dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Pancreatic carcinoma is one of the most lethal human cancers. In patients with resectable tumors, surgery followed by adjuvant chemotherapy is the only curative treatment. However, the 5-year survival is 20%. Because of a strong metastatic propensity, neoadjuvant chemotherapy is being tested in randomized clinical trials. In this context, improving the selection of patients for immediate surgery or neoadjuvant chemotherapy is crucial, and high-throughput molecular analyses may help; the present study aims to address this. Methods: Clinicopathological and gene expression data of 695 pancreatic carcinoma samples were collected from nine datasets and supervised analysis was applied to search for a gene expression signature predictive for overall survival (OS) in the 601 informative operated patients.
The signature was identified in a learning set of patients and tested for its robustness in a large independent validation set. Results: Supervised analysis identified 1400 genes differentially expressed between two selected patient groups in the learning set, namely 17 long-term survivors (LTS; ≥ 36 months after surgery) and 22 short-term survivors (STS; dead of disease between 2 and 6 months after surgery). From these, a 25-gene prognostic classifier was developed, which identified two classes ("STS-like" and "LTS-like") in the independent validation set (n = 562), with a 25% (95% CI 18-33) and 48% (95% CI 42-54) 2-year OS (P = 4. 33 × 10 -9 ), respectively. Importantly, the prognostic value of this classifier was independent from both clinicopathological prognostic features and molecular subtypes in multivariate analysis, and existed in each of the nine datasets separately.
A few prognostic gene expression signatures have been developed  [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] , in general from small sample series and without validation in independent sets, or with validation in limited tumor sets. Biologically relevant molecular subtypes have been identified  [16, 25, 26] , and associated with OS  [27]. However, identifying molecular predictors to aid in patient care remains necessary. Here, we collected data of 695 pancreatic carcinoma samples from gene expression datasets, and searched for a gene expression signature predictive for post-operative OS.
Gene expression datasets We retrospectively collected clinicopathological and gene expression data of clinical pancreatic carcinoma samples from nine publicly available datasets  [15, 16, 20, 21, 23, 25, [28] [29] [30]  from the National Center for Biotechnology Information/Genbank Gene Expression Omnibus, ArrayExpress, European Genome-phenome Archive, and The Cancer Genome Atlas (TCGA) databases (Additional file 1: Table  S1 ). Samples had been profiled using whole-genome DNA microarrays (Affymetrix or Agilent) and RNA-Seq (Illumina). The complete dataset contained 695 samples, including 601 operated primary cancer samples with available survival data. The study was approved by our institutional board.
Gene expression data analysis Data analysis required pre-analytic processing. First, we normalized each DNA microarray-based dataset separately, by using quantile normalization for the available processed Agilent data, and Robust Multichip Average  [31]  with the non-parametric quantile algorithm for the raw Affymetrix data. Normalization was performed in R using Bioconductor and associated packages. Then, we mapped hybridization probes across the different technological platforms. We used SOURCE  [32]  and NCBI EntrezGene  [33]  to retrieve and update the Agilent annotations, and NetAffx Annotation files  [34]  for the Affymetrix annotations. The probes were then mapped according to their EntrezGeneID. When multiple probes represented the same GeneID, we retained the one with the highest variance in a particular dataset.
For the TCGA, Bailey's and Kirby's data, we used the available normalized RNA-Seq data that we log 2 -transformed. We defined the molecular subtypes of all pancreatic cancer samples in each dataset separately as defined in the original publications, i. e. , the three Collisson's subtypes  [16]  were classical, quasi-mesenchymal, and exocrine-like, the two Moffitt's epithelial subtypes  [26]  were basal-like and classical, and the four Bailey's subtypes  [25]  were squamous, pancreatic progenitor, immunogenic, and aberrantly differentiated endocrine exocrine (ADEX). To identify a prognostic expression signature, we applied a supervised analysis using learning and validation sets. The learning set was a subset (n = 39) of the Bailey's and TCGA RNA-Seq datasets that included samples from patients with survival of at least 36 months after surgery (long-term survivors (LTS); n = 17) and from patients dead of disease between 2 and 6 months after surgery (shortterm survivors (STS); n = 22).
