dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection The Isis-Diab cohort is a large multi-centric cohort of T1D patients in France which recruitment started in 2007. Starting in March 2010, three copies of the questionnaire were sent to the parents of 6618 T1D patients enrolled in the cohort during the month following their inclusion in the study. Parents were asked to fill the questionnaire regarding the exposures and events having taken place in their child's life before the clinical onset of T1D. They were also asked to enroll as controls two of their friends having an unaffected child of the same age. The 6144 parents having provided a phone number were contacted once during the week following the questionnaire sending. If the questionnaire was not returned within 3 months, parents received a reminder by mail. One thousand seven hundred sixty-nine cases (i.
Urban/farm environment Using the postal code of the participants obtained through the questionnaire, two variables defined at the level of the patient's "commune" (town) of residence were used to quantify whether the participants lived in an urban or rural area. Those variables are the urban units index (as a code reflecting the size of the commune's urban area) and the percentage of farmers in the active population. Those two variables came from anonymous public databases (French Quetelet Network (http://www. reseau-quetelet. cnrs. fr), via the Centre Maurice Halbwachs -Archives de Données Issues de la Statistique Publique (http://www. cmh. ens. fr/ greco/adisp. php)) and were dated in 2007 (census closest to the date that patients started to receive the environmental questionnaire). Environment was also controlled by the recruitment center e.
Acknowledgement We deeply thank our sponsors who made this study possible:  NovoNordisk France for the institutional "Diabetes Alliance " grant running from  2008 ,  Inserm  for a specific support to "Diabetes Alliance", the  Programme Hospitalier de Recherche Clinique , the  Agence Nationale de la Recherche , the  Association pour la Recherche sur le Diabète (ARD). We thank  Gérard Biau  for his oversight. We thank  Alain Fourreau ,  Adeline Guégan ,  Gaël Leprun  and  Valérie Jauffret  for mailing the questionnaires and entering the responses. We thank the participants and their parents for their time. We acknowledge the collective effort of the Isis diab collaborative group which is composed of the following physicians:  Dominique Thevenieau ,  Corinne Fourmy Chatel ,  Rachel Desailloud ,  Hélène Bony-Trifunovic ,  Pierre-Henri Ducluzeau ,  Régis Coutant ,  Sophie Caudrelier ,  Armelle Pambou ,  Emmanuelle Dubosclard , Florence.
Funding This study was sponsored by an ongoing institutional grant from  NovoNordisk France , a specific Inserm support lasting from 2008 to 2014, the  Programme Hospitalier de Recherche Clinique (PHRC ISIS-DIAB  and  ISIS-VIRUS) , the  Agence Nationale de la Recherche (ANR ENVIROGENEPIG) , the  Association pour la Recherche sur le Diabète (ARD). The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. They also did not have access to the clinical trial database, nor any opportunity to review the manuscript. The corresponding author had full access to all the data in the study and final responsibility for the decision to submit for publication. FB acknowledges a PhD grant from  Ecole Normale Supérieure.
This spring period lasted for about 40-50 days, and therefore covered four or five pollen sampling dates. The spring decline in pollen harvest was then estimated from the apiary level as the relative peak-to-trough decline in the daily pollen harvest (see Data analysis, Fig. 1e ). M O N I T O R I N G H O N E Y B E E C O L O N Y D Y N A M I C S The colony dynamics were monitored by measuring three core colony traits: the amount of brood in the colony (termed brood size), adult population and honey reserves. Three of the five colonies in each apiary were monitored bimonthly throughout the monitoring period. Complete details may be found in  Odoux et al. (2014). About 15 sets of measurements were taken each year (Table  S1 ) giving 1636 series of colony traits measured one 257 different colonies 9 year (three colonies per apiary and the replacement of dead colonies).
Colonies may allocate more space to honey reserves in the hive brood chamber by reducing the brood size (both workers and drones). To quantify the in-hive space allocation strategy, colonies were qualified as reserve maximizers or brood maximizers in comparison with the average pattern using the relationship between honey reserve and total brood area (see data analysis, n = 1636), and using the residuals as a proxy for the strategy. The sexual reproductive effort of the colony was also assessed using the relationship between worker (female) brood and drone 0•0 0•5 1•0 1•5 2•0 2•5 0•0 0•5 1•0 1•5 2•0 2•5 3•0 (a) (b) (c) (d) (e) (f) (g) (h).
