dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We proposed a time-saving tool of modelling for the experts, allowing them to design a better global model of their process by a semi-interactive approach. Figure  3  shows that the resulting models are "better", not only according to the expert requirements, but also with respect to the numerical data (faster and better convergence). Above all, this method o↵ers tools for domain experts to design and test di↵erent hypothesis, using di↵erent datasets and class constraints. The complexity of the modelled process and the scarcity of the dataset is taken into account by allowing the expert to interact with the results all along the optimisation process. The expert who tested the software mentioned it is easy to question her hypothesis, to keep an open-mind posture and to find new mechanisms.
Conclusions In this paper, we proposed a new approach to semi-automatic modelling allowing to design complex models for multi-scales and multi-steps processes. Using expert's knowledge integrated during the optimisation process, the proposed approach is able to tackle challenges such as scarcity in a dataset, high dimensionality and high variability. According to experts guidelines, a set of local models are proposed for each variable, using symbolic regression. The local models form a Pareto front of candidate solutions to compromise between model fitness and complexity. These local models are then used to automatically construct a global model where each variable is defined by a given equation from the local models. In a global model, the multi-scales multi-steps process is taken into account by classifying the variables into di↵erent classes and by forwarding the predicted value of a variable to equations that use this variable to predict other ones.
Just 30 min after the onset of zygotic transcription and the steady establishment of the Bicoid gradient, endogenous Mitacs Global Link Scholarship (CAPR) and an Internal Curie Institute Scholarship (CAPR), a Mayent Rothschild sabbatical Grant from the Curie Institute (CF) and an NSERC discovery grant RGPIN/06362-15 (CF), a Marie Curie MCCIG grant No. 303561 (AMW) and PSL ANR-10-IDEX-0001-02. -https://www. psl. eu/en/research/fundingopportunities/psl-calls-proposals -http://www. agence-nationale-recherche. fr/en/ -https://www. fondation-arc. org/espace-chercheur -https://grad. uwo. ca/current_students/student_finances/ots. html -https://www. mitacs. ca/en/programs/ globalink -https://enseignement. curie. fr/en/ content/senior-researcher-sabbatical -http://www. nserc-crsng. gc. ca/index_eng.
The first hints of zygotic transcription are detected by RNA FISH marking the onset of zygotic transcription  [15]. B) At nc 11 (T = 1 hr 30 min), the main Bicoid target gene, hunchback (hb), is expressed within a large anterior expression domain. hb expression is schematized here from RNA FISH data  [15] : nuclei where ongoing transcription at the hb loci is detected are shown in green and nuclei silent for hb are shown in white. https://doi. org/10. 1371/journal. pgen. 1007676. g001 the endogenous expression of hb at the onset of zygotic transcription. The most obvious interpretation of these discrepancies was that the hb-MS2 reporter was missing key cis-response elements allowing repression of endogenous hb by an unknown repressor in the posterior  [26]. Here, we first show that the homogenously distributed Zelda transcription factor is responsible for the expression of the hb-MS2 reporter in the posterior.
A value of 1 is assigned to each nucleus experiencing transcription during the cycle In the green channel, MCP-GFP proteins recruited by the nascent MS2-containing mRNA can be seen accumulating at the hb-MS2ΔZelda loci (bright spots). In the red channel, the mRFP-Nup proteins localized at the nuclear envelope delineate nuclei. (B-D) Probability of active hb-MS2ΔZelda loci along the AP axis (dashed blue lines with error bars), extracted from snapshots of 6 movies near the end of each nuclear cycle: 360 s after the onset of nc11 interphase (B), 420 s after the onset of nc12 interphase (C) and 500 s after the onset of nc13 interphase (D). In each panel the probability of active endogenous hb loci (black dashed lines with error bars) extracted from the FISH data from  [3]  is also shown. Hill coefficients (H) are indicated in blue (hb-MS2 reporter) or in black (endogenous hb from FISH data).
