dataesr/scientific-paragraphs-categorization huggingface dataset:
Toexamine the strength and significance of metabolite concentration changes within each of the three treatment arms, i. e. , (1) CBT (16 sessions); (  2 ) duloxetine (30-60 mg/d); or (3) escitalopram (10-20 mg/d), linear mixed effect models (with random intercept) with metabolite levels (in log scale) as the dependent variable, were fitted while correcting for age, sex, BMI, and baseline HRSD17. Then, the R package "emmeans" was used to compute the least squared means of the contrasts of interest (week 12 vs. baseline) and their corresponding p-values. To detect whether metabolites levels were associated with clinical outcomes, linear regression analyzes corrected for age, sex and treatment arm were performed. Dependent variables (Baseline HRSD17, Week 12 HRSD17, and 12 weeks change in HRSD17) were regressed on either of following independent variables: 1) baseline metabolite, 2) week 12 metabolite, 3) 2 weeks change in metabolites and 4) 12 weeks change in metabolites.
ACKNOWLEDGEMENTS Rotterdam Study: The  Rotterdam Study  is funded by  Erasmus Medical Center and Erasmus University, Rotterdam ,  Netherlands Organization for the Health Research and Development (ZonMw) , the  Research Institute for Diseases in the Elderly (RIDE) , the  Ministry of Education, Culture and Science , the  Ministry for Health, Welfare and Sports , the  European Commission (DG XII)  and the  Municipality of Rotterdam. The  Exome Sequencing  data set was funded by the  Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO)  sponsored  Netherlands Consortium for Healthy Aging (NCHA ; project number  050-060-810 ), by the  Genetic Laboratory of the Department of Internal Medicine , Erasmus MC and by the and by a  Complementation  Project of the  Biobanking and Biomolecular Research Infrastructure Netherlands  (BBMRI-NL; www.
bbmri. nl; project number  CP2010-41 ). We are grateful to the study participants, the staff from the  Rotterdam Study  and the participating general practitioners and pharmacists. The generation and management of the exome sequencing data for the Rotterdam Study was executed by the  Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, The Netherlands. We thank  Mr Pascal Arp ,  Ms Mila Jhamai  and  Mr Marijn Verkerk  for their help in creating the RS-Exome Sequencing database. SHIP-Trend: This work was funded by grants from the  German Federal Ministry of Education and Research (BMBF , grants  01ZZ0403 ,  01ZZ0103 ,  01GI0883 ,  Athero-SysMed 03IS2061B ), the  Ministry for Education, Research and Cultural Affairs , as well as the  Ministry of Social Affairs of the Federal State of Mecklenburg-West Pomerania.
This work is also part of the research project  Greifswald Approach to Individualized Medicine (GANI_MED ). The  GANI_MED  consortium is funded by the  Federal Ministry of Education and Research  and the  Ministry of Cultural Affairs of the Federal State of Mecklenburg-West Pomerania  ( 03IS2061A ). This work was also funded by the  German Research Foundation  (DFG:  GR1912/11-1 ). KORA : The  KORA study  was initiated and financed by the  Helmholtz Zentrum München -German Research Center for Environmental Health , which is funded by the  German Federal Ministry of Education and Research (BMBF)  and by the  State of Bavaria. Furthermore, KORA research was supported within the  Munich Center of   Health Sciences (MC-Health ),  Ludwig-Maximilians-Universität , as part of LMUinnovativ. K. S. was supported by the  Biomedical Research Program   at Weill Cornell Medicine in Qatar , a program funded by the  Qatar Foundation.
Data Sharing The datasets generated during and/or analyzed during the current study are not publicly available due to restrictions based on privacy regulations and informed consent of the participants. All summary statistics are provided in the Supplementary Material. For individual cohort level data, respective Principal Investigators can be contacted. For the Rotterdam Study data, requests should be directed towards the management team of the Rotterdam Study (secretariat. epi@erasmusmc. nl), which has a protocol for approving data requests. The EPIC-Norfolk data can be requested by bona fide researchers for specified scientific purposes via the study website (https://www. mrc-epid. cam. ac. uk/research/studies/epic-norfolk/). Data will either be shared through an institutional data sharing agreement or arrangements will be made for analyzes to be conducted remotely without the need for data transfer.
