dataesr/scientific-paragraphs-categorization huggingface dataset:
The XANES spectra were collected in both total electron yield (TEY) and fluorescent yield (FLY) mode (only FLY is presented in this work) with a chamber pressure above 1 × 10 -7 torr at room temperature. The beamline slits' sizes were 50 µm × 50 µm with an instrumental resolution of E/∆E > 10,000. The data were normalized by the I 0 current, which was simultaneously measured with the FLY, by monitoring the drain current emitted from a Nickel mesh (90% transmission) placed in front of the sample. The Fe K-edge XANES was obtained at the soft X-ray micro characterization beamline (SXRMB). It can provide 10 11 photons/s at 100 mA with a resolution higher than 10,000 (E/∆E). The spectra were normalized with respect to the edge height after subtracting the pre-edge and post-edge backgrounds using Athena software.
Clinical Trials Interpretation of clinical trials related to TNBC remains very complex due to the heterogeneous response to systemic therapy. In the last decade, multiple clinical trials have been conducted to control TNBC growth and metastasis, in order to define new targeted therapies and novel biomarkers. Furthermore, targeting PI3K, AKT, HDAC, PD-1, AR and Hsp 90 in metastatic TNBC with specific inhibitors and antagonists was the topic of a substantial number of clinical trials, including NCT01629615, NCT01790932, NCT02162719, NCT02353988, NCT02457910, NCT02657889, NCT02513472, NCT02768701, NCT02734290, NCT01349959, NCT02898207 and NCT02474173. Moreover, the effect of different drugs such as dasatinib, iniparib, epalrestat, tinostamustine and others was evaluated in various clinical studies NCT00371254, NCT01173497, NCT03244358, and NCT03345485, in the search of therapeutic biomarkers for metastatic TNBC.
Up regulated Pro-tumoural  [133]  DKK2 promoter CpG methylation EMT suppressor by suppressing canonical Wnt/β-catenin signaling via inhibiting β-catenin activity. Downregulated Anti-tumoural  [134]  Some authors have tried to revert the triple negative character of TNBC patients in order to give them a chance to benefit from treatments such as trastuzumab (Herceptin ® ) or tamoxifen (NCT01194908). It has been demonstrated that ER is not absent in some TNBCs but is epigenetically silenced by several histone and methyl groups leading to its inactivation. Thus, some clinical trials have intended to evaluate the effect of epigenetic reprogramming. Histone deacetylase inhibitors such as LBH589 and demethylating inhibitors can rid of histone-or methyl-group excess; the reactivation of ER opens the door to use hormonal agents in order to control the growth and the metastasis of TNBCs.
Acknowledgments: This work is performed within the framework of  CPER Epicure 2016  project by the  Region Auvergne-Rhone-Alpes  and was supported by the " Ligue contre le cancer du Puy de Dôme ". We gratefully acknowledge  Nancy Uhrhammer  for her kind grammatical/spelling review of this paper. We also thank  Flora Ponelle-Chachuat  for assistance in the generation of the network diagram.
Statistical analyses Data were analyzed using the general linear models procedure of Statistical Analysis System (version 8. 2; SAS Institute Inc. , Cary, NC, USA) in a balanced 2 × 2 factorial experiment (2 basal diet' types and 2 concentrate' types) using the following model: Y ijk = μ + BD i + CT j + BD i CT j + e ijk where Y ijk represents the responses of the ewe k receiving the basal diet i and the concentrate j; µ is the mean; BD i is the basal diet (i = straw or straw + RR); CT j is the concentrate type (j = control or linseed concentrate); BD i × CT j are the interactions between BD and CT.
Acknowledgments This work was financially supported by the  Tunisian Ministry of High and Scientific Research through 'Animal and Forage Production Laboratory '. The authors would like to thank the  'Societe de Nutrition Animale' (SNA)  for their donation of linseed;  Dr M. Chikhaoui  is specially acknowledged; and  Ms Taghouti Zina  from  PAF Laboratory of INRAT  for help in laboratory analysis.