Image processing and data extraction Live imaging processing was performed in MATLAB as in  [26]. Following imaging, movies are checked manually to verify all the nuclei included in data analysis are fully imaged in their depth and incompletely imaged nuclei (mostly nuclei at the periphery of the imaging field) are excluded. Nuclei segmentation is performed in a semi-automatic manner using our own software  [54]. Only nuclei that exist throughout the nuclear interphase are used for the analysis. The MS2 spot detection was performed in 3D using a thresholding method. An average filter was applied before thresholding on each Z of the processed time point for noise reduction. MS2 spots were detected by applying a threshold equal to ~2 fold above background signal and only the spots composed of at least 10 connected voxels were retained.
The intensity of the 3D spot is calculated as the sum of the voxel values of each Z-stack. At the end of nc13, some MCP aggregates may cause false spot detections: they are less bright and their shape is more spread compare to the MS2-MCP spots. The aggregates are eliminated automatically by raising the spot detection threshold without affecting the detection of the MS2 spots. We manually checked each movie to ensure the correct spot detection. The data from a segmented movie indicates for each nucleus its segmentation profile, identifier number and the intensity trace of the detected spot over time. During mitosis, nuclei divide in waves, usually from the embryo poles. Therefore, nuclei at the anterior pole may produce MCP-MS2 spots earlier due to either earlier chromatin decondensation or earlier reentrance of Bicoid into the nucleic space  [6].
This work was supported by a  PSL   IDEX REFLEX Grant for Mesoscopic Biology  ( ND, AMW, MC ),  ANR-11-BSV2-0024  Axomorph (ND and AMW),  ARC PJA20151203341  ( ND ),  ANR-11-LABX-0044   DEEP Labex (ND) , an  Ontario Trillium Scholarship for International Students (CAPR) , a.
In each panel the probability of active endogenous hb loci (black dashed lines with error bars) extracted from the FISH data from [3]  is also shown. Hill coefficients (H) are indicated in blue (hb-MS2 reporter) or in black (endogenous hb from FISH data). In B, the difference between the FISH signal and the probability of active hb-MS2ΔZelda loci is due to small number of nuclei and large variability distance between them at nc11 which limit statistics.
Image and Data Analysis. To track the recombinant protein production, images were taken every two hours in bright field and in fluorescence to detect CellTracker Red (red channel) and GFP (green channel). GFP production was measured by quantifying the intensity evolution of each detected cell in the green channel. A custom-made MATLAB code was used to process the images. First, the images taken in the red channel (i. e. labeled for CellTracker Red) were thresholded and converted into masks. The objects with a diameter comprised between 10 and 25 µm were considered as cells. The green channel signal intensity (I cell ) was then quantified for each cell. The green channel signal measured in rest of the anchors was considered as the background (I back ). The green channel signal per cell (I) was defined as the value of green channel signal in the mask minus the value of green channel signal in the background: I = I cell -I back.
Acknowledgements The study was financially supported by the  São Paulo Research Foundation  ( FAPESP  process number  2014/24797-2 ,  2014/10557-5  and  2015/26701-0 ),  National Council of Technological and Scientific Development (CNPq  process number  202086/2015-1 ) and  European Research Council (ERC)  under contract number 278248 Multicell. We would like to thank the  Condensed Matter Physics Laboratory (PMC) at Ecole Polytechnique  for providing the use of Malvern Zetasizer equipment. We also thank  Caroline Frot ,  Cyprien Guermonprez  and  Raphaël Tomasi  for helpful support of image analysis and discussions on this work.
cDNA was synthesized from 2 µg of RNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) and 100 ng were used to conduct qPCRs on ABI Prism 7700 (Applied Biosystems) or StepOnePlus Real-Time PCR System (ThermoFischer Scientific). The following Taqman Gene expression assay probes were used: GAPDH Hs02758991_g1, GPR43 Hs00271142_s1, GPR41 Hs02519193_g1, GPR109a Hs02341584_s1, SMCT1 Hs00377618_m1, and MCT1 Hs01585687_m1. GAPDH was used for normalization. Samples were tested in experimental duplicates and at least biological triplicates. Promoter analysis. To search for potential transcription factor binding sites on HT-29-TGF prom , we used the MatInspector software tool (Genomatix, Munich, Germany) and Jaspar database. Statistical analysis and Graphics. Data were analysed using R and RStudio software.