). Different versions of the platform have been used and details on the platforms are included in the Supplementary Materials. In each study, metabolites with KORA EPIC-Norfolk B2 EPIC-Norfolk B3 SHIP-Trend Rotterdam Study NEO n=1746 n=4639 n=5163 n=965 n=484 n=599 N(Metabolites)=276 N(Metabolites)=666 N(Metabolites)=734 N(Metabolites)=264 N(Metabolites)=850 N(Metabolites)=1069 Combining summary statistics Meta Analysis from all cohorts using sample size n=13,596 weighted (p-value based) meta- analysis Significant metabolites Mendelian Randomization Identification of Identification of food NIHR BioResource microbial sources of sources of (n=8809) metabolites using metabolites using GWAS of MDD AGORA database literature (n=500,199) Study the effect of Association of food antidepressant sources of therapy (CBT, SSRI, metabolites with SNRI) on metabolite depression in the in the UK Biobank PReDICT study n=500,000.
Acknowledgments This research was supported by the  National Research Foundation  under its  CREATE program  and the  Singapore-MIT Alliance for Research and Technology,   Future Urban Mobility Interdisciplinary Research Group. The work has been partially supported by the project " WEAKI-TRANSIT: WEAK-demand areas Innovative TRANsport Shared services for Italian Towns " (unique project code:  E44I17000050001 ) under the  Italian programme "PRIN 2017 ", by the project " ADDRESS " under the  University of Catania programme  " PIACERI Linea 2 " and by the research grant, funded by  Institut Francais (Ambassade de France en Italie) , received by  G. Calabrò.
Introduction Next generation sequencing has revolutionized our understanding of the molecular pathways that drive the development and progression of human cancers. 1  The establishment and expansion of large-scale cancer databases have allowed scientists to mine and analyze an unprecedented number of patient-derived datasets that could lead to the identification of targetable vulnerabilities. 2  Nonetheless, translation of these major findings into clinical practice has been slower than expected. This could be partly explained by the current dominant paradigm in drug discovery based on the identification of one specific disease-related target leading to the design of selective inhibitors/activators of this target. However, it is now well-established that most effective drugs act on multiple rather than single targets and combination therapy is the most effective strategy to treat complex diseases, such as cancers.
In particular, we provide the validation in an original ex vivo organotypic and an orthotopic syngeneic mouse models of the therapeutic potential of the dual AURKA/BET inhibition against glioblastoma. Implications of all the available evidence Our work opens new therapeutic avenues for glioblastoma patients. Given that Alisertib and Birabresib have been already tested in phase I clinical trial, the therapeutic potential of the dual AURKA/BET inhibition could be rapidly translated to the clinic. Our results also point out the interest of developing RRM1 inhibitors that can effectively cross the blood brain barrier for glioblastoma treatment. Importantly, all the datasets generated in our study, including our dual drug and RNAi screening, are available to the scientific community. This could appeal to clinicians and scientists working on glioblastoma biology and provide rationale for translational projects focusing on identifying pharmacological synergisms.
Cell culture The human GBM cell lines T98G (RRID: CVCL_0556), U87 (RRID: CVCL_0022) and its EGFR-mutated derivative U87vIII (RRID: CVCL_XZ66) were kindly provided by the Children's Cancer Institute (Sydney, Australia) while the murine GL261 (RRID: CVCL_Y003) GBM cell line was obtained from the German Collections of Micro-organisms and Cell Cultures (DSMZ, Germany; #ACC802). They were grown in Dulbecco's Modified Eagle Medium (Gibco, #11960044) containing 10% Fetal Calf Serum, 1% sodium pyruvate (Gibco, #11360070) and 1% penicillin-streptomycin (Gibco, #15140122). Fluorescently-labelled U87 and GL261 cell lines were established by transfection of an mtDsRed plasmid with Lipofectamine 2000 (Invitrogen, #11668019) according to the manufacturer's protocol, followed by geneticin selection (0. 8 mg/ml, Gibco, #10131035) and two cycles of fluorescence-activated cell sorting.
Focused siRNA library generation All the known targets and interactors of the pharmacological hits were listed by interrogating three complementary databases: DrugBank (RRID: SCR_002700 https://go. drugbank. com/), Gostar (https://www. gostardb. com/gostar/) and DRUGSURV. 16  This yielded a total of 1100 known targets and interactors, of which 292 were associated with at least 3 pharmacological hits. A focused siRNA library (Silencer™ Select pre-designed siRNA; ThermoFisher Scientific) was designed to individually silence the genes encoding the 292 targets/ interactors, with 3 different and specific siRNA sequences per gene.