Post-harvest management of crop microbiomes Contributed by Jasna Kovac, Taejung Chung, and Xiaoqing Tan Understanding the taxonomic composition and functional role of post-harvest crop microbiomes is essential for enhancement of the quality and safety of plant foods  (Bokulich et al. 2016 ). Early challenges with the sensitivity of direct metagenomic sequencing for detecting lowabundant microbial taxa of interest relevant to plant food quality and safety have led to a paradigm shift in approaches to microbial hazard detection. As opposed to direct detection of low abundance pathogens of interest, changes to microbiomes in which pathogens and spoilage microorganisms commonly occur are beginning to be explored. Detection of such microbiome perturbations requires a baseline database of microbiomes and their transcriptomes in samples collected throughout the food supply chain.
Mesocosms were planted with Brachypodium distachyon and inoculated with Glomus versiforme. After establishment, a 50 m mesh core (1. 5 cm x 13. 5 cm) containing a sand and soil mixture (1:1 v/v) was inserted to allow hyphal colonization of the substrate while excluding plant roots. Hyphae were harvested by removing the core, submerging the soil/sand matrix and collecting hyphae under a dissecting scope for downstream DNA extraction. Tagged 16S rRNA gene amplicon libraries were prepared at the University of Minnesota Genomics Center (Minneapolis, MN) and sequenced on the Illumina MiSeq system. Preliminary data reveal an ERH associated community distinct from both rhizosphere and bulk soil and that this community forms in a soil-dependent manner. Hyphal communities were dominated by representatives of the Gamma-, Delta-and Betaproteobacteria, as well as the Cytophagia (Bacteroidetes).
Soil type had the largest impact on microbial community composition, indicating the important driving force of the indigenous microbe pool for rhizosphere microbe assembly. The influence of the soybean cultivar in rhizosphere microbiome assembly was also found to be significant though smaller, with a drought tolerant cultivar and a wild type accession (Glycine soja) showing greater differences from other genotypes. Overall, the alpha diversity of the soybean rhizosphere was significantly reduced in comparison to bulk soil, and similarly, the co-occurrence network in rhizosphere featured with less complexity. To explore the difference of microbe function potential between rhizosphere and bulk soil, Tax4Fun software were used to predict microbial functional and metabolic capacities. Antibiotic producing pathways were significantly higher in bulk soil microbial community, while flagellar assembly, bacteria secretion, and phosphotransferase systems were more enriched in the rhizosphere microbial community.
Fungal endophytes do not appear to be vertically transmitted in magnolia. Arils and seeds are relatively devoid of endophytic fungi. Endophytes are not detected in leaves of lab-grown plants. Spore fall under the canopy is very high and allowed us to capture C. acutatum as a spore. Additionally, our potted endophyte-free plants can acquire endophytes under the canopy. These data indicate entry at the level of the leaf through stomata, hydathodes or penetrative entry as expected for many endophytes. We will also report on the foliar endophytes of root sprouts from our study tree and of volunteer magnolia seedlings. To ask about endophyte entry through the vascular system, we examined about 60 petioles from a branch of our magnolia tree for cultivable endophytes. The endophyte population appeared to be somewhat restricted and was dominated by two fast growing species.
Sequences were analyzed using Mothur open-source software  (Schloss et al. 2009). Operational taxonomic unit (OTU) classification, rarefaction curves for species richness and the inverse Simpson Index for diversity are being analyzed and will be reported to establish the composition of the typical sweet cherry leaf microbiome in managed orchards. Additional sampling and analyses of the leaf microbiome will be conducted in spring and summer to determine potential seasonal changes as well.
Our data support a mechanism during competition of increased percent AMF root colonization of the invasive and decreased of the native. When specific measurements of net photosynthetic rate, leaf N, and leaf P were taken, in competitive mixtures the invasive plant performed better with AMF inoculum added and the native plant performed worse. Metabolic synchronization: How plant root exudation and microbial substrate preferences interact to shape rhizosphere microbiomes Kateryna Zhalnina, 1 Yuan Wang, 2 Nameer Baker, 3 Joelle Schläpfer Sasse, 1 Eden Tepper-Fobes, 1 Katherine B. Louie, 1 Ulas Karaoz, 1 Dominique Loque, 4 Benjamin P. Bowen, 1 Kelly Craven, 2 Jennifer Pett-Ridge, 5 Mary K. Firestone, 1,3 Eoin L. Brodie 1,3 and Trent R. It has been known for more than a century that plants interact with soil microorganisms through their exudates, however the mechanisms of how plants shape their rhizosphere microbiome are poorly understood.