PCA analysis was done with prcomp package and correlation matrix was done with Hmisc package. Graphics were produced with ggplot2 package. Statistical test used were two-tailed, T test or Wilcoxon rank test on medians with a confidence level of 95%. P value: *P < 0. 05, **P < 0. 01, ***P < 0. 001, NS: non-significant. Data come from at least 3 independent biological experimentations.
Acknowledgements The authors are grateful to  Drs Véronique Douard  ( INRA   UMR1319 ),  Marion Espeli  ( INSERM UMR-S996 ) and  Pierre Larraufie  ( University of Cambridge, UK ) for helpful discussions and critical comments on the manuscript. The authors are grateful to members of the team for helpful discussions. The authors are grateful to  Dr Cora Weigert  ( University of Tübingen ) for sharing the TGF promoter constructs and  Agnès David  ( UMR1280 PHAN ) for SCFA analysis. This work was supported by the  Institut National de la Recherche Agronomique (INRA) , by grants funded by  EU-FP7   METACARDIS  ( HEALTH-F4-2012-305312 ), by the  ANR FunMetagen  ( ANR-11-BSV6-0013 ). CMG was the recipient of a fellowship from the  Ministère de la Recherche et de l'Education Nationale (Sorbonne Université ).
FACS data was analyzed using the FlowJo software. GraphPad Prism software was used to calculate drug concentrations causing 50% lethality (LC 50 ). For the larger scale drug synergy assays, cells were seeded and treated with nine 2-fold serial dilutions of each compound dispensed into cell assay plates. Single agent IC 50 's were calculated using standard four-parametric curve fitting. IC 50 was defined as the compound concentration at which the Cell Titer Glo (CTG) signal was reduced to 50% of that measured for the vehicle (DMSO) control. In order to analyze the activity of the compounds in combination, the cells were seeded and treated with seven or eight 3. 16fold serial dilutions of each compound dispensed, either individually or in all possible permutations in a checkerboard fashion. Effects of the single agents, as well as their checkerboard combinations on cell viability were assessed after three days of incubation at 37 °C/5% CO 2 using CTG at 75 μL reagent/well.
Two independent experiments, each one performed in duplicate, were performed. Luminescence was quantified on a multipurpose plate reader. Potential synergistic interactions between compound combinations were assessed using the Loewe additivity model (Chalice TM Bioinformatics Software available in Horizon website) and reported as the Synergy Score  [20]. Cell viability assays for LC 50 determination and colony forming unit (CFU) assays have been previously described  [18].
The translational potential of these findings in AML are high, as both S55746 (NCT02920541) and the clinical MCL1 inhibitor derivative S64315 (NCT02979366) are both currently undergoing phase 1 clinical evaluation in human AML. Although venetoclax has previously been shown to synergistically enhance the activity of the MCL1 inhibitor A-1210477, the latter was only been shown to be effective in vitro and against AML cell lines at micromolar concentrations  [29]. In contrast, the current work shows that combined S55746 and S63845 has low nanomolar activity across a broad spectrum of AML genotypes, including primary samples from patients with adverse genetic risk and chemoresistant AML. A proportion of primary AML samples remained resistant to BCL-2/MCL1 targeting (Fig. 2 ). Our gene mutation profiling studies showed enrichment of samples with TP53 mutation in this sub-group.
We evaluated potential heterogeneity of associations between alcohol consumption and the risk of nonfatal CHD and stroke according to smoking status by including an interaction term between baseline alcohol consumption (continuous) and smoking status (0=never, 1=current smokers). We assessed the interaction term with the Wald test on one degree of freedom. We also evaluated the heterogeneity of associations by physical activity and education level, but the results were compatible with the hypothesis of homogeneity across levels of these variables. The assumption of proportionality of hazards was evaluated using the inclusion into the disease model of interaction terms between exposure and attained age (data not shown). The certainty of each incident cardiovascular event was assessed using available clinical information, and a score was developed to express low, medium, or high certainty, as summarised in supplementary table 5.