Identifying carry-over effects The carry-over effects of the decline in pollen harvest on subsequent colony dynamics were determined by testing correlations with ever-increasing time-lags. Colony dynamics data were binned into 2-week periods, starting from the beginning of the pollen decline period (n = 10-48 per period, Table  S1 ). The statistical link between the decline in pollen harvest and subsequent colony dynamics (the three core colony traits and the Varroa mite load) was assessed for each period using LMs (see Table  S1  for complete results). The seasonal and overwinter colony survival rates were calculated once a year and analysed using binomial generalized linear models (GLM) with a logit link function (glm function in the R nlme package).
Acknowledgements This study was supported by grants from the  French Ministry of Agriculture  ( CASDAR ,  POLINOV  project), the  Poitou-Charentes Region  and the  European Community Program  ( 797/2004 ) for French beekeeping ( RIS-QAPI  project). V. B. was also partially supported by  ANR AGROBIO-SPHERE AGROBIOSE  ( AGRO-2013-001 ). Special thanks go to  P. Aupinel ,  M. Chabirand ,  C. Le Mogne ,  M. Liaigre ,  T. Tamic  and  C. Toullet  for help in the fieldwork, and  P. Legagneux ,  S. D. Leonhardt  and  J. Osborne  for their constructive comments that greatly improved the manuscript. We also thank  Tony Tebby  for critical reading of English used in the text, as well as the associate editor  Romina Rader  and three anonymous referees for their helpful comments.
In particular these three features, which CHARISMA has been specially designed to promote, are not necessarily compatible (or consistent) with each other (i. e. they can be somewhat selfcontradictory, e. g. the desire for Open Access can potentially compromise security, if the architecture does not appropriately take this into account, e. g. via appropriate tenant isolation measures) and so impose their own additional constraints on how the architecture is best designed. Conversely, these particular features can also act to reinforce and help each other, e. g. the desire for end-to-end low latency can also act to assist in the secure operation of the network, e. g. by reducing the scope for interception or breakdown over long lengths of the topology, since low latency tends to require data to be processed (transmitted, etc.
Requirements and Challenges The abovementioned use cases facilitate the requirement elicitation for the CHARISMA architecture. In the requirement analysis process, the similar requirements, belonging to different use cases, are merged and in case where different KPIs are considered for the same requirement, the most stringent KPI is taken into account. Following are the high-level consolidated requirements for CHARISMA: • Support for low latency services: CHARISMA architecture shall support low latency services (≤ 1ms) via, i) Routing of data at the lowest common aggregation point, ii) devolved offload strategies for device-to-device, device-to-remote-radio, device-to-baseband, device-tocentral office/metro, etc. , and iii) mobile distributed caching. • Support for advanced end-to-end security: CHARISMA architecture shall support distributed security as well as physical layer security.
Architecture Definition A key architectural innovation of CHARISMA is the adoption of a self-similar hierarchical approach, with active nodes intermediate to the central office (CO) and end-users. The CHARISMA 5G architecture described has been designed to exhibit low-latency (towards the 1-msec KPI of the 5G-PPP programme) as well as security and open access. Achieving low latency requires data to be handled (i. e. routed and/or processed) as close to where it is required (i. e. either at the receiving end, and/or at the source end). Indeed, this implies that a low-latency architecture requires network intelligence to be located as close to the edge as possible, such that traffic which is expected to remain local never needs to travel towards the core of the network; minimizing transmission latency.
Likewise, in cases where data is frequently required (e. g. from a popular video streaming source) it makes sense to store that video data at a location close to where it is frequently accessed; in such a way, access time latency can also be minimized. Overall, this requires the CHARISMA architecture to be much more distributed in nature, as compared to more centralized 5G architectures, e. g. as typically exemplified by the purely C-RAN architecture, where intelligence is almost completely located in the Central Office (or Central Node). The legacy C-RAN network might also have had some limited storage at the RRH (equivalent to CAL2 in Fig. 3 ), CHARISMA's much more distributed and hierarchical approach sees such intelligence, processing and caching (i. e. in the IMU at each CAL node) pushed out also to the small cell (CAL1 at the rear of the bus) and at CAL0.
