dataesr/scientific-paragraphs-categorization huggingface dataset:
(b): For each brewing method, the loss factor selected by EFSA corresponds to the lowest reliable percentile (25th percentile) of the loss factors reported in Section 3. 2. 1. (c): All coffee drinks were assumed to be prepared from roasted and ground coffee beans, except for instant coffee and coffee imitates where a specific category of solid samples is available. (d): Prior to the pooling of liquid and solid samples, occurrence values for the solid samples were divided by the corresponding dilution and loss factors to obtain an estimate for the corresponding beverages as consumed. The difference between LB and UB estimates was found to be small, indicating that the uncertainty due to left-censored data is limited. The highest chronic exposures were estimated in the youngest population group, i.
Home-cooking: toasting bread Furan has been reported in home-cooked foods. The highest furan concentrations for these foods were reported for toasted bread and caramel (see Appendix E, Table  E. 1). Considering the importance of toasted bread in some diets, a scenario assessing the impact of toasting bread on the dietary exposure was considered by the CONTAM Panel. For this scenario, a furan concentration of 260 lg/kg was assumed for all toasted bread, which corresponds to the highest mean concentration reported for fully browned toast in Appendix E, Table  E. 1. To identify consumption records referring to toasted bread, any consumption record containing 'toast' in its original food description was selected, and where the consumption data were already coded according to FoodEx2, consumption records containing the processing facet for toasting (A07HC) were also considered.
Previously reported dietary exposure The occurrence of furan in food was reported annually by the EFSA  (2009, 2010c, 2011d). The report published in 2011 includes all data collected between 2004 and 2010. In addition, it presents exposure estimates for different populations. The analysis included a total of 5,050 analytical results for furan content in food submitted by 20 countries. Mean furan exposure across surveys was estimated to range between 0. 03 and 0. 59 lg/kg bw per day for adults, between 0. 02 and 0. 13 lg/kg bw per day for adolescents, between 0. 04 and 0. 22 lg/kg bw per day for other children, between 0. 05 and 0. 31 lg/kg bw per day for toddlers, and between 0. 09 and 0. 22 lg/kg bw per day for infants. A major contributor to exposure for adults was brewed coffee with an average of 85% of total furan exposure.
Exposure scenario/exposure model To estimate chronic dietary exposure to furan, EFSA used a final data set containing 9,663 samples provided by governmental organisations and 7,393 samples provided by commercial organisations. The amount of occurrence data submitted differs considerably depending on food products and reporting data provider, with most of the samples (~80%) collected in only five Member States (and ~60% originating from one single Member State). There is therefore uncertainty on whether possible countrybased differences in the levels of furan in diverse food commodities are well represented. Likewise, the lack of information on the analytical method used to analyse some food samples (~30% of the governmental data) adds some uncertainty to the concentrations of furan reported for some food commodities.
In addition, possible losses of furan during standing of beverages after brewing could not be considered in the present assessment because the standing time is highly dependent on consumer behaviour and therefore not predictable. Overall, these uncertainties may lead to an overestimation or underestimation of exposure. Furan may also occur in food additives and flavourings, which may be used in a wide range of food products. While it is expected that most of the furan concentrations resulting from those uses have been captured in the final data set, some minor food products may not be covered by the available data hereby resulting in a minor underestimation of the exposure estimates. Uncertainties and limitations related to the use of the EFSA Comprehensive Food Consumption Database have already been described by EFSA (EFSA, 2011a) and are not further detailed in this opinion.
However, among those with a particular implication for the dietary exposure to furan, the uncertainty associated to commercially processed foods should be mentioned because for certain food groups (e. g. vegetables and vegetables products) consumption data have been mostly reported at the level of raw commodities (e. g. tomatoes), without systematic information on whether these were commercially processed products (e. g. canned tomatoes). Following a more detailed analysis of the Comprehensive Database, the highest contribution of commercially processed products among dietary surveys was derived for each food category and applied to all consumers, regardless of the dietary survey. Assuming the same contribution for all consumers may underestimate the upper tail exposures for high consumers of commercially processed products.