Strengths and weaknesses of this study This is the first population based study assessing the association between baseline and lifetime alcohol consumption and the risk of non-fatal and fatal CHD and stroke in a pan-European prospective investigation. Our findings reinforce previous evidence of the association between alcohol consumption and the risk of CHD in the Spanish population. 31 Our analysis included a comparatively large number of incident CHD and stroke events (after exclusion of prevalent CVD at baseline), and controlled for several potential confounding factors. We harmonised exposure data across participating countries on alcohol consumption, smoking status, and other lifestyle characteristics, and studied validated CVD outcomes. A controversial element in the evaluation of the association between alcohol consumption and CVD has been the observation that non-drinkers (<0.
30 Data from the EPIC-CVD case-cohort study have contributed to a recent combined analysis of 83 prospective studies. In contrast with the current study, however, that combined analysis could not focus systematically on lifetime alcohol consumption or on different types of alcohol consumed. 11 In a metaanalysis of 26 epidemiological studies (17 cohort and 9 case-control) on stroke morbidity and mortality, alcohol intake was positively associated with the risk of haemorrhagic and ischaemic stroke, similarly in men and women. 10 In a large recent study conducted in the UK, heavy drinkers (defined as participants exceeding current UK guidelines for alcohol of 24 g/ day and 16 g/day, for men and women respectively) compared with moderate drinkers (within the UK alcohol guidelines) showed hazard ratio estimates of 1.
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain.
L. monocytogenes Strains The bottleneck in the hazard characterization step in MRA using NGS data is often the lack of reproducible health endpoints linking genotypic to phenotypic data  (Brul et al. , 2012). The L. monocytogenes isolates included in this study were from an exhaustive and epidemiologically balanced surveillance in France. The 245 strains were selected from a nonredundant collection of 7,342 isolates from food (n = 4,551) and clinical (n = 2,791) sources sampled in France between 2005 and 2013. Selection of the 246 strains from the collection was based on MLST and PFGE typing by  Maury et al. (2016)  for further study aiming at sampling a representative panel of L. monocytogenes species diversity. The 7,342 isolates were collected by the French National Reference Center (NRC) for Listeria (n total = 6,804; n food = 4,013; n clinical = 2,791) and the French National Reference Laboratory (NRL) for Listeria (n total = n food = 538).
Other nonalert food isolates were deduplicated by selecting an isolate from the same date of isolation and food source. This resulted in 7,342 food and clinicalassociated isolates from which the 245 sequenced strains were selected after MLST and PFGE typing to obtain a panel of strains representative of the original population. The clinical frequency, consisting of percentage of reported cases associated with each of the clinical strains, was treated as the outcome or dependent variable in this study  (Maury et al. , 2016)  (data available in Supporting Information Tables  SI  and SII ). The 245 strains encompassed 207 strains from food as well as food environment and 38 clinical strains. The 38 clinical case strains consisted of (i) 30 clinical strains from the work of  Maury et al. (2016) , (ii) strains representative of ST218 and ST2 consisting of six strains isolated from an outbreak in England by Public Health England and two other clinical strains belonging to ST121.
Virulence Genes Database The L. monocytogenes population is very structured, as shown by the huge diversity between lineages almost to species level  (Haase et al. , 2014). This study considered an exhaustive set of virulence and associated genes and compared their profiles among two diverse panels of food and clinical strains. An exhaustive literature review for all known virulence-associated factors, virulence factors, virulence genes, and environmental stress tolerance genes was performed. A final set of 136 genes was selected (Supporting Information Tables  SI  and SII ). Amino acid sequences of the genes were extracted from the NCBI protein database. Multiple sequences were found for each query and in such cases, sequences from EGD-e strain, which has been the most extensively annotated strain, were used as a reference.
