 = -0. 04 °C below average), but not extremely so compared to other years, such as 2005 (-0.
Colony counts during incubation. The colony was photographed with aerial images (either by helicopter or camera-fitted kites  21  in mid-December during five years between 1998 and 2009. This period corresponds to the middle of the incubation stage, so that a photo taken in December 2009 will be associated with the 2010 breeding season. To estimate the number of breeding pairs, the images were stitched together and incubating penguins were counted by hand based on the typical incubation posture  21. Foraging behavior during incubation. For the 2010 season, foraging data were obtained only during incubation, as high abandonment rates of tracked adults prevent the recovery of their data logger. It was then also decided to stop any telemetry of chick-rearing birds, given the breeding failure rates.
Consequently, only foraging data during incubation were used to compare the 2010 breeding season with other years. During nine years between 1999 and 2022, 44 individuals (including 7 in 2010) were equipped during incubation (December-February) with GPS or ARGOS tags from various manufacturers. From this data, the maximum foraging distance was calculated and used as a measure of foraging effort. During the same period, 37 individuals (two in 2010) were equipped with depth-loggers either from Wildlife Computer (Redmond, United States) or Technosmart (Colleverde, Italy). The number of foraging dives per day was calculated from this data and served as a metric of foraging effort. A foraging dive was defined as a dive with maximum depth > 50 m  22, 23. We also calculated the average number of undulations or wiggles in the dive per day as a measure of foraging success.
In the 2010 breeding season, when a high abandonment rate was apparent, an additional 37 breeding pairs from non-equipped individuals were monitored to better quantify incubation failure. In a similar fashion, 36 additional breeding pairs were monitored in 2011 for comparison. www. nature. com/scientificreports/ The exact sample size for all years is reported in the supplementary material (Table  S1 ), as well as in the results (Fig. 3 ). No data could be collected during incubation for the 2009 season. It is also noteworthy that egg abandon rate, trip length and mass gained per day can all have sample sizes slightly differing from the number of equipped individuals. Reasons explaining these occasional discordances are listed in the supplementary material (Table  S2 ). Foraging behavior during chick-rearing.
Foraging data in 2009 were only collected during the chickrearing period (February 2009). Four individuals were equipped with an ARGOS tag, while thirteen were equipped with a depth-logger. Maximum distance from the colony, number of dives per day, and number of wiggles per day were calculated during this breeding period as well as for all others between 1998 and 2022, except for 2010 (i. e. 24 years, with a total of 161 GPS/ARGOS-equipped birds, 173 depth-logger-equipped birds). Mass gained per day (n = 380 individuals) and trip length (n = 583 individuals) were also recorded for those individuals as well as for additional non-equipped birds. The total sample size of equipped individuals for all years during chick-rearing is reported in the supplementary material (Table  S3 ). Chick mass and survival.
Acknowledgements This data collection was possible thanks to the dozens of field technicians, often staying over a year in Kerguelen to collect data on the life cycle of king penguins. The authors are especially grateful to  M. Bastianelli ,  Y. Charbonnier ,  V. Chartendrault ,  A. Chevallier ,  A. Corbeau ,  L. Denonfoux ,  A. Fromant ,  M. Goepfert ,  T. Getti ,  T. Gouello ,  T. Lacombe ,  C. Marteau ,  F. Orgeret ,  R. Perdriat ,  B. Planade , and  J. Sereau. They also want to thank the following funding sources:  John & Pat Warham Studentship  from the  British Ornithologists' Union, Centre d'Étude Biologique de Chizé CEBC-CNRS UMR7372 , the  Vanier Canada Graduate Scholarships, Zone Atelier Antarctique et Terres Australes (ZATA-LTSER France ,  CNRS-INEE) ,  Terres Australes et Antarctiques Françaises ,  IPEV (Institut Polaire Français) , the Program No.
| Participants, SPES, and data acquisition As shown in Figure  S1 , we screened patients affected by focal drug-resistant epilepsy who underwent SEEG investigations at the "C. Munari" Epilepsy Surgery Center between January 2017 and October 2020. Patients were selected based on the following inclusion criteria: (1) >18 years of age, (2) at least one seizure evoked by SPES, and (3) at least five SPES pulses (out of 15) delivered before and after the seizure onset. We included only seizures showing both electrical and clinical (video-recorded ictal exam) manifestations. Each patient signed the informed consent to participate in the study, which has been approved by the local ethics committee (ID. 348-24062020, Comitato Etico Milano Area 3). Data were acquired through a Nihon Kohden Neurofax 1200 EEG Amplifier with a sampling rate of 1000 Hz.