Low Latency CHARISMA targets end-to-end low latency with a multiple prong approach including, cooperative hierarchical caching and routing, hardware acceleration, and high data throughput in the aggregation network. CHARISMA offers a unified content delivery solution in the access and aggregation networks, and for device-to-device (D2D) communications latencies towards the 1-msec 5G KPI. CHARISMA caching solution is based on cooperative hierarchical caching, i. e. , caching decisions are made both locally and globally at each cache. Beyond CDN, the concept of in-network caching and information centric networking (ICN) also allows cache functionalities to reside at network devices like routers, switches, etc. [2]. The latter allows such devices forming the CHARISMA hierarchical in-network caching system to be controlled through a centralized SDN controller that can be used to manage/control content replicas by keeping track of the location and availability of content in distributed locations.
To mitigate this trend, CHARISMA also proposes the use of a smart network interface card (NIC) armed with NFV acceleration for the data path as a means to reduce latency, power consumption, and also CAPEX. In the back-haul or aggregation network, respectively, CHARISMA is investigating OFDM-PON technology  [5] , both as a means to achieve high data throughputs at a low cost and as a means to reduce network latency. Key parameters here are an aggregated data rate of 100 Gb/s together with 1024 subcarriers providing an additional degree of freedom for media access to provide effective virtualization. Here, latency is dominated by input buffering, error correction, and synchronization. Simulations show a processing delay due to MAC and PHY signal processing in the low µs range, which is already well below the propagation delay of 50 µs for a 10-km fibre connection.
V. Conclusions This paper presented a virtualized hierarchical and intelligent 5G access network architecture, based on hierarchical CAL and flexible IMU technology, suitable for services and applications requiring low-latency, enhanced security, and multi-tenancy. To achieve the low-latency requirements, CHARISMA explores hierarchical routing with in-network caching, NFV acceleration for data paths using smart NICs, and OFDM-PON technology. In order to meet the enhanced security (both physical and virtual) and multi-tenancy challenges, the proposed architecture employs network slicing concept along with SDN and NFV principles. In this paper, we have also listed various use cases, where CHARISMA's innovation architecture can play a vital role for enabling required 5G networking solutions. The requirements extracted from these use cases allowed to refine the CHARISMA architecture.
The free induction decays were multiplied by an exponential window function with a 1 Hz line-broading factor prior to Fourier transformation. All NMR spectra were initially phase adjusted, and then the baseline was corrected by using Mestrenova 7. 0 software (Mestrelab Research SL, Spain). Chemical shift was referenced to the peak of the methyl proton of L-lactate at δ 1. 33. NMR spectra (δ 0. 5-9. 5) were integrated into regions of 0. 002 ppm wide by using Mestrenova 7. 0 software (Mestrelab Research SL, Spain). Regions distorted by imperfect water saturation were discarded with the regions containing urea signals. These regions were δ 4. 47-5. 18, δ 5. 5-6. 0 and δ 4. 28-4. 45. Subsequently, each integral region was normalized to the total sum of all integral regions for each spectrum prior to pattern recognition analyses.
Statistical analysis Data are presented as means with pooled SEM, unless otherwise specified. The pen was considered the experimental unit for statistical analyses. Body weight, feed intake, milk composition, jejunal antioxidant and mRNA abundance data were analyzed by using the GLM procedures of SAS statistical package (version 8. 1; SAS Institute, Inc. ). The following statistical model was used: Y ij = μ + T i + e ij where Y is the analyzed variable, μ is the overall mean, T is the effect of treatment (i = 1…3), and e is the residual error (i = 1…3, j = 1…6). The least significant difference test was used to compare the group means when the F test in the analysis of variance table was significant. Meanwhile, body weight at postpartum d7 was compared with that at postpartum d0 by a two-sample paired t test.
Multivariate data analysis was carried out on the normalized NMR datasets with the software package SIMCA-P+ (v11 • 0, Umetrics, Sweden). Principal component analysis (PCA) was performed to show an overview of intrinsic similarity/ dissimilarity within the data sets. The orthogonal projection to latent structure-discriminant analysis (OPLS-DA) was further carried out using the unit-variance scaled (UV) NMR data as X-matrix and class information as Ymatrix  [21]. The quality of the model was assessed by the parameters R 2 X representing the total explained variations for X matrix and Q 2 indicating the model predictability. The models were validated by two methods: a cross validation method and a permutation test  [22, 23]. The models were interpreted by the coefficient coded loading plots.