However, as this contribution refers to the maximum contribution observed among dietary surveys, mean exposures are expected to be overestimated. Furthermore, eating occasions reported for composite food were assumed to be commercially processed as no information is available on whether they referred to homemade dishes or ready-to-eat foods. Also in this case, an overestimation of the exposure is likely. The Comprehensive Database also contains limited information on consumer's preferences regarding coffee consumption (e. g. roasting degree, coffee type). In particular, unspecified coffee beverage is the most frequently reported coffee beverage in the Comprehensive Database. While these coffee beverages were handled as the most frequently consumed coffee within Europe (i. e. Americano coffee or filtered coffee), these consumption events might in fact refer to other coffee types with higher or lower furan concentrations.
4. 1. Occurrence/Exposure • To estimate chronic dietary exposure to furan, EFSA used a final data set containing 9,663 samples provided by governmental organisations and 7,393 samples provided by commercial organisations. Governmental data and commercial data were found to be comparable and it was considered appropriate to merge both data sets. • Food characteristics, processing and cooking conditions, and the losses which occur mainly due to evaporation in the preparation of the food at the level of the consumer, determine the final concentration in the food as consumed. • The highest concentrations of furan were found in whole roasted coffee beans, with a mean value of 4,579 lg/kg. Mean concentrations in ground roasted coffee (2,361 lg/kg) and unspecified coffee solids (2,186 lg/kg) were lower, suggesting a loss of furan during the process of grinding.
2MF/F: 2-methylfuran/furan ratio; 3MF/F: 3-methylfuran/furan ratio; DPE: Documentation provided to EFSA by FoodDrinkEurope; n. a. : not analysed; n. c. : not calculated; n. r. not reported; n: number of samples; RL: reporting limit. (a): Applied method of analysis was Headspace sampling followed by analysis using GC-MS unless otherwise stated. (b): The ratios were estimated by EFSA, with the exception of those food products reported in Becalski et al. (2016)  where the ratios were provided and are reproduced in this table. Ratios were only calculated when both furan and the methylfuran was quantified. (c): The mean values were calculated by EFSA using the middle bound approach if they were not provided in the publication. (d): Data on the co-occurrence of furan, 2-methylfuran and 3-methylfuran in jarred baby foods, cereal flakes, roasted and grounded coffee, soluble coffee, infant cereals and tomato sauce.
One-way analysis of variance was used to determine whether there were any statistically significant differences between the mean values of three or more independent (unrelated) groups. Youden index with area under the curve (AUC) was used to determine the cutoff of nasal FENO for diagnosis of AR in patients with or without asthma. MedCalc software (Ostend, Belgium) with DeLong method was used to compare the AUC between groups.
R3-Design support Technocity introduced significant ruptures between the playful and the serious part. The game and videos presenting industries were clearly dissociated. Similarly the graphic style between the games and the serious parts do not were consistent. It must be said that the videos were part of a database that the sponsor could operate freely. Despite the break with the design of games, he wanted them to be implemented. Here, dedicated support would have been welcome. D1-General Adoption Some teachers have been at the initiative of the Technocity Serious Game project. But nothing has been done to prepare all the other teachers for the arrival of this Serious Game: To think about how to organize the activities, associated pedagogies and assessments have not been conducted.