The 562 other samples with available survival data from the other datasets were gathered and used as an independent validation set. Samples of the learning set were pooled before supervised analysis by using COMBAT (empirical Bayes), included in the inSilicoMerging R/Bioconductor package, as a batch effects removal method. The final merged set included 15,291 genes in log2-transformed data. The accuracy of normalization was controlled by principal component analysis (Additional file 2: Figure  S1 ). The supervised analysis compared the expression profiles of 15,291 genes between the 22 STS samples and the 17 LTS samples using a moderated t-test with empirical Bayes statistic included in the Limma R packages. False discovery rate was applied to correct for the multiple testing hypothesis and significant genes were defined by the following thresholds: P < 5%, false discovery rate < 25%, and fold change superior to |2x|.
Ontology analysis of the resulting 1400-gene list was based on the gene ontology (GO) biological processes of the Database for Annotation, Visualization and Integrated Discovery (DAVID)  [35]. We then developed a prognostic classifier while minimizing the number of retained genes. Starting from the resulting 1400-gene list, we used logistic regression analysis with Least Absolute Shrinkage and Selection Operator  [36]  (LASSO), which is a selection method that handles highdimensional regression variables with no prior feature selection step by shrinking all regression coefficients toward zero, and thus forcing many regression variables to be exactly zero. The penalty regularization parameter λ was chosen via the cross-validation routine cv. glmnet before running the main algorithm implemented in the R package glmnet version 1.
9-8, with an n-fold equal to 10. The λ value was finalized by using the lambda. min, which is the value of lambda giving minimum mean cross-validated error (lambda. min was 0. 0153). The resulting classifier allowed the definition of two classes of samples, namely the predicted STS-like class and the predicted LTS-like class. Its robustness was assessed in the independent validation set (n = 562) by classifying each sample in each dataset separately as STS-like or LTS-like. Since a few studies have indicated that many gene signatures were random noise signatures  [37, 38] , we evaluated whether our prognostic 25-gene signature was not inferior to random signatures. A resampling scheme was used to generate 100,000 random 25-gene signatures within the 1400 genes differentially expressed identified by supervised analysis in the learning set.
T1), lymph node status (positive vs. negative), grade (2, 3, and 4 vs. 1), our 25-gene classification (STS-like vs. LTS-like), and the different molecular subtype classifications. Variables with a P value lower than 0. 05 were tested in multivariate analysis. All statistical tests were two-sided at the 5% level of significance. Statistical analysis was performed using the survival package (version 2. 30) in the R software (version 2. 15. 2)  [39]. We followed the reporting REcommendations for tumor MARKer prognostic studies (REMARK criteria)  [40]. A Sweave report describing the analysis of gene expression data and the associated statistical analysis is available as Additional file 3 (Supplementary Text).
Patient population We collected nine retrospective/prospective public whole-genome mRNA expression datasets of 695 pancreatic samples, and focused our analysis on the 601 cancer samples from patients operated from the outset and with available survival. As shown in Table  1 , the majority of patients were aged 60 years or older, and 54% were male. Most cases (96%) were AJCC stage 1 or 2, ductal type (98%), and grade 2 (55%). All but one case had been treated by front-line surgery, and the majority of tumors were pT2 (16%) or pT3 (77%), and pN-positive (69%). All Bailey's, Moffitt's, and Collison's molecular subtypes were represented. A total of 354 patients died. The median OS was 20 months (range, 1-156), and the 2-year OS was 40% (95% CI  [36] [37] [38] [39] [40] [41] [42] [43] [44] [45].
Identification of a prognostic expression signature We searched for a gene signature associated with OS. Supervised analysis was performed in a learning set of 39 samples selected to represent the two opposite groups of patients, including 17 LTS and 22 STS. Analysis identified 1400 genes differentially expressed between the two groups (Additional file 4: Table  S2 ). All associated GO biological processes are shown in Additional file 5: Table  S3 , and the top 40 processes are shown in Table  2. The robustness of those genes was tested by testing their ability to classify the LTS and STS samples from the other independent datasets. Out of the 67 samples classified, 49 (76%) were accurately classified, suggesting strong robustness (P = 7. 68 × 10 -5 , Fisher's exact test). To render this signature more easily applicable in clinics, we built a multigene classifier from the 1400gene list.