The loadings were backtransformed in Excel (Microsoft, USA) and plotted with color-coded absolute coefficient values (|r|) of the variables in MATLAB  [22]. The coefficient plot indicated the significance of variables (resonances) that contributed to the differentiation of classes of interest. The significant discriminatory metabolites were indicated in red color whereas blue color showed no significance. In the present study, appropriate correlation coefficients were used as the cutoff values (depending on the number of animals used) for the statistical significance based on the discrimination significance at the level of P < 0. 05; such was determined according to the test for the significance of the Pearson's product-moment correlation coefficient  [22]. P < 0. 05 was considered statistical significance.
Multivariate data analysis result of NMR data PCA was initially performed on the spectral data, and two principal components were calculated for the six treatment groups with a total of 44. 4 % and 19. 3 % of variables being explained by PC 1 and PC 2, respectively. The PCA results (Fig. 2a ) showed that separations between postpartum d0 and d7 in sows were observed in their metabolic plasma profiles from the CON, DLM, or HMTBA groups, respectively. The plasma metabolic changes in the sows from postpartum d0 to d7 were analyzed by using the OPLS-DA strategy. A plot of OPLS-DA scores showed a clear separation between the sows at postpartum d0 and d7 (Fig. 2b, c, d ). The corresponding coefficient plot showed that the plasma levels of acetamide, choline and low density lipoprotein (LDL) in the CON group were higher (P < 0.
(2013). Fluorescence was converted to a normalized substrate cleavage rate using an AFC standard (Sigma) and normalized to total protein concentrations obtained from the same samples. Total protein concentrations were determined by a Pierce ™ BCA protein assay kit (Thermo Scientific product #23225). 2. 9 16S rRNA gene sequencing and data analyses Bacterial community diversity was analyzed by deep sequencing of the 16S rRNA gene in samples from two replicate bottles from experiment 1 (see Sect. 2. 2) at three time points each. Seawater samples were filtered on 25 mm, 5 µm pore-size Supor filters (Pall Gelman Inc. , Ann Arbor, Michigan), snap-frozen in liquid nitrogen, and stored at -80 • C for later extraction. Community genomic DNA was isolated from the filters using a phenol-chloroform extraction method modified according to  Massana et al.
The amplicons were sequenced at the Bar-Ilan Sequencing Center, using an Ion Torrent ™ (Life Technologies, USA). The adapter-clipped sequences were processed using tools and scripts from the UPARSE pipeline  (Edgar, 2013). Reads from all samples were pooled for operational taxonomic unit (OTU) calling. Reads were de-multiplexed and primers and barcodes were stripped using the script fastq_strip_barcode_relabel. py, leaving 42 747 raw reads altogether for six samples. As suggested for OTU calling from single-end amplicon sequences  (Edgar, 2013) , sequences (mostly between 280 and 300 nt) were trimmed to a fixed length of 280 nt, and shorter sequences were dis-carded (26 740 trimmed raw reads remained). For OTU clustering, trimmed raw reads were quality-filtered using the -fastq_filter command with a maximum expected error rate (-fastq_maxee) of 2 (21 590 reads remaining), clustered into unicals (100 % identity) and the unicals sorted by weight (number of sequences in the cluster).
OTU clustering with an identity threshold of 0. 98 was done using the -cluster_otus command on sorted unicals, with built-in chimera filtering. To infer OTU abundances for each individual sample, the trimmed raw reads per sample (after a more relaxed quality filtering with -fastq_maxee 5) were mapped back to these OTUs with -usearch_global and a minimum identity of 98 %. For taxonomic classification, OTUs were submitted to https://www. arb-silva. de/ngs/ and classified using the SINA aligner v1. 2. 10 and database release SSU 123  (Quast et al. , 2013). Sequences having (BLAST alignment coverage + alignment identity)/2 < 93 % were considered unclassified and assigned to the virtual group "No Relative" (5. 58 % of OTUs).
Bioinformatics processing and analysis of metatranscriptome data To remove adapters, perform quality trimming, and set a minimal length cutoff, raw fastq reads were processed with Cutadapt version 1. 8. 1  (Martin, 2011)  in paired-end mode with a minimum adapter sequence overlap of 10 nt (-O 10), an allowed error rate of 20 % (-e 0. 2) in the adapter sequence alignment, and a minimum base quality of 20. To remove residual ribosomal RNA reads, the fastq files were further processed with SortMeRNA version 1. 8  (Kopylova et al. , 2012)  with the accompanying standard databases in pairedend mode, resulting in 9 469 339 non-ribosomal reads for T 0 , 22 407 194 for T 8 , and 18 550 250 for T 22. The fastq files with all non-ribosomal forward reads were used for mapping against the Trichodesmium erythraeum IMS101 genome with Bowtie2  (Langmead and Salzberg, 2012)  in very-sensitivelocal mode.
