dataesr/scientific-paragraphs-categorization huggingface dataset:
At the latter, Brodgar Isthmus, microfossil analysis and radiocarbon dates demonstrate that the Loch of Stenness, adjacent to the Brodgar Isthmus, was transformed from a freshwater to brackish environment during the early Neolithic at a time when there was a marked increase in sedimentation. It was inferred that this was probably related to occasional marine flooding during exceptional storm events  (Bates et al. 2016). Given that the 5500-year BP Garth tsunami has been reported from the Shetland Islands (100 km NE) and that the Afen slide to the NW of both island archipelagos is a potential source, it remains to be seen whether further studies of the coastal sediments laid down in the early Neolithic may be related to one or both processes of storm and tsunami. On balance, the archaeological data from Orkney's CCs may be considered equivocal, although the intense period of early Neolithic cairn construction and use suggests an unusual, possibly unique, catastrophic driver.
In the case of West Voe, oysters, limpets and mussels point to a rocky substrate, whereas cockles are active suspension feeders, living in the top few centimetres of what is normally a clean sand  (Ducrotoy et al. 1991; Global Invasive Species Database 2017). Furthermore, cockles are easily dislodged by storms which suggests that while there may have been increased storminess around this time  (Sorrel et al. 2012; Walsh et al. 2012) , these storms are unlikely to have significantly affected this area. An examination of the sand layer separating the two middens was restricted to the West Voe site and so no lateral variations are known. However, it was comprised of a structureless, normally graded (fining-upwards) medium sand with shallow water benthic foraminifera that are not typical in aeolian sediments  (Gillmore and Melton 2011).
However, as we have worked through numerous examples in this paper, it has become apparent that it is difficult to find sufficient detail from any one site or region to be categorical in determining a tsunami link with a mass burial. In many ways, this is because no one has been looking for the type of evidence necessary or has assembled the data in such a manner that allows this scenario to be considered. There is undoubtedly a much more acceptable case to be made for a tsunami-mass burial link in tectonically active areas (e. g. Pacific: Solomon Islands, Vanuatu; Mediterranean: Greece) as opposed to less active areas like the northern Atlantic. The argument here is not that every mass burial is tsunami-related, but that in a coastal context in particular, there is the danger of not considering it and, as such, misinterpreting much about human-environment interactions in prehistory.
Often the data are not available to effectively address the question because the research carried out did not consider such a scenario. While this is understandable for work carried out prior to the 2004 IOT, it is a notable omission from more recent research. Finding palaeotsunami evidence that is contemporaneous with a coastal mass burial(s) is an important step in making a link between them. In some cases, the geological record for a contemporaneous palaeotsunami can be lost through taphonomic processes, but the effect of it on the coastal human population at the time may be preserved in a more enduring form such as a mass burial, with or without any additional associated archaeological/anthropological evidence. The key point is that finding evidence for past tsunamis does not rest solely in the domain of the geologist but rather across the disciplinary divides of geology, geomorphology, archaeology and anthropology.
RESEARCH DATA Sample Selection Database for this study is provided by Tan Viet Securities Company (www. tvsi. com. vn) and cross-checked with Stockbiz's (www. stockbiz. vn). The sample period is from 1 January 2006 to 31 December 2011. To avoid bias in the research findings, observations are eliminated from the research sample if they meet the following criteria: 1. Observations experiencing events, namely stock splits, stock dividends, share repurchases and right issues within 21 days from Day -10 to Day +10; 2. Observations with missing or incomplete information including price data, trading volume data and dividends; 3. Observations without transactions for more than 10 days in the estimation period. After the above elimination, the research sample contains 781 observations. Following  Milonas et al.
The ATLAS Collaboration has started a vast programme of upgrades in the context of high-luminosity LHC (HL-LHC) foreseen in 2024. We present here one of the frontend readout options, an ASIC called FATALIC, proposed for the high-luminosity phase LHC upgrade of the ATLAS Tile Calorimeter. Based on a 130 nm CMOS technology, FATALIC performs the complete signal processing, including amplification, shaping and digitisation. We describe the full characterisation of FATALIC and also the Optimal Filtering signal reconstruction method adapted to fully exploit the FATALIC three-range layout. Additionally we present the resolution performance of the whole chain measured using the charge injection system designed for calibration. Finally we discuss the results of the signal reconstruction used on real data collected during a preliminary beam test at CERN.