After 15 min of complexation, GBM cell lines were seeded on top of the lipoplexes (1000 cells/well; final [siRNA] = 5 nM) and incubated for 3 days at 37 • C and 5% CO 2 in a humidified incubator. Cells were fixed with Paraformaldehyde 4% (Sigma, #1004969011), stained with Hoechst 33342 and plates were imaged on a High-Content Imaging device (Operetta HCS epifluorescence microscope, PerkinElmer). Three fields per well were acquired at 10× magnification in blue channel (excitation: 380 ± 20 nm; emission: 445 ± 35 nm). Hoechst-stained nuclear regions of interest were segmented using Harmony software (RRID: SCR_ 023543 PerkinElmer) and cell viability was expressed in each plate as the relative number of cellular events in sample wells relative to the average of NEG wells. Genes were considered as candidates when at least 2 out of 3 siRNA sequences gave statistically significant results in at least 2 GBM cell lines (Z-score scoring).
DepMap database analysis Using the Dependency Map portal (RRID: SCR_ 017655 https://depmap. org/portal/), we extracted from the CRISPR DepMap 22Q2 Public + Score, Chronos dataset, the gene effect scores derived from CRISPR knockout screens published by Broad's Achilles and Sanger's SCORE projects. Gene effect scores were inferenced by Chronos (https:// genomebiology. biomedcentral. com) and integration of the Broad and Sanger datasets was performed as described in Pacini et al. ,  17  except that quantile normalization was not performed. Negative scores imply cell growth inhibition and/or death following gene knockout. Scores are normalized such that nonessential genes have a median score of 0. As a criterion, genes were considered hits when their gene effect scores are <-0. 5 in all 3 selected GBM cell lines (U87MG, U251 and T98G -DepMap id: ACH-000075, ACH-000232 and ACH-000571 respectively).
Gene expression analysis on patient samples Gene expression analysis was conducted using the R2 microarray analysis and visualization platform (http:// r2. amc. nl). RNA-seq data were extracted from two independent cohorts providing open access to data acquired from various forms of cancer, The Cancer Genome Atlas (TCGA) database, and from normal tissues, the Genotype-Tissue Expression (GTeX) database. GBM TCGA dataset was used and partitioned in five subtypes according to the data available: classical (n = 17), mesenchymal (n = 27), neural (n = 17), proneural (n = 24) and not determined (n = 455). We used GTeX normal brain tissue data from the following subgroups: caudate (n = 246), cortex (n = 255), frontal cortex (n = 209), nucleus accumbens (n = 246), putamen (n = 205), spinal cord (n = 159) and thalamus (n = 202).
Organotypic co-culture models were daily exposed to Alisertib (100 nM for GBM6 or 2. 5 μM for GL261), Birabresib (500 nM for GBM6 or 1 μM for GL261) or their combination for 5 consecutive days. Tumor growth of mtDsRed-expressing GL261 cells within the brain slices was analysed over time using the JuLI™ Stage imaging system and the PHERAstar ® plate reader (λ ex 580nm/ λ em 620 nm-fluorescence signal acquisition with a 15 × 15 matrix scanning mode). Images of GFPexpressing GBM6 cells grafted into brain slices were acquired using a Zeiss AXIO-Observer Z1 microscope (Carl Zeiss SAS) with a 4 × fixed objective in the GFP+ channel. Images were automatically stitched together using the Tile Scan mode of the microscope. Area of each generated tumor was measured using ZEISS™ ZEN Software.
Statistics Each experiment was performed at least in triplicate. Data are presented as mean ± S. D or S. E. M as indicated in the figure legends. Statistical significance was tested using unpaired Student's t test. For experiments using multiple variables, statistical significance was assessed via two-way ANOVA. Log-rank (Mantel-Cox test) and Gehan-Breslow-Wilcoxon tests were used to compare animal survival by Kaplan-Meier analysis. A significant difference between two conditions was recorded for *p < 0. 05; **p < 0. 01; ***p < 0. 001. Figures were made using GraphPad Prism 9. 4. 1 (RRID: SCR_002798).