Assembly and Alignment The program Velvet for de novo assembly of short reads  (Zerbino & Birney, 2008)  was used for the assembly of the raw reads. Prediction of open reading frames and proteins was performed using Prodigal for each of the de novo assemblies  (Hyatt et al. , 2010). Each genome's predicted proteins were aligned against the virulence database using BLASTP, a Basic Local Alignment Search Tool (NCBI-blast version 2. 3. 31+). The best hits from the assessment of the length and similarity were kept as the desired potential virulence gene. A matrix consisting of best hit in percentages for each virulence gene in each genome was constructed. Heatmaps were used to visualize the percentage alignment matrix of both the food and clinical strains using R version 3. 3. 1.
Patients and Methods The MDS-005 trial was a randomized, placebo-controlled, double-blind phase III study that evaluated the efficacy and safety of lenalidomide in patients with IPSS low-or intermediate-1 risk MDS without del(5q) who were ineligible for or refractory to ESA treatment. Full details of the study supporting the efficacy, safety, and selected HRQoL findings have been published previously. 19 he study was approved by individual institutional review boards of participating centers and conducted according to the Declaration of Helsinki. Written informed consent was obtained from all patients before enrollment. The trial was registered as NCT01029262 at https://clinicaltrials. gov.
The standardized scores for this questionnaire range from 0 to 100, with higher scores indicating greater levels of functioning or more severe symptoms. Data were collected electronically using tablet computers. All HRQoL scales of the QLQ-C30 were analyzed; however, the analysis focused on 5 preselected and clinically relevant scales  [22] [23] [24] [25]  : fatigue, dyspnea, global quality of life, physical functioning, and emotional functioning.
The incidence of Grade 3/4 adverse events was analyzed according to RBC-TI ! 8 weeks response in lenalidomide-treated patients. Adverse events were coded as in the Medical Dictionary for Regulatory Activities and graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (version 3. 0). In addition, a post hoc analysis was conducted to assess the relationship between change in hemoglobin level and changes in HRQoL outcomes at weeks 12 and 24 across all 5 preselected scales. The postbaseline hemoglobin collection dates were matched with the HRQoL assessments to obtain hemoglobin data on the same day as the HRQoL visit. The association between variables was analyzed using the Pearson correlation coefficient. The data cutoff date for inclusion in this analysis was March 17, 2014.
25 mm internal diameter; 0. 1 µm film thickness; Agilent), and a split-splitless injector (60 ml/min split-flow; valve opening 30 s after injection) with helium as carrier gas (velocity = 50 cm/s at 120 • C). The temperature program began at 120 • C, ramping at 10 • C/min to 140 • C, then ramping at two • C/min to 290 • C, and holding for 10 min. The chemical identity of cVA was checked using gas chromatography-mass spectrometry equipped with a CP Sil 5CB column. The amount (in ng) of cVA was calculated on the basis of the data obtained from the internal standards.
Acknowledgements Maša Ždralević  was supported by a post-doctoral fellowship from the  Fondation ARC on Cancer Research  (grant n°P  DF20151203643 ), IM was funded by  SERVIER-CNRS  and LNCC (équipe JP Labellisée). MV, BD, SP were funded by the  Centre Scientifique de Monaco  and a Grant from  GEMLUC (MV, JP)  and  Université Côte d Azur ,  IRCAN & Centre A. Lacassagne (MZ, IM, JP).
This lack of development can also be partly explained by the significant differences between the data handled by the 3D visualization software packages and the analytical tools. On one side, 3D visualization solutions such as PyMol  [9] , VMD  [10]  and UnityMol  [11]  explore and manipulate 3D structure coordinates composing the molecular complex that will be displayed. The scene seen by the user is composed of 3D objects reporting the overall shape of a particular molecule and its environment at a particular state. This scene is static if we are interested in only one state of a given molecule, but is often dynamic when a whole simulated trajectory of conformational changes over time is considered. Analysis tools, on the other side, handle raw numbers, vectors and matrices in various formats and dimensions, from various input sources depending on the analysis pipeline used to generate them.