Validation of the 25-gene classifier and clinicopathological associations We tested the 25-gene prognostic classifier in the independent validation set of 562 patients whose clinicopathological characteristics were close to those of the learning set (Additional file 7: Table  S4 ) and with a 2-year OS of 39% (95% CI 35-44; Fig. 1a ). The classifier sorted the 562 patients into two classes, STS-like (n = 216; 38%) and LTS-like (n = 346; 62%), with a 2-year OS of 25% (95% CI 18-33) and 48% (95% CI 42-54), respectively Interestingly, in each of the nine datasets separately, the 2year OS was shorter in the STS-like class than in the LTSlike class, and the difference was or tended to be significant (Additional file 8: Figure  S3 ). To assess the likelihood of our 25-gene signature as a non-random signature, we generated by a resampling scheme 100,000 random gene signatures from the list of 1400 genes differentially expressed and tested their prognostic value in the validation set.
To our knowledge, this study is by far the largest prognostic study of gene expression profiles in pancreatic carcinoma. Gene expression profiling remains today the most promising and successful high-throughput molecular approach to identify new prognostic tools in early-stage cancers. Multigene signatures are already marketed, such as Oncotype™ in breast cancer or Coloprint™ in colon cancer, yet no similar signature is available in pancreatic carcinoma. The paucity of tumor specimens available for analysis explains the relatively small number of samples profiled in previous prognostic studies, with 102 samples in the largest one  [20]  to use supervised analysis, and 328 in the Australian ICGC study  [25] , which identified prognostic molecular subtypes by unsupervised analysis. We overcame the problem by pooling nine public datasets, representing a total of 601 operated primary cancers with available follow-up, and allowing the use of a learning set and a validation set in the supervised analysis.
Interestingly, and by contrast to the other published studies in the field, this prognostic value existed in each of the nine datasets considered separately. As expected, the other variables significant in univariate analysis included the AJCC stage and the pathological lymph node status. The pathological type (other vs. ductal) was not significant (HR 0. 36 with P = 0. 151) because of the small percentage (1%) of "other" types. Interestingly, all six "other" type samples were classified in the LTS-like class, in agreement with the better prognosis of neuroendocrine tumors. Importantly, the OS analysis was not modified when limited to the 504 ductal samples with a 27% 2year OS in the STS-like and 48% in the LTS-like. Our 25-gene classifier displayed an independent prognostic value. Interestingly, it outperformed the molecular subtypes in multivariate analysis and identified patients with shorter and longer survival in all subtypes but one, highlighting substantial heterogeneity in each of them.
Conclusions We have identified a 25-gene classifier associated with post-operative OS independently of classical prognostic factors and molecular subtypes. The strength of our study lies in the size of the series, the robustness of the classifier in a large and multicentric validation set and in each dataset separately, its independent prognostic value, its non-random nature, and the biological relevance of the included genes. The small number of genes should facilitate the clinical application of the classifier by using other transcriptional tests applicable to formaldehydefixed paraffin-embedded samples such as qRT-PCR, RNA-scope™ or Nanostring™ technologies. Limitations include the retrospective nature of our series and associated biases. Despite the very high P values, the HR for death was relatively low, around 2, in both uni-and multivariate analyses, and therefore of uncertain clinical value.
Phenotypic data Data were provided by the Lombardy Breeders Association (Milano, Italy) and comprised 980 725 monthly test-day of milk (kg/day) and SCCs from 29 535 primiparous (n = 10 964) and pluriparous (n = 18 571) Holstein cows. Records were collected from 179 herds and included calvings from 1998 to 2013. Only the first four lactations were used and records were retained if between 10 and 400 days in milk (DIM). A minimum of 10 test-days/cow were required An alternative case-control design for mastitis and if a cow had more than two lactations only the last 22 test-days were retained (e. g. if a cow had three calvings only the last two calvings were retained). After editing the data set included 146 160 records from 7662 cows: 67 062 records for first parity (45. 9%), 63 115 records for second parity (43.