Besides experimental drugs, which account for ∼41% of the pharmacological hits (34/83), the high-throughput drug screen also identified 19 chemotherapy agents (∼23%), 3 targeted therapies and 27 non-oncology drugs, of which 15 are FDA-approved drugs currently used for human diseases and could potentially be repurposed for the treatment of GBM (Fig. 1a-b  and Table  S4  for details). As the second step of our methodology, we performed target deconvolution using three chemoinformatic databases (DrugBank, Drugsurv and Gostar) to exploit drug poly-pharmacology. All the known molecular targets and interactors of the 83 pharmacological hits were listed (Table  S5 ). Amongst the 1100 identified interactors, 292 were associated with at least three pharmacological hits and were used to design a focused siRNA library containing three individual sequences for each of the 292 top targets.
21  A transient knockdown using three different siRNA sequences leading to a significant inhibition of RRM1 gene and protein expression (Fig. 2a b, Student's t-test) resulted in a decrease in tumor spheroid growth, observed at day 5 and maintained until day 8. This was reflected by a significant drop in the U87 tumor spheroid growth of 70 ± 10%, 56 ± 16% and 72 ± 6% for the siRNA sequences #1, #2 and #3 in comparison to control, respectively (p < 0. 001, Fig. 2c , Student's t-test). Similar results were obtained with the U87vIII cells, in which RRM1 knockdown resulted in a 65-72% decrease in tumor spheroid viability after 8 days in comparison to control (p < 0. 001, Figure  S1a-c , Student's t-test). Our transcriptomic analysis using freely available datasets in the R2 platform showed that RRM1 expression is significantly up-regulated in GBM subtypes as compared to normal brain tissue (8.
Polar plot of 22 gene hits, which their gene expression downregulation decreased the cell viability in each tested cell line by at least 20%. Polar plot was made up of 10 data rings, each radial point representing a ten percent increment of the cell viability on a scale from 0 (inner radial point) to 100 (outer radial point). (d) Heat map representing the gene effect score of the 22 gene hits in 3 GBM cell lines, extracted from the CRISPR screen cohort data from the online Dependency Map portal. Right panel shows the protein-protein interaction network of the 9 hits identified as core vulnerabilities in all tested GBM cell lines (Gene effect score <-0. 5; https://string-db. org/). created, allowing to cover a large range of drug combinations. To assess the synergy of the two compound treatments, the Bliss score was calculated using the SynergyFinder web application (https://synergyfinder.
We found that both drug combinations resulted in a light synergistic effect with overall Bliss scores of 4. 4 ± 1. 2 and 6. 1 ± 0. 8 for LY3295668/Birabresib and Alisertib/Molibresib, respectively (Figure  S4c  and d ). These results were reflected by a decrease in tumor spheroid viability of 75. 1 ± 0. 6% after 15 days of treatment with the combination of LY3295668/Birabresib at 250 nM in comparison to control, while monotherapies reduced spheroid viability by 31. 5 ± 0. 8% and 50 ± 0. 5% for LY3295668 and Birabresib, respectively (Figure  S4e ). Similar results were obtained with Alisertib and Molibresib at 250 and 500 nM, respectively (Figure  S4f ). Finally, to evaluate the clinical relevance of the canonical targets of Alisertib and Birabresib, we used freely available datasets in the R2 platform to compare gene expression in GBM subtypes and normal brain tissue.
65  This co-culture system can keep brain tissues alive in incubation chambers mimicking natural conditions. Our results showed that the synergistic effect of Alisertib/Birabresib was maintained in both models and even exacerbated over time. To increase the complexity of our pre-clinical model and strengthen our results, we finally used an orthotopic syngenic mouse model. We showed that the orally administered drug combination significantly increased the median survival of GBM-bearing immunocompetent mice, without any observed side effects. Moreover, our transcriptomic analysis showed that AURKA and BRD2/3/4 gene expressions are up-regulated in GBM tissues in comparison to healthy brain tissues, reinforcing the rationale of dual BET and AURKA inhibition in GBM. Since Alisertib has been safely evaluated in a phase I trial for recurrent high-grade glioma patients  66  and several clinical trials have been undertaken or are ongoing with both drugs separately in GBM patients [NCT02186509, NCT02296476], our preclinical data strongly support the therapeutic relevance of the Birabresib/Alisertib combination in GBM.