The primers used in the PCR reaction are listed: Atg7 forward primer: 5′-TGGCTGCTACTTCTG-CAATGATGT-3′, reverse primer: 5′-CAGGACAGAGACCATCAGCTCCAC-3′; p48-cre forward primer: 5′-ACC GTCAGTACGTGAGATATCTT-3′, reverse primer: 5′-ACCTGAAGA-TGTTCGCGATT ATCT-3′; Rip3 primer-1: 5′-CGCTTTAGAAGCCTTCAGGTTGAC-3′, Rip3 primer-2: 5′-GCCTGCCCATCAGCAACTC-3′, Rip3 primer-3: 5′-CCAGAGGCCACTTGT-GTA GCG-3′. Immunofluorescence. IF was conducted using 4 μm thin FFPE pancreatic tissue sections obtained from Atg7 mice and human specimens and processed as described in detail previously. 2, 31, 40 All images were processed using the StrataQuest software (TissueGnostics, Vienna, Austria), allowing the quantitation of the total cell numbers from DAPI-positive cells as well as quantitation of target positive cells. In FACS-like scattergrams, every point represents one single cells were plotted according to their Cy3 and Cy5 IF intensity versus their DAPI-intensity from the entire tissue.
, Rockford, IL, USA) to ensure equal protein loading, according to the instructions. Protein fragments were processed and analyzed by using computer-assisted software ImageJ (NIH, Bethesda, MD, USA). EM observation. Freshly isolated pancreatic tissue pieces were fixed in 3% glutaraldehyde and embedded in Epon. Ultra-thin sections were prepared for microscopy according to a routine procedure described previously. 2  Caspase-3 activity. The activity of caspase-3 was measured in pancreatic tissue extracts using a modified assay for measuring caspase activity as described previously. 42 Results were expressed as the caspase-3 substrate cleavage in release fluorescence units, after subtraction of the nonspecific product formation (substrate plus inhibitor). Statistical analysis. Statistical analysis was performed using Student's t-test for each group.
Acknowledgements. We thank kindly  Masaaki Komatzu  and  Erwin Tschachler  for providing Atg7 transgenic mice and  Oliver Strobel  for providing us with the p48-Cre mice. Rip3 transgenic mice were kindly provided by  Vishva M Dixit  and  Manolis Pasparakis. We would like to express our gratitude to  Drs Leopold Eckhart ,  Klaus Felix ,  Nathalia Giese ,  Ingrid Haußer-Siller ,  Ingrid Herr ,  Harald Höger ,  Heidemarie Rossiter  and  Jennifer Buchholz. We also like to appreciate the following persons for their support:  Mrs Bruni Bentzingern ,  Soyka, Esther ,  Ulf Hinz ,  Simone Hoppe ,  Monika Meinhardt  and  Karin Ruf  for technical assistance, as well as  Dr. Wolfgang Groß  and  Dr. Michael Schäfer  for their IT support. This study was supported by a  DFG Excellent Initiative Frontier award  (to FF) and institutional funding (to MWB and TH).
GK is supported by the  Ligue contre le Cancer  (équipe labelisée);  Agence National de la Recherche (ANR) -Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC) ;  Cancéropôle Ile-de-France ;  Institut National du Cancer (INCa) ;  Institut Universitaire de France ;  Fondation pour la Recherche Médicale (FRM) ; the  European Commission (ArtForce) ; the  European Research Council (ERC) ; the  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; the  SIRIC Cancer Research and Personalized Medicine (CARPEM) ; and the  Paris Alliance of Cancer Research Institutes (PACRI).
Background: Automation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies. Methods: Defined monomicrobial and clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software.
Imaging time The image acquisition was performed with the ImagA BT using the OPTIS™ software (BD Kiestra, Drachten, Netherlands). The 26 bacterial strains and 99 clinical urine samples were imaged every 2 h from time 0 to time 24 h post inoculation. Each defined and clinical urine samples were processed in 2 runs, 0e12 h and 12e24 h. The first run was inoculated at 8 h in the morning and imaged at times 0e2e4e6e8e10e12e24 h. The second run was inoculated at 20 h in the evening and imaged at times 0e12e14 e16e18e20e22e24 h. In addition, 119 clinical urine samples were imaged in 1 run at times 0 h, 16 h, 20 h, and 24 h post inoculation. For the algorithmic analysis, time 0 was chosen as a reference of no growth or no detectable growth. Time 12 h was chosen as an intermediate time point (since most microbial colonies are already detectable) allowing an increase in algorithmic performance for growth prediction.