ACKNOWLEDGEMENTS We thank  Joy Drohan  for recording notes during the workshop, which contributed to the structuring of this manuscript. We also thank all workshop and symposium participants for insightful discussions on these and other topics that came up during the event. Funding support for the  Wild and Tamed Phytobiomes Symposium  was provided by  Penn State University , through the  College of Agriculture, Eberly College of Science , and the  Huck Institutes of the Life Sciences , with additional funding support from  WrightLabs, Pall Laboratory, Pacific.
(  6 )). " /$ %&% &% $ (6) In the calculation of safety factors, the continuous reductions or increments of the cohesion and the friction angle are conducted until = 0 (provided in Eq. (  5 )), which represents the limit state where the working rate contributed by external forces is equal to the ones contributed by dissipations. The aforementioned calculation is achieved by a so-called dichotomy process (Fig. 4 ) with the help of the optimization tool of Genetic Algorithm built in MATLAB. The adopted dichotomy contains: 1) select a suitable initial interval $ ' , $ ). The upper bound $ ) represents an stable state (i. e. < 0) and the lower bound $ ' represents an unstable state (i. e. > 0); 2) calculate $ by averaging $ ' and $ ) ; 3) compute critical face pressure by maximizing Eq. (  5 ) with reference to the variables: * /+, * (see Fig.
Study design and patients This is a study of the performance of S100B protein for screening and identifying neonates at risk of neurological complications hospitalized in a NICU, combining biological data and serum cord blood measurement of S100B from an already constituted prospective biobank for which arterial cord blood was collected. At first, we established cord blood serum S100B reference intervals from preterm and fullterm infants without neurological complications. Our project took advantage of a research program funded by the CIHR Institute of Human Development, Child and Youth Health Initiative (Grant Number: NRFHPG-78880). An extensive clinical database (environmental, psychosocial, socio-demographic and anthropometric, clinical personal and family history data) was developed with a comprehensive biobank (blood samples including umbilical cord) from pregnant women recruited prospectively from the first trimester/early second trimester and followed during pregnancy.
Data collection The data collected were: gestational age at birth, maternal age, smoking during pregnancy, hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), abruptio placentae, intrauterine growth restriction (IUGR), multiple pregnancies, chorioamnionitis, mode of delivery, use of instrument (forceps, suction cup) during vaginal delivery, gender of newborn, birthweight and length of newborn, head circumference, APGAR scores and blood gas analysis results. Diagnosis of brain injury relied on imaging examination. Diagnosis of hypertensive disorders of pregnancy (HDP) was made by a senior obstetrician according to the classification of the Society of Obstetricians and Gynaecologists of Canada based on information retrieved from medical records, which includes gestational hypertension (GH) and preeclampsia.
Attention was paid on the study of interactions between significant factors and on the presence of multicollinearity. The model was validated by a two-step bootstrapping process. For each step, bootstrap samples with replacements (n=1000) were generated from the training set. In the first phase, the percentage of models including each initial variable was determined by usual stepwise approach. Then, in the second phase, parameters of generalized linear regression (exact logistic regression) of the final model were independently estimated. The bootstrap estimates associated to each covariate regression coefficient, and their associated standard errors, were finally averaged from replicates. Statistical analyses were carried out using STATA software (version 12, StataCorp, College Station, US).
Acknowledgements: The authors thank the research nurses for the recruitment of participants and retrieval of data from the medical records and  Alexandra Castillo  for S100B dosages. This work was supported by the  Canadian Institutes of Health Research (CIHR, Healthy Pregnancy Initiative  from the  Institute for Human Development, Child  and  Youth Health , Grant number:  NRFHPG-78880 ). The authors thank  Roche Diagnostics (Laval, Canada)  for providing the s100 kits. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Survey Design and Sampling Data were obtained from the Uganda Food Consumption Survey (UFCS)  [20, 21]. The UFCS, a cross-sectional survey conducted between May and September 2008. It remains the most recent nationally representative food consumption survey in Uganda, even though there have been subsequent regional-level surveys. The UFCS was undertaken in three regions, collectively data representing rural and urban Uganda. A representative sample of women (n = 957) aged 15-49 years was recruited using a multi-stage cluster sampling technique. The sampling procedure used for the UFCS has been published in full detail elsewhere  [20, 21]. Ethical approval for the 2008 UFCS was obtained from the Ugandan National Council for Science and Technology (UNCST) on 30 April 2008 and from the AED/Research Integrity Department in Washington, DC on 5 January 2008  [20, 21].