| CCEP preprocessing and quantification The SEEG data were analyzed employing custom-made MATLAB scripts. The stimulation artifact was removed through a Tukey filter; then the signal was high-pass filtered (third-order Butterworth; cutoff: 1 Hz), split in epochs (from -300 to 700 ms), baseline corrected (from -290 to -10 ms), and visually inspected by a trained neurophysiologist (SR) to exclude electrical artifacts from further analyses. Finally, the activity of each contact was z-scored with respect to the mean and standard deviation (SD) of the concatenated baselines of all trials (ictal and interictal). The quantifications on the elicited CCEPs were performed between 10 and 150 ms to focus mainly on direct cortico-cortical connectivity. Contacts showing CCEPs exceeding 6 SD from the baseline-in at least one condition-were considered as responsive to SPES  14  and used for further analyses.
| RESULTS We retrospectively screened a database of 130 patients (see Figure  S1 ) and found 12 patients fulfilling the inclusion criteria-mainly because SPES rarely elicits seizures. From these 12 patients we retrieved 19 seizures evoked by SPES. Specifically, 12 seizures were elicited by SPES F I G U R E 1 Upper panels refer to MTL seizures; bottom panels refer to NC seizures. In both cases, A shows the location of the recording contacts (upper section, blue dots) and stimulating contacts (lower section, black dots) in the 3D reconstruction of the subject's brain surface. The activity of a responding contact is shown in B, together with the highlight on the interictal (gray) and ictal (red) period. C and D show the CCEP obtained from all SEEG contacts by averaging interictal trials and ictal trials, respectively.
, 2002). This has led some researchers to doubt that the TVAs are sensitive to vocal sounds, in general, and suggest that they are in fact Speech Areas, that is, responsive to the phonemic and/or semantic content of the input signal [e. g. , component 5 in  Norman-Haignere et al. (2015)  study]. Yet, other results indicate that even non-speech vocal sounds induce greater TVA activity than control sounds  (Bodin et al. 's, 2021)  or lead to above chance classification into vocal/non-vocal categories  (Rupp et al. , 2022) , suggesting a selectivity to this category of sounds in the TVAs. Here, we provide a perspective on this issue by performing additional analyses of a published dataset  (Bodin et al. 's, 2021) , in which the same number (n = 12) of individual speech and non-speech vocal sounds were used along with 24 non-vocal sounds.
Auditory stimuli The analysis was performed on fMRI events corresponding to a subset of the stimulus set used in  Bodin et al. 's (2021)  study. Two main categories of sounds were used: human voices and non-vocal sounds, each containing 24 stimuli, for a total of 48 sound stimuli. Each main category was divided into two subcategories of 12 stimuli, forming four subcategories in total (cf. Supplementary Table  1 ). Human voices contained both speech [sentence segments from the set of stimuli used in  Moerel et al. 's (2012)  study, n = 12] and non-speech vocal sounds [vocal affect bursts selected from the Montreal Affective Voices dataset  (Belin et al. , 2008) , n = 12]. Non-vocal sounds included both natural and artificial sounds from previous studies from our group  (Belin et al. , 2000; Capilla et al.
Decoding We tested whether support vector classification with a linear kernel [SVC:  Chang and Lin (2011) ] was able to predict, from beta values in primary auditory cortex (A1) and TVAs, whether fMRI events corresponded to the presentation of vocal or nonvocal sounds. We first tried this decoding using only speech vocal sounds and then using only non-speech vocal sounds. To have a balanced frequency in each category tested (n = 12), only half of the non-vocal sounds were used during classification. As the dataset consisted of sessions containing two functional runs during which a repetition of each stimulus was presented, we used a two-fold cross-validation, with each run serving successively as train and test sets. For each participant, the classifier was first trained on data from one functional run and tested on the other, and the other way around in a second fold.
Standardization To assess the contribution of either categorical or topographical differences in stimulus activation, activity patterns of each ROI (RSA: 48 stimuli × 57 voxels; SVC: 96 events × 57 voxels) were standardized using two methods before running RSA and SVC: a standardization along stimuli, where z-scores were computed for each voxel along the stimulus (RSA) or event (SVC) dimension [which is the default standardization in machine learning packages;  Pedregosa et al. (2011) ], and a standardization along voxels, where z-scores were computed for each stimulus (or event) along the voxel dimension (see Supplementary Figure  4 ). For RSA, standardization was performed on activity patterns before computing RDMs. For SVC, standardization was performed on all events (both runs) before splitting data in train-test sets.
Funding This work was funded by the  Fondation pour la Recherche Médicale  ( AJE201214  to PB), the  Agence Nationale de la Recherche  grants  ANR-16-CE37-0011-01  ( PRIMAVOICE ),  ANR-16-CONV-0002  ( Institute for Language, Communication  and the  Brain ) and  ANR-11-LABX-0036  ( Brain and Language Research Institute ), the  Excellence Initiative of Aix-Marseille University  (A * MIDEX), and the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation program  (grant agreement no. 788240 ).