Image acquisition and image analysis The major steps of sample image analysis are presented in Fig. A. 1. The system is described in the context of an automated sample streaking module (InoqulA BT) using a magnetic rolling bead to inoculate samples on agar plates along a predefined streaking pattern (InoqulA BT pattern #17 in this study). To overcome classical SNR imaging limitation and to optimize contrast, the BD Kiestra™ OPTIS™ image acquisition software was used in this study to analyze imaged plates which is designed to adjust and optimize in real time the contrast and the SNR for each pixel in each red/green/blue (RGB) channels by performing an acquisition session of multiple frames (22 images acquisition per plate) using several illumination conditions and exposure times in a calibrated and standardized manner.
Semi-quantification and identification classifiers Random forest classifiers were used at three different stages to perform: (1) microbial vs non microbial objects classification, (2) semi-quantification and (3) presumptive identification. The parameterization of each classifier including number of trees, number of nodes and number of features used at each split was chosen to minimize and level off outof-bag error and to avoid over fitting  [14]. The features used as input to the random forest algorithm were chosen as the 20 most important features detected using the Boruta algorithm (R package Boruta) and confirmed with VSURF R package as providing a good balance between out-of-bag error, classification performance, computation speed and classifier object storage size  [15, 16]. For each classifier, algorithm update test performance was assessed and compared to baseline performance in a regression testing to ensure that unanticipated effects do not appear.
Only algorithm updates showing performance improvement were selected. Training of the classifiers was performed on an entirely separate dataset ensuring that the performance reported here corresponds to the test performance of a cross-validation process. The training of the semi-quantification classifiers was performed on a dataset generated at the BD laboratory (BD Sparks, Baltimore, USA) composed of serial 10-fold dilutions ranging from 10 À2 to 10 À7 of a 0. 5 McFarland bacterial suspension of 5 defined microbial strains for each of 11 different selected bacterial species, of 20 mixed samples composed of 2e3 defined microbial strains and of 200 clinical urines (Table  A. 2). The semi-quantification classifiers were tested on the CHUV dataset composed of 26 microbial strains and 138 clinical urines (Tables A.
1 and A. 3). For the identification classifiers, a microbial objects dataset was generated from 8 defined microbial strains of the BD laboratory (Table  A. 2), 15 defined microbial strains and 41 clinical urines of the CHUV laboratory (Table  A. 3). One semi-quantification classifier was developed per media, each providing a probability for each plate to be within ordered count buckets <10 2 (no growth), 10 2 e10 3 , 10 3 e10 4 , 10 4 e10 5 and !10 5. Growth detection was performed when comparing the <10 2 category to the four other count categories. Classification performance was assessed with agreement, agreement with 1 logarithmic tolerance, sensitivity (growth detection only) and specificity (growth detection only). Two-sided 95% confidence intervals were computed by bootstrapped percentile method  [17].
Multi-reader and imaging algorithm comparisons Readings from 10 independent technicians and prediction from the algorithm were compared over a set of 150 images randomly selected from the data set to assess the agreements percentage. The algorithm was considered as one independent reader and agreements were evaluated in two conditions, with or without algorithm as part of the reader's dataset. Global agreement between readers was calculated by permutation obtained among 2 to 10 readers, with and without algorithm results, with 0, 1 and 2 logarithmic tolerances. Twosided 95% confidence intervals were obtained by bootstrapped percentile method performed on reviewed plates during the reader permutation process  [17].
The BD Kiestra™ OPTIS™ image acquisition software was used to maximize SNR and contrast for each pixel in each RGB channel of the plate image in order to collect multi-sources standardized data information for subsequent image analysis. Due to the internal The plates streaking process with the InoqulA BT generates a non random uniform pattern characterized by a CFU density gradient along the streaking pathway to produce isolated colonies regardless of the original concentration of CFUs in the sample. The rolling magnetic bead spread the inoculated sample generating thus a print out of what was loaded on the bead. The bead model count (supplementary material and methods A. 1) relies on the analysis of the CFU distribution along the streaking pattern allowing an estimation of the microbial load on the bead, which in case of a high microbial load leading to CFUs confluence, represented the best estimation of the microbial load carried on the magnetic bead and thus of the bacterial load contained in defined or clinical samples.