Data Collection A single quantitative 24 h recall was used to collect information on all the food and drink consumed by participants in the 24 h preceding the interview. To ensure accuracy of the data, the multi-pass method was employed  [20, 21]. Locally-developed food picture charts were used to estimate portion sizes for dietary intake during the last stage of the multi-pass interview  [20, 21]. Dietary data were collected by trained field assistants who were primarily graduates of either Human Nutrition or Food Science and Technology from Makerere University  [20]. Interviews took place at participants' households on all days of the week to account for variations between weekday and weekend day intakes  [20, 21]. Data were not collected on "festive" days since on such day's intakes are not representative of usual intakes  [21].
Food intake (grams) for the food categories was then standardized to account for portion size effect  [34]. Standardized intakes were used to identify possible outliers (±6SD)  [35]. For individuals with intakes identified as possible outliers, food intake (grams) for all food items consumed over the 24 h period was scrutinized for plausibility, based on knowledge of the local context. Intakes deemed biologically implausible were removed from the dataset, but not the individual's entire dietary intake. PCA was run with the standardized intakes as the input variables, with missing cases excluded pairwise, to account for implausible intakes removed during data cleaning. Varimax rotation, with Kaiser Normalization, was used to interpret the components  [36]. Eigenvalues >1, in combination with the scree plot (inflection point), were used to identify possible components to retain.
Sample Characteristics Although 957 women were sampled for the UFCS, dietary data of only 955 women were available for use at the time of this study. The sample of women who participated in the UFCS was largely rural (67. 2%) compared with urban (32. 8%)  [20, 21]. Most study participants were younger than 35 years of age and were either married or living with a partner. Urban women were more highly educated than their rural counterparts, with 90% of them having completed at least the primary level of schooling, compared with 72% of rural participants. Urban participants were also relatively wealthier than rural participants. Over 75% of urban participants were in the two highest wealth quintiles, while approximately 65% of rural participants occupied the two lowest wealth quintiles  [20, 21].
Some strengths and limitations of our study must be highlighted. To the best of our knowledge, this is the first study that describes dietary patterns of Ugandan WRA, through both a transitioning and environmental sustainability lens. Data for this study were drawn from a large representative sample (n = 955) that included participants across a range of socio-economic, occupation, and ethnic groups, possibly increasing the generalizability of these findings to other Ugandan WRA. Our sample was disproportionately rural, which can be highlighted as a limitation. However, we believe our sample was somewhat reflective of the skewed population distribution in the country given that approximately 7 in 10 WRA live in rural areas  [4]  while about 80% of the general population are rural dwellers  [4, 5].
More recent surveys have been carried out since the UFCS, e. g. , the nationally-representative 2014 Uganda non-communicable disease (NCDs) risk factors STEPS survey, which collected data on men and women's fruit and vegetable consumption patterns and the Africa/Harvard School of Public Health Partnership for Cohort Research and Training (PaCT) pilot study, which collected food consumption data from rural and peri-urban men and women using an FFQ. However, at the time of analysis, the 2008 UFCS was the most comprehensive dietary intake survey available, collected from a nationally-representative sample of both rural and urban Ugandan women. We were also unable to carry out estimations for energy intake in our study, which is a limitation that we acknowledge. Lastly, data for this study were collected using a single 24 h recall.
Although the 24 h recall is the most appropriate for use in resource-limited contexts where populations might be unable to keep food records, it is limited by recall bias and relies heavily on the skills of the interviewer. However, for the UFCS, data were collected by trained interviewers. Additionally, in each of the sampled regions, repeat 24 h recall interviews were conducted on 10% of the study participants on a non-consecutive day to account for intra-individual variation in food intake  [20, 21].