Due to bandwidth limitations, only two gains are forwarded to the output. Auto-selection of the data to be transmitted is performed between the high and the low gains, while the medium gain data are always included. This auto-selection is performed digitally inside the chip based on the saturation of the most sensitive channel. Fig. 3  shows an example of an analogue signal at the FATALIC shaper output (using a digital scope) and a digitised event at the ADC output, both shown for a muon event. The dynamic performance of the whole chain is measured in terms of resolution using the dedicated charge injection system designed for calibration. Fig. 4  shows the linearity results for the three gains of the previous FATALIC ASIC, FATALIC4b. It can be seen that the non-linearity is contained within 2% for the high and medium gains over their whole range.
1377 The design of FATALIC 5 will overcome this problem by changing the electronic transmission of digitized data. Nevertheless, data were taken with particles. Figure  106  shows, that once the pedestal will be adjusted, they will be stable because there is no AC coupling 1358 in between the PMT and the ADCs. Dynamic performances are measured by using first the 1359 charge injection system, then light-emitting diodes on a dedicated test bench. Figure  103  shows the linearity results for the three gains by using the CIS.
1377 The design of FATALIC 5 will overcome this problem by changing the electronic transmission of digitized data. Nevertheless, data were taken with particles. Figure  106  shows, algorithm, the dependence of S i on τ can be linearised via Taylor's expansion at first order: S i p + Ag(t i ) -Aτ g (t i ) + n i = p + Ag i -Aτ g i + n i (2) Three sets of free parameters a i , b i and c i are defined, called OF weights, such as: A = N i=1 a i S i = N i=1 a i S i ( 3 ) where N is the number of samples. The OF procedure aims at minimizing the variance of the linear combinations of these parameters subject to a set of constraints. These constraints lead to a set of linear equations  [5] , which prompt the derivation of the OF weights and hence the amplitude, pulse time and pedestal can be calculated.
RT-qPCR Primer Wheat Design and Validation Using the software SPADS (Specific Primers and Amplicon Design Software, saruman. versailles. inra. fr) primers were designed for a RT-qPCR application: an amplicon size between 60 and 150 bp, a melting temperature between 55 and 62 • C and a percentage of GC between 30 and 80%. The selected primers (Supplementary Table  1 ) were validated using nullisomic lines and cloning technology (Supplementary Figure  1 ).
2). Hits were filtered by considering only the 552 genes that showed an alignment with at least 40% amino-acid identity covering at least 50% of the length of the query sequence. Wheat homeologous genes were further identified using BLAST  (Altschul et al. , 1990)  with a threshold of 90% minimum identity. The phylogenetic analysis was conducted using MEGA version 7  (Kumar et al. , 2016)  using CLUSTALW  (Thompson et al. , 1994)  for multiple protein sequence alignment and the Maximum Likelihood method based on the JTT matrix-based model  (Jones et al. , 1992). Sequences for markers harboring QTLs for Fusarium Head Blight resistance were obtained from the GrainGenes database and a BLAST analysis was developed to localize sequences on chromosomes using Triticum_aestivum_CS42_TGAC_v1 assembly.
, 2005) , constituted of a stretch of 44 amino acids. Among the 245 amino acids in common to the three proteins Traes_2AS_99FF5CO43. 1, Traes_2BS_14CA35D5D. 1, and Traes_2DS_5CEOA969D. 1, sequence identity ranges from 83% (2A-2D) to 89% (2A-2B) and similarity from 89% (2A-2D) to 91% (2A-2B, 2B-2D; Supplementary Presentation 1). We then built a phylogenetic tree based on a multiple alignment of wheat protein sequences (IGWSC CSS v2 assembly for Triticum aestivum) that are homologous to the Bradi5g03300 protein. Only 9 wheat proteins with a BLASTP score higher than 400 were considered here, encoded by genes located on wheat chromosomes 2 (including the three homeologs identified in the microsynteny analysis) and chromosomes 5 which were designated on the phylogenetic tree after the corresponding gene accession numbers.