2%), 13 375 records for third parity (9. 2%) and 2608 records for fourth parity (1. 8%). To obtain a distribution that is closer to normal, SCC data were transformed to LS as follows  (Ali and Shook, 1980) : Linear ScoresðLSÞ = log 2 SCC 100 + 3 Edited data were used to estimate for each cow a LS value adjusted for a set of fixed and random effects fitting the following repeatability mixed model: Y ijlmnopqrs = μ + T D i + HERD j + CDIM l + Y S m + PARITY n + AGE o + bp MILK ð Þ + PE q + SIRE r + ϵ ijlmnopqrs ð1Þ where Y ijlmnopqrs is the test-day record of LS, µ the overall mean of edited records, T D i the fixed effect of test-day, HERD j the fixed effect of the herd, CDIM l the fixed effect for class of DIM (defined as 1 class for every 30 days, resulting in 14 classes), Y S m the fixed effect for year and season of calving, PARITY n the fixed effect for the parity effect, AGE o the fixed effect of age at calving and b p the regression coefficient associated with the fixed effect of milk yield (MILK).
The random environmental effect between consecutive test-day records within each lactation of a cow was included in the term PE q. Population stratification was accounted for by including an uncorrelated sire (SIRE r ) random effect and ε ijlmnopqrs the residual error. The R software (http://cran. r-project. org) and the lmer function from lme4 package  (Bates et al. , 2015)  were used for all the data editing and analyses. Next, a two-step procedure was used to identify HI v. LI herds and control and case animals, respectively, within the two herd types. Herd classification was based on the percentage of animals that showed individual high or low mean SCC values. A value of 200 000 cells/ml was used as a threshold to identify a possible case of mastitis or suggest an ongoing bacterial infection  (Bradley and Green, 2005; Madouasse et al.
, 2012). Using historical SCC herd data, herds with >30% of the animals averaging above 200 000 cells/ml were classified as HI herds. Then, herds with <30% of animals above the threshold, on average, were classified as LI herds. Of the entire data set, 69 and 50 herds were classified as HI and LI herds, respectively. Subsequently, individuals to be included in the association study were selected from these herds, identifying animals with high adjusted LS (case animals) in LI herds and animals with low adjusted LS (control animals) in the HI herds. An adjusted LS threshold equal to 0 was used to cluster animals in case and control groups. If a cow from a LI herd had a positive adjusted LS value, she was included in the 'case' cluster, and if a cow from a HI herd had a negative adjusted LS value she was included in the 'control' cluster.
Association analysis Association analysis was conducted using the R statistical environment with the 'qtscore' function of the package GenABEL  (Aulchenko et al. , 2007). The response variable, namely the adjusted LS value, was analysed by fitting a single-SNP regression model. The significance of an association between a given SNP and the response was measured using a likelihood-based score test  (Schaid et al. , 2002). In order to deal with multiple testing a Bonferroni correction was applied to uncorrected P-values. Bonferroni correction is used to change the threshold value (α) = 0. 05 against which the P-value is compared, into α/k (0. 05/k) where k is the number of statistical tests performed.
Data analysis A two-state switching Bayesian state space model (BSSM) was fitted to the SPOT tag data that allows movement parameters to switch between two behavioural states, resident and traveling, in discrete time and within a hierarchical framework to improve parameter and location estimation  [36]. The BSSM was implemented using the package bsam v0. 44  [37]  in R v3. 1. 1 (R Development Core Team, 2014) by running Markov Chain Monte Carlo (MCMC) methods using Just Another Gibbs Sampler (JAGS) at a time-step of eight hours. To estimate movement parameters and behavioural mode, two MCMC chains were run for 40,000 iterations with a 30,000 sample burn-in and thinned every 10 samples leaving every 10 th sample from the remaining 10,000 for parameter estimation. Autocorrelation was assessed visually via autocorrelation plots and chain convergence for each parameter was estimated by Gelman and Rubin's potential scale reduction factor which was < 1.