Acknowledgements We thank the 'HiTS' platform (Carine Derviaux; https://www. crcmmarseille. fr/equipes/plateformestechnologiques/hits-ipcdd/) for the help with the combination drug screening. The authors would like to thank the  AP-HM Tumor Bank  (authorization number:  AC2018-31053 ;  CRB BB-0033-00097 ) for providing GBM tissue samples, and the  ARTC-Sud  patients association ( Association pour le Recherche sur les Tumeurs Cérébrales). This work was exclusively funded by institutional grants and donations from non-for-profit organizations, which had no influence on the direction of this research. It included research grants attributed to EP:  Marie Sklodowska-Curie Fellowship  from the  European Research Council  ( FP7-PEOPLE-2013-IIF  # 626794 ), " Pépiniere d'Excellence " grant from the  AMIDEX of Aix Marseille University  (funded by socioeconomic partners) or " Pépiniere d'Excellence " grant from the  Aix Marseille University  (funded by socio-economic partners),  Translational Research  grant from the  French National Cancer Institute  ( PRT-K19-135 ,  INCa ), and research grants from  Eva pour la Vie  and  Wonder Augustine , and to MLG: post-doctoral fellowship from the  ARC Foundation  (#  ARCPDF22020070002553 ).
For researchers whose expertise is outside of computer sciences, ensuring a proper application of ML methods and keeping track of new developments is challenging. The aim of the present review is to serve as a resource for marine ecologists who want to apply ML to their own data. To that effect, the section "A quick primer on machine learning" serves as a starting point for non-practitioners and introduces relevant vocabulary. The section "The setup of the database and its tags" describes our survey of the literature and the resulting structured database, on which the rest of the review is built. From it, we identified that ML is used at two stages in ecological research: (i) to process the raw data collected and extract ecologically meaningful datasets from it and then (ii) to combine these ecology-ready datasets together, and with others, to improve our understanding of ecological systems.
Therefore, the section "Machine learning to extract ecological information from observational data" describes applications where ML was used to generate ecological datasets from various raw data types: images and video, optical spec-tra of single cells, acoustics, omics, geolocation records, and ocean colour imagery and biogeochemical profiles. The section "Machine learning to improve ecological understanding" describes how ML can be used to gain knowledge on the relationships between species and their environment (section "Predicting species abundance and distribution"), among species (section "Capturing dynamic ecological relationships"), and between us, humans, and marine ecosystems (sections "Summarizing ecosystems through regionalization" and "Supporting human decisions on ecosystem management").
The general process for tackling an ML task is shown in Figure  2 , and the successive steps are described in its caption. Of course, depending on the approach and study case, some steps will be modified. For example, target variables are not available in the case of unsupervised learning (step 2). In DL, feature extraction is included in the model (step 4). In many situations, cross-validation is used in lieu of a dedicated validation set (step 6): The training set is split into subsets, the model is trained on all subsets but one, and validated on this remaining one; this process is repeated until each subset has been held out once. Comparisons with an external dataset (step 8), although important, are rarely performed due to the lack of such independent data. Finally, many ML models are never deployed (step 9), but serve to describe and understand a particular dataset.
Diverse ML algorithms have been developed to solve a large variety of tasks. In Table  1 , we provide a brief description of those commonly used in marine ecology publications. Finally, several open-source software libraries implement many ML methods under a consistent interface. Thus, once one understands the general process (as highlighted in Figure  2 ), exploring various methods is relatively easy and progress can be quick. The better known libraries of relevance for marine ecology are scikit-learn (https://scikit-learn. org/;  Pedregosa et al. , 2011)  and, more recently, TensorFlow (https://www. tensorflow. org;  Abadi et al. , 2016)  and PyTorch (https://pytorch. org;  Paszke et al. , 2019)  in Python, the tidymodels collection of packages in R (https://www. tidymodels. org/;  Kuhn and Wickham, 2020) , Flux (https://fluxml.
The setup of the database and its tags As a basis for this paper, we built a database of literature references covering the application of ML methods to marine data (supplemented by a few additional works, outside of this scope, but providing context and cited in this review). In its broadest definition, ML covers a wide array of methods and data types. Because many methods have been applied in marine ecology, it is extremely challenging to make an exhaustive inventory. Therefore, the goal of this database is instead to showcase the diversity of ML applications to marine data. To do so, multiple keyword-based searches in various scholar databases were performed by the authors, including the keywords "machine learning", "marine", "ecology", and variations thereof. The results were complemented with the personal libraries of the authors, who span a range of specialties.