Method Participants: Seventy-two right-handed native speakers of French, between 18 and 45 years old, participated in the experiment. They all reported having no hearing or speech disorders. Each of them gave written informed consent prior to experimentation, and they received either a course credit or 10 Euros for their participation. Half of them heard the target words in the right ear and the other half in the left ear. Materials: Forty-eight words with a CVCV syllabic structure were selected from VoColex, a lexical database of the French language  (Dufour et al. , 2002)  and were used as primes and targets. They have a mean frequency of 8 occurrences per million. 16 additional words with the same syllabic structure were selected and used as control primes. They have a mean frequency of 10 occurrences per million.
Because each target word was paired with three different primes (match, mismatch, control) and no participant was presented with the same target twice, three experimental lists were created. The three lists were then divided into two sub-lists so that each stimulus of the prime and target blocks was heard in its accented and unaccented version. Procedure: The participants were tested individually in a sound attenuated booth, and stimuli were presented over headphones at a comfortable sound level. The presentation of the stimuli was controlled by a computer thanks to the software E-Prime. In both the prime and the target blocks, participants were asked to make a lexical decision as quickly and accurately as possible with "word" responses using their dominant hand on a button-box that was placed in front of them.
Results Statistical analyses were performed on the target words. Two items that gave rise to an error rate of more than 40% were removed from the analyses. The mean RT and percentage of correct responses in each condition are presented in Figure  2. RTs (available at https://osf. io/x2trp/) were analyzed using linear mixed effects models with participants and items as crossed random factors, using R software (R Development Core Team, 2020) and the lme4 package  (Baayen et al. , 2008; Bates & Sarkar, 2007). The RT analysis was performed on correct responses, thus removing 459 data points out of 3312 (13. 86%). Following  Baayen and Milin (2010) 's recommendations, the model was applied to the complete set of correct RTs. Also, following  Baayen and Milin (2010) , for the model to meet the assumptions of normallydistributed residuals and homogeneity of variance, a log transformation was applied to the RTs prior to running the model.
The mass spectrometer was operated in the data-dependent acquisition mode to automatically switch between full-scan MS and MS/MS acquisition. Instrument control was through Tune 2. 7. 0 and Xcalibur 2. 2. The mass spectrometric data was acquired in positive ion mode, with an 1,800-V ion spray voltage, no sheath and auxiliary gas flow, and a capillary temperature of 260˚C. Survey full-scan MS spectra (from m/z 300 to 2,000) were acquired in the Orbitrap with a resolution of 240,000 at m/z 400 (after accumulation to a target value of 1e6 in the linear ion trap with the maximum allowed ion accumulation time of 300 ms). The 12 most intense eluting peptides above an ion threshold value of 3,000 counts and charge states of �2 were sequentially isolated to a target value of 1e4 and fragmented in the high-pressure linear ion trap by low-energy CID with a normalized collision energy of 35% and wideband-activation enabled.
Data management and analysis Four softwares were used for this study: MaxQuant v1. 5. 5. 1, Perseus v1. 5. 6. 0, SPSS v25, and Excel 2019. The raw files from the LC-MS/MS were analyzed using MaxQuant version 1. 5. 5. 1 and the integrated Andromeda search engine. The fasta file version was Sprot_Human_20432en-tries_20190903. fasta. Moreover, for both proteins and peptides, the maximum FDR was set to 0. 01  [16]. MaxQuant maps the sequences of detected peptides and uses these peptide levels to determine the identified protein level. Since the protein levels likely vary between samples due to minor differences in handling and analysis, normalization of protein levels is essential. Consequently, label-free quantification (LFQ) algorithms within MaxQuant, MaxLFQ, were used to create the normalized protein intensities  [16].
05. The data were normalized on the protein level with Z-scoring prior to hierarchical clustering. In addition, we performed a mixed linear model analysis with correction for multiple testing for comparison (S4, S5 Figs in S1 File). R-scripts and data output can be found in the S4 Table in S2 File and S1 Data. To visualize the results, a heat map was created to evaluate the significantly differently proteins' levels between the groups. The data were not imputed. The significantly expressed proteins of interest were further analyzed with IBM SPSS Statistics 25. Besides one-way ANOVA, the Tukey's range test was also carried out. After running one-way ANOVA and Tukey's range test in SPSS, box plots were constructed for each of the significantly expressed proteins to illustrate the mean and the median Log2 intensity differences of these proteins detected in the PBMCs of PTB patients at different treatment time points (0M, 2M and 6M).
Protein interaction and pathway analysis The STRING database (STRING v. 11. 0; www. string-db. org) was used to identify known and predicted functional networks and to predict protein-protein interactions. Gene ontology (GO) annotation (www. geneontology. org) was conducted to classify proteins based on biological process (BP), molecular function (MF) and cellular component (CC) using the Protein Analysis Through Evolutionary Relationships database (www. pantherdb. org). Pathway analysis and biological reactions were performed using Reactome version 72 (www. reactome. org).