Traes_2BS_14CA35D5D Potentially Maps to a QTL for FHB Resistance Sequences for markers harboring QTLs for Fusarium Head Blight resistance were obtained from GrainGenes database and a BLAST analysis was developed to map sequences on wheat chromosomes using the IGWSC CSS v2 assembly for T. aestivum. Among the three homeologous genes, only the Traes_2BS_14CA35D5D gene was found co-located with a QTL responsible for FHB incidence and narrow opening of flowers between markers Xbarc200 and Xgwm210 surrounding this QTL  (Gilsinger et al. , 2005).
The 2BS and 2DS Homeologous Genes Are Induced Following Infection by a DON-Producing F. graminearum Strain The three identified homeologous wheat genes identified as potential orthologs of the B. distachyon Bradi5g03300 gene were then studied at the transcriptional level. In order to reinforce their potential involvement in DON detoxification, the expression level of genes Traes_2AS_99FF5CO43, Traes_2BS_14CA35D5D, and Traes_2DS_5CEOA969D was determined following inoculation with two different strains of F. graminearum: FgDON + able to produce DON and FgDON -not able to produce DON  (Cuzick et al. , 2008). Using the SPADS software (Specific Primers and Amplicon Design Software, saruman. versailles. inra. fr), primer pairs specific for each gene were designed for a RT-qPCR application and were validated using nullisomic lines (Supplementary Table  1  and Supplementary Figures  1A-C ).
ACKNOWLEDGMENTS MG's Ph. D. fellowship was supported by the  DIM ASTREA  ( 2014-2017 ,  Ile-de-France ). We gratefully acknowledge  V. Changenet ,  Dr. L. De Bont , and  Dr. C. Saintenac  for their valuable suggestions and discussions. The authors wish to thank  V. Daric  for his contribution as bioinformatic support. IPS2 benefits from the support of the  Labex Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
Virtual olfactory environment: Spatio-temporal character of the pheromone plume and stimulus distribution ORNs were stimulated with the major sex pheromone component of A. ipsilon, (Z)-7-dodecenyl acetate (Z7-12:Ac). Pheromone stimuli were diluted in decadic steps in hexane and applied on a filter paper introduced in a Pasteur pipette at doses ranging from 10 -6 to 10 0 ng. The antenna was constantly superfused by a humidified and charcoal-filtered air stream (70 L � h -1 ). Air puffs (10 L � h -1 ) were delivered through a calibrated capillary (Ref. 11762313, Fisher Scientific, France) positioned at 1 mm from the antenna and containing the odorloaded filter paper (10 × 2 mm). An electrovalve (LHDA-1233215-H, Lee Company, France) was controlled by custom Labview programs reading sequences generated using Matlab scripts.
Data analysis The data were analyzed using the R programming environment  [23]. In total, we analyzed recordings of 217 moth ORNs obtained at 5 virtual distances and for 7 levels of pheromone dose. For each combination of virtual distance and pheromone dose we had 3-11 recordings of distinct ORNs, with the exception of 128 m and 1 ng dose, a category that was not studied because the occurrence of extremely long whiffs induced a complete interruption of the spiking activity at this high pheromone dose. Because the activity of ORNs is independent of other neurons  [24] , all the recordings obtained with a particular dose of pheromone and at a particular virtual distance were pooled and analyzed together. Latency correction. The experimental setup induces latency between the valve opening and the actual pheromone delivery, mainly due to the time needed for the air to pass through the capilary to the sensillum.
This work was supported by the  Institute of Physiology RVO : 67985823 , by the  Czech Science Foundation  project No. 17-06943S , by  Mobility Project between France  and the  Czech Republic  through Grant No. 7AMB17FR059  and by the state program Investissements d'avenir managed by  ANR  (grant  ANR-10-BINF-05  "Pherotaxis"). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All participants were imaged with 82 Rubidium-positron emission tomography (Rb-PET) as part of the RUBIS study (clinicaltrials. gov: NCT01679886). Examinations were analyzed by experienced readers and classified as abnormal or normal for the presence of myocardial ischemia based on the results of the stress test and myocardial perfusion imaging (MPI). If MPI was classified as abnormal, an invasive coronary angiography was proposed to patients to assess coronary stenosis. Patients with hemodynamically relevant stenosis on coronary angiography (coronary stenosis ≥ 50% with fractional flow reserve ≤ 0. 8, critical coronary stenosis without FFR and coronary occlusion), or patients with myocardial ischemia but who did not accept to undergo coronary angiography were excluded from this cohort to avoid the confounding effect of stenosis of epicardial coronary arteries.