Statistical analysis The statistical analysis was performed using SPSS 14. 0 for Windows, version 14. 0. 1 (SPSS Inc, Chicago, IL, USA. BMI was set as a continuous variable in order to allow using parametric tests and maximizing the power of the analysis, but ordinal BMI was also used for comparison. The survival curves and log-rank tests were calculated and plotted according to Kaplan-Meier. Hazard ratios were calculated via a Cox multivariate regression model.
Each sample was sequenced on four different flowcell lanes to avoid lane effect. In total, 269,440,904 reads were obtained for the six SG samples, 257,744,832 reads for the three AT samples, and 1,678,378,894 reads for the 33 head samples. Raw data are available in NCBI Sequence Read Archive (https://trace. ncbi. nlm. nih. gov/Traces/sra/) with reference numbers shown in Supplementary Table  S2.
De Novo Assembly Reads from the three SG samples from P123 (this study) and LSR1  (Boulain et al. , 2018)  were trimmed using trimmomatic (version 0. 36, options ILLUMINACLIP:TruSeq3-PE-2. fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36), and an assembly for each biotype was done using Trinity (v2. 5. 1)  (Grabherr et al. , 2011). Lowly expressed contigs were removed by applying a filter with RSEM (--fpkm_cutoff 0. 5, -isopct_ cutoff=15. 0) (Li and Dewey, 2011). The remaining contigs were mapped on the LSR1 reference genome (The International Aphid Genomics Consortium, 2010) with gmap (version 2018-03-25)  (Wu et al. , 2016). Unmapped contigs from each LSR1 and P123 SG library were searched against the nonredundant protein database using a blastx (BLAST+ v2. 5. 0, e-value = 1e-8)  (Camacho et al.
1), the Rhopalosiphum padi virus (NC_001874. 1), the Brevicoryne brassicae virus (NC_ 009530. 1), the rosy apple aphid virus (DQ286292. 1), the Aphis glycines virus 2 (NC_028381. 1), the Macrosiphum euphorbiae virus 1 (NC_028137. 1), the Myzus persicae densovirus (NC_005040. 1), and the Dysaphis plantaginea densovirus (NC_034532. 1). Three gene expression analyses were conducted separately (with SGs and ATs only, with LSR1 and P123 heads only and finally with all heads) following previously described workflows  (Chen et al. , 2016; Lun et al. , 2016). First, the raw fragment counts were converted to counts per million (CPM) using the edgeR  (Robinson et al. , 2010 ) R-implemented package (R Development Core Team, 2017). Expressed genes were filtered based on a CPM > 1 in at least three of the libraries incorporated in the analysis and then CPMs were normalized using the edgeR TMM method for Normalization Factor calculation  (Robinson and Oshlack, 2010).
Copy Number Variation Analysis Population genomic data from  Nouhaud et al. (2018) , that consisted of Illumina sequencing of two pools of 60 pea-or alfalfa-adapted genotypes (pool-seq) with coverage values >110X each, were used to evaluate copy number variation. The reads from pea and alfalfa biotype pools were mapped following the protocol described by  Nouhaud et al. (2018) , only primary alignments were kept, and low-quality mapping (q < 20) and identically located reads resulting from PCR duplication were removed with MarkDuplicates from Picard tools (http://broadinstitute. github. io/picard/). A mean coverage for each exon for each biotype was then computed with Bedtools coverageBed  (Quinlan, 2014)  with the mean option. The mean coverage of each gene was then calculated by summing its exon coverages and dividing by its total exon size.
Differential Expression of Candidate Salivary Genes Is Associated With Copy Number Variation Between the Two Biotypes As the biotype-specific differential expression of salivary genes may result from copy number variation between the alfalfa and pea biotypes, we examined the sequence coverage of the genomes of the two biotypes using the genomic pool-seq data created previously  (Nouhaud et al. , 2018). Comparison of the sequencing coverage ratio between the two biotypes revealed copy number variation. Mean coverage ratio (pea/alfalfa) of the alfalfa-up set was significantly lower than the non-DE set and that of the pea-up set was significantly higher. This pattern was observed among the salivary effectors (Figure  3B ) as well as in the SG-upregulated effectors (Supplementary Figure  S5 ).