Moreover, the growth of some bacterial species on CHROM agar produced lawns of tiny and faint colonies after 24 h incubation in normal atmosphere that were not detected, or detected but not considered as microbial object by the algorithms, and thus reported as negative (see Corynebacterium striatum and Lactobacillus fermentum in Fig. G. 1), representing 45% (13/29) FN cases on CHROM agar. This result was probably caused by an absence of these growth patterns in the database and/or in the training set of the algorithms, underlying how important it will be to enrich and train the algorithms with the most exhaustive possible set of strains presenting unique growth patterns. Thanks to cloud-based informatics technology, a solution for continuous improvement of detection Se and Sp over time could be to  automatically enrich the training databases with samples identified as wrongly assessed by the algorithms during manual reviews of routine cases.
Even though some features could be normalized to be pattern independent, other features such as overall plate growth are dependent of the streaking pattern and accounted for some decreased level of performance. The future imaging applications will therefore be pattern dependant. The rolling bead model requires the detection of single colonies to generate a semi-quantification prediction. When only a bacterial lawn with no detectable single colonies was observed such as growth of Lactobacillus spp. , Corynebacterium spp. and Aerococcus spp. on CHROM agar, the algorithmic prediction of semi-quantification has to rely exclusively on overall plate growth derived features contained in the training dataset. Thus, in this case, inaccurate semiquantification prediction was generated in absence of similar growth patterns in the database.
The manual annotation from clinical urine samples allowed thus to enrich the database with microbial intraspecies morphology and color variations and with features belonging to bacterial species not included in the microbial isolates used in this study. Finally, the performance of the object classification into microbial and non-microbial object affected the identification performance since each non-microbial object such as dust considered as microbial object were analyzed by the identification algorithms, which generated wrong identification prediction based on the features used by the classifiers. This study demonstrates that intelligent imaging applied to diagnostic bacteriology has the potential to significantly improve laboratory workflow by automating software-based decision making processes that are nowadays humandependent.
Understanding the rat neurochemical connectome is fundamental for exploring neuronal information processing. By using advanced data mining, supervised machine learning, and network analysis, this study integrates over 5 decades of neuroanatomical investigations into a multiscale, multilayer neurochemical connectome of the rat brain. This neurochemical connectivity database (ChemNetDB) is supported by comprehensive systematically-determined receptor distribution maps. The rat connectome has an onion-type structural organization and shares a number of structural features with mesoscale connectomes of mouse and macaque. Furthermore, we demonstrate that extremal values of graph theoretical measures (e. g. , degree and betweenness) are associated with evolutionary-conserved deep brain structures such as amygdala, bed nucleus of the stria terminalis, dorsal raphe, and lateral hypothalamus, which regulate primitive, yet fundamental functions, such as circadian rhythms, reward, aggression, anxiety, and fear.
subcortical brain areas, which are known to be responsible for primitive, yet essential and evolutionary conserved functions related to survival. ChemNetDB provides the first whole-brain, multiscale, and consistently collated rat connectome database. ChemNetDB also includes neurochemical specificity and represents a powerful tool for in vitro, in vivo, and in silico investigations of brain function and disorders. The central nervous system consists of a multiscale chemically multilayer network that represents the integration of macro-, meso-, and microconnectomes  [1]. Groups of morphologically and physiologically diverse neurons are wired as connectivity patterns with a certain degree of universality across species and individual variability. This multiscale wiring diagram provides the topological foundation of motor, sensory, emotional, and cognitive information processing in the brain and has attracted the attention of neuroanatomists for decades.