Patients with type 1 diabetes (n = 9) were excluded. National ethics committee approved the study protocol; all patients provided signed informed consent; the full description of the study design has been registered on clinicaltrials. gov (NCT01679886). In addition, the local ethical committee (CEERB Paris Nord-IRB 00006477-of HUPNVS, Paris 7 University, AP-HP) approved the retrospective access to clinical and biological data acquired in these patients.
Image analysis Stress and rest LVEF were measured using the 4DM-SPECT software (4DM; INVIA, LLC). Stress and rest myocardial blood flow was quantified on dynamic PET acquisitions using a one-compartment model for 82 Rb uptake in the myocardium of the FlowQuant software  [18]. In addition, rest myocardial blood flow was corrected for cardiac workload as described  [19]. Myocardial flow reserve (MFR) was calculated as the ratio between stress myocardial blood flow (MBF) and corrected rest MBF. For clinical convenience, MFR was primarily displayed as a dichotomous variable using a cut-off point of 2. 0 for an impaired ratio. MFR < 2. 0 has been shown to be associated with worse cardiovascular outcomes in a general referral population  [20, 21].
We compared MFR in patients with diabetes in different subgroups according age, sex, smoking status, diabetes duration, HbA1c, eGFR, BMI, presence of hypertension, antiplatelet agents and statins use. As sensitivity analyses, we used a threshold of 2. 5 for impaired MFR and we excluded patients with CAC > 400 of our analyses. Authors had full access to all the data and take responsibility for its integrity and the data analysis. All statistical computations were performed using the JMP software (version 9, SAS Institute Inc. , Cary, NC).
Taxonomic assignment, diversity and statistical analyses The OTUs' taxonomical annotation was performed using blast against Greengenes 13. 5, Silva 128 and RDP classifier for 16S rDNA and UNITE  [45] , Targeted Host-associated Fungi (THF) version 3 (Underhill)  [37] , and Findley  [46]  for ITS1 analysis, applying criteria of !75% and !94. 5% homology with reference sequences for annotation at phylum or genus level  [47] , respectively and <10 -5 e-values. A complementary analysis of fungal OTUs non-assigned to the genus level with UNITE, THF or Findley database was performed using MycoBank  [48]. The normalization of OTU counts was performed at the OTU level using DESeq2 normalization method  [49]. The generalized linear model (GLM) implemented in the DESeq2 R package was then applied to detect differences in abundance of genera, at general or individual level, between the extraction condition (IHMS, MOBIO) and the RNAlater1 usage (yes, no).
However, when environmental conditions are unfavorable or when combined transcriptomic analysis is planned, RNAlater1 remains an interesting option. To confirm our findings, complementary data regarding the role of nucleic acid stabilizer solutions on combined fungal and bacterial microbiota analysis should be necessary. Our study also emphasizes specific difficulties associated with the analysis of the fungal microbiota compared to the bacterial one. While fecal samples of healthy adults were processed exactly the same way and a similar amount of reads were obtained for 16S and ITS1 high-throughput sequencing, the fungal analysis could not be completed for one of the 3 individuals. Indeed, only a very low rate of ITS reads were assigned as fungal reads despite the use of multiple sequence databases, which lead to an insufficient assessment of the fungal diversity and the exclusion of the individual for further analysis.
By comparison, all 16S reads of the same individual were fully assigned as bacterial reads. This fact, also described by other authors  [16, 37, 50] , raises two hypotheses: it might either be a problem of unspecific DNA amplification due to the very low yield of fungal DNA obtained from fecal samples extraction ("rare biosphere"  [20] ) or a problem of insufficient annotation due to incomplete ITS sequence databases. This last issue was already discussed by different authors  [37, 46, 50, 51] , who highlighted the high rate of incomplete, incorrect or redundant taxonomic assignment in public fungal sequence databases. They also emphasize the evolving nature of the phylogenic relationships between fungi and the heterogeneity of fungal taxonomy to explain the difficulty to build a well-established and commonly-accepted database.