Data availability The raw transcriptomic data for T 0 , T 8 , and T 22 are available from NCBI short read archive under the bioproject accession number PRJNA304389, biosample accessions SAMN05207415, SAMN05207416, and SAMN05207417. The Supplement related to this article is available online at doi:10. 5194/bg-13-4187-2016-supplement.
Acknowledgements. Funding was obtained for  Ilana Berman-Frank  through a collaborative grant from  MOST Israel  and the  High Council for Science and Technology (HCST) ,  France , and a  United States-Israel Binational Science Foundation (BSF)  grant (no. 2008048 ) to  Ilana Berman-Frank  and  Kay D. Bidle. This research was partially funded by the  Gordon and Betty Moore Foundation  through grant  GBMF3789  to KDB. The participation of  Ilana Berman-Frank ,  Dina Spungin ,  Ulrike Pfreundt , and  Wolfgang R. Hess  in the VAHINE experiment was supported by the  German-Israeli Research Foundation (GIF) , project number  1133-13. 8/2011  to  Ilana Berman-Frank  and  Wolfgang R. Hess , and the metatranscriptome analysis by the  EU  project  MaCuMBA (Marine Microorganisms: Cultivation Methods for Improving their Biotechnological Applications ; grant agreement no.
311975 ) to  Wolfgang R. Hess. Funding  for the VAHINE experimental project was provided by the  Agence Nationale de la Recherche  ( ANR starting  grant  VAHINE ANR-13-JS06-0002 ),  INSU-LEFE-CYBER program ,  GOPS ,  IRD , and MIO. The authors thank the captain and crew of the  R/V Alis , the SEOH divers service from the  IRD research center of Noumea  ( E. Folcher ,  B. Bourgeois , and  A. Renaud ) and from the  Observatoire Océanologique de Villefranche -sur-mer (OOV,  J. M. Grisoni ), and the  IRD research center of Noumea  for their helpful technical support. Thanks especially to  E. Rahav  for his assistance throughout the New Caledonia experiment and to  H. Elifantz  for assistance with the 16S sequencing and data analysis. This work is in partial fulfillment of the requirements for a PhD thesis for D.
FASII bypass in natural isolates carrying FabD polymorphisms. Our results show that the CondT R -17 G196R substitution correlates with FASII bypass via incorporation of exogenous fatty acids. We searched public databases for natural S. aureus isolates carrying FabD modifications at the G196 position, and found three such isolates. Two independent sputum and nare isolates carry FabD R196 : M0251 (a methicillin-resistant S. aureus (MRSA) clonal complex CC5 isolate, BioSample accession code SAMN02325289) and M1532 (BioSample accession code SAMN02364048). The third strain, SA40, is a community-and epidemic-associated methicillin-resistant sequence type ST59 that was isolated from a healthy child and carries a FabD S196 protein. A closely related ST59 strain SA957 is an MRSA blood isolate that does not carry the FabD 196 polymorphism  45.
The detection limit was 10 2 CFU per organ; samples with no CFUs were plotted as '0'. In DepT R -5 samples, one mouse showed no detectable counts in liver or kidney. G196 þ þ þ þ - - SA957 Blood FabD G196 þ þ þ þ - - SA40 Nare FabD S196 þ þ þ þ þ þ þ þ Exo MO251 Sputum FabD R196 þ þ þ þ þ Exo MO1532 Nare FabD R196 þ þ þ þ þ þ þ þ Exo CondT R -4 FAT R FabD V196 þ þ þ þ þ þ þ Exo CondT R -17 FAT R FabD R196 þ þ þ þ þ þ þ Exo The NCBI database was searched for natural S. aureus isolates bearing FabD proteins with residues other than G196. Three such strains were received: CC5 isolate M0251, M1532 (Brigham and Women's Hospital, Boston; Mary Delaney and Michael Calderwood), and ST59 strain SA40 a Taiwanese commensal strain (Go ¨tz and Stemmler;  45  ). ST59 strain SA957, whose fabD is identical to that of SA40 except for position 196, was used as reference for SA40 (Go ¨tz and Stemmler  45  ).