1 for all parameters signifying convergence. The BSSM calculates a continuous probability behavioural state value between 0 (transient) and 1 (resident) where a value >0. 5 is presumed to exhibit resident behaviour. Location estimates from the BSSM were then used to calculate home ranges at 50% and 95% kernel utilization distributions (KUD) using the AdehabitatHR package v0. 4. 13  [38]  in R. Home ranges were calculated separately for each individual and then for all individuals combined. To assess spatial use relative to the proposed development, the number of locations within each 5 km distance interval from Mukkawar Island were isolated. Distance intervals from the proposed development site were established as polygons using the 'Buffer Tool' in ArcMap 10. 2 1 (ESRI, Redlands, California), and the number of points within each distance interval were identified using the 'Select by Location' function.
Although the number of individuals tagged for this study is relatively low, the longevity of the tracking and the urgent need for data from this highly understudied region highlights their importance. The manta rays tagged in this study are a subset of individuals sampled for genetics and implanted with acoustic transmitters. It is anticipated that continued investigations will further elucidate the behaviours and dynamics of this regionally important population. Future investigation will increase sample size to help validate, and further describe, the spatial ecology of manta rays in the region. Consistent use of coastal and reef areas by reef manta rays has been documented throughout the globe, including, Mozambique  [39] , Hawaii  [40] , and Australia  [12, 41]. For example, across the Red Sea, off the coast of Saudi Arabia, reef manta rays demonstrated site fidelity to specific reef sites, exhibited regional movements up to 200 km from the tagging site  [42] , and occupied the surface waters during the daytime and deeper waters at night  [43].
In Egypt, the rapid influx of capital investment and western amenities associated with the tourism industry resulted in a dilution of traditional culture, which would be favorable to avoid or minimize in Sudan. In Indonesia, it is estimated that manta ray-related ecotourism generates ~US$15 million per year  [11, 50]. In the Red Sea itself, it has been estimated that a single shark can be worth as much as US$200,000 in neighbouring Egypt's scuba diving ecotourism economy, with the success of elasmobranch dive sites such as the Three Brothers  [51]. Given the relative scale of operations in the Sudan, the revenue potential would be much lower than in Egypt. However, in a region of very limited employment opportunities, this natural resource represents an important economic asset. Although these preliminary data suggest the DMNP provides legislative protection to this regional manta ray population, in reality there is a considerable lack of resources for active enforcement.
It is hoped, however, that the recent Sudanese-led designation of the DMNP as an UNESCO World Heritage Site would make it unlikely that these development plans will proceed as previously planned, but national and international awareness of this proposed development and its potential impacts is essential. With government stakeholders currently debating vastly contrasting tourism trajectories, providing important data about the spatial use of key charismatic/flagship species, such as reef manta rays, is crucial to contribute to the decision-making process and support more sustainable options.
Acknowledgments This research forms part of the  Equipe Cousteau Sudan Shark and Ray Conservation and Management Program. We thank  N. Stroh  ( Equipe Cousteau ),  C. Scarpellini  and regional dive operators, the  Wildlife Conservation General Administration (WCGA) , the  Red Sea State Government , the  Red Sea University , the Security Administration, and the field team from  KAUST. We would finally like to thank the editor,  Heather Patterson , and two reviewers,  Rachel Graham  and  Jonathan Werry , for their thorough and highly constructive comments on the earlier draft of this manuscript, which improved the current version.
Object Naming: Stimuli and Procedure The participants performed an object naming task with 80 black and white drawings of objects and animals (DO-80 test;  Metz-Lutz et al. , 1991)  using a blocked design alternating four task and control periods. Twenty images lasting 2 s each with an inter-stimulus interval of 500 ms were presented during each task period, and participants were instructed to name the images as accurately and rapidly as possible. During each control period, 20 simple images (circles and squares) were presented and participants were instructed to respond either ''square'' or ''round. '' Stimuli were displayed using E-prime (E-prime Psychology Software Tools Inc. , Pittsburgh, PA, USA) and were projected onto a screen behind the magnet using a video projector. Oral responses were recorded via an MRI-compatible microphone (FORMI TM II, version 1.