In 2015, Tang et al  [37] , after comparing the results of three existing databases (UNITE  [45] , Findley  [46] , and RefSeq Targeted Loci  [42]  databases), observed that the distribution of fungi detected in a sample could vary significantly according to the database used and thus proposed a new hand-curated database (the THF database). Unfortunately, two years later, using a combination of the most notorious ITS databases, we had to face the same difficulty again, as did Huseyin et al  [16] , who obtained extremely high percentages of unassigned ITS reads (> 90%) for 2/18 subjects. The amplification and/or database challenge is still a crucial limitation for ITS targeted metagenomics analysis, leading to underestimation of mycobiota diversity, and these issues require particular attention from the mycologist community over the upcoming years.
Participants and samples The participants for this study were part of the PISCINA II study, an experimental study performed in a 25 m long indoor chlorinated pool in Barcelona, Spain, between June and December 2013. It included 116 volunteers, aged 18-40 years, non-smoking and non-professional swimmers, who swam for 40 min at a leisurely pace in the swimming pool, resting at their own initiative. Four participants were evaluated per day, between 9 am and 2 pm (before having lunch). From these 116 volunteers, 60 were selected for subsequent metabolomic profiling. Selection criteria were defined to ensure that: i) complete exposure measurements in the swimming pool were available, ii) biological samples were available; iii) there was an even proportion of men and women; iv) data on physical activity, lifestyle, and all possible adjustments covariates (see below) were available.
4 mL/min, consisting of ultrapure water and LC-MS grade methanol, both containing 0. 1% (v/v) of formic acid. The gradient profile was as follows: 0-6 min: 5% → 100% methanol, 6-10. 5 min: 100% methanol, 10. 5-12. 5 min: 5% methanol. The mass spectrometer was operated in positive polarity using following conditions: drying gas (nitrogen) temperature 175 °C and flow 12 L/min, sheath gas temperature 350 °C and flow 11 L/min, nebulizer pressure 45 psi, capillary voltage 3500 V, nozzle voltage 300 V, and fragmentor voltage 175 V. Data was acquired using extended dynamic range mode across a mass range of 50-1200, with a scan rate of 1. 67 Hz. Continuous mass axis calibration was performed by monitoring two reference ions throughout the runs (m/z 121. 050873 and m/z 922. 009798). The analytical run was initiated with priming injections of a pooled quality control (QC) sample to achieve stable instrument response, followed by study samples that were intervened after every 10 injections with a QC sample and a solvent blank to monitor instrument performance.
Data processing Pre-processing of the acquired data was performed using Qualitative Analysis B. 06. 00 SP1, DA Reprocessor, and Mass Profiler Professional 12. 1 software (Agilent Technologies, Santa Clara, CA, USA). The initial processing was performed using find by molecular feature (MFE) algorithm. Threshold values for mass and chromatographic peak heights were 500 and 5000 counts, respectively, with a compound quality score threshold at 70. Only singly charged ions were included. The resulting features were combined into a single list using 0. 1 min retention time window for alignment, and those existing in at least 2% of all the samples were used as targets for a recursive feature extraction of the raw data using a find by formula (FBF) algorithm. For the recursive process, match tolerances were ± 30 ppm and ± 0.
Statistical analysis Correlations between the exposures and measurements of physical activity were calculated using Spearman correlation coefficients. Exposures for which > 25% of the measurements were missing (i. e. across participants and for both sampling occasions, before and after the swim) were excluded from subsequent analyses. Because the distribution of most metabolic features was skewed a log-transformation was performed on all features. Metabolic features that were detected in < 60% of the samples were excluded from the analysis and missing data in the remaining features were imputed using a quantile regression approach for left-censored missing data, implemented in the imputeLCMD R package  (Lazar, 2015). A common set of potential confounders at baseline was included in all statistical models: age, sex and BMI.