dataesr/scientific-paragraphs-categorization huggingface dataset:
Specimen For the purpose of this study, we re-examined the holotype of Concavicaris woodfordi, which is stored in the paleontological collections of the National Museum of Natural History, Smithsonian Institution, Washington, DC (USNM PAL 112025). This specimen was collected along the east side of U. S. Highway 77, Arbuckle Mountains, Oklahoma, USA (sec. 30, T2S, R2E; Cooper 1932; Figure  1C ), and is the only known specimen. Taphonomy. The specimen is preserved in 3 dimensions in a phosphatic concretion. Neither the anterior nor the posterior parts of the shield are preserved. Some soft-parts, including eyes, cephalic appendages, and part of the posterior trunk appendages are also not preserved. The tomographic data emphasize the differential preservation of the anatomical structures. Organs (circulatory and digestive systems) and appendages appear less dense in the tomograms, indicating a preservation in a low-density material or as empty mold.
ACKNOWLEDGEMENT We would like to thank  N. Poulet-Crovisier  and  F. Goussard  ( MNHN, Paris ) for their help during the processing and the segmentation of the scans. We also thank  M. Florence  ( USNM, Washington, DC ) for his help with tracking down the specimen at the Smithsonian institution. We finally acknowledge the editor,  L. Laibl , and the two anonymous reviewers for their comments that greatly improved the manuscript.
Identification and assembly of ICEs A broad family of P. syringae ICEs (PsICEs) was identified using BLASTn searches of a collection of sequenced Psa genomes, combined with genomes deposited in the NCBI Genbank and WGS databases (updated to July 2021)  [20]. When matches were identified in draft assemblies, contigs were downloaded and used to join contigs that overlapped by at least 6 bp. To delineate the chromosomal integration sites, flanking sequences were inspected for direct repeats (att) sites. The broader family of ICEs was defined with tBLASTn  [20]  searches in the NCBI GenBank WGS database (updated to April 2017).
Samples were further treated with the Turbo DNA-free TM kit (ThermoFisher AM1907) according to manufacturer's instructions. 210 ng total RNA was used for rRNA depletion with the bacterial Ribo-Zero kit (Illumina 20037135), according to manufacturer's recommendations. After rRNA depletion, the remaining RNA was fragmented, and Illumina-compatible libraries prepared using the NEBNext Ultra™ II Directional RNA Library Prep Kit for Illumina (New England Biolabs E7760S). Libraries were sequenced on the Illumina HiSeq3000 system. RNA sequencing data was analyzed as described in  [38]. Genes with statistically significant differential expressions (p<0. 05 and p<0. 01) were annotated with KEGG pathways  [39]  and visualized using scripts stored at https://gitlab. gwdg. de/guilhem. doulcier/pseudomonas_rnaseq/.
Phenotypic characterization of motility on TCA cycle intermediates Wild type and ∆Tn6212 mutant cells were grown in succinate and placed in Adler chambers with succinate or casamino acids (CAA) as attractants, and a PBS buffer control. Timeresolved data were collected by imaging at regular intervals (either 5 min intervals over the course of 1 h, or 500 ms over 15 s). The resulting images were subject to analyses that included measurement of cell swimming speed, directionality, and cell density at the moving swarm. The rate of radial expansion of both wildtype and ∆Tn6212 cells was measured by stab inoculating cells into semi-solid M9 agar containing either succinate (at pH 7. 0 and pH 6. 0), glucose (pH 7. 0) or CAA (ph 7. 0) as growth substrates. Stab inoculation was performed using initial cell density of either 10 5 or 10 6 cells, measuring radial expansion daily for 8 days using precision calipers.
An expanded family of ICEs is circulating in the P. syringae species complex Bacterial whole genome sequences (updated to November 2017) and complete bacterial genomes (updated to July 2021) in NCBI GenBank were searched with BLASTn  [20]  using as query ICEPsaCL1, ICEPsaI10, ICEPsaNZ13, ICEPsyB728a and ICEPph1302A (Table  S1 ) to identify homologous ICEs. This search resulted in a collection of 207 ICEs, collectively referred to as PsICEs (Table  S1 ). The 207 PsICEs (Table  S1 ) were found integrated adjacent to a tRNA-Lys gene, 41% in att-2 and 32% in att-1 (in the remaining 27%, the contig was too short to infer the position of the tRNA-Lys). The first attachment site (att-1) is proximal to clpB (Psa NZ13 IYO_024910), and the second site (att-2) is proximal to queC (Psa NZ13 IYO_008010)  [15, 40].
While inter-ICE recombination ICEs from different years sometimes cluster together, for example, the aforementioned ICEPdp529 (1957) and ICEPar4457 (1967) group with ICEPsaNZ13-like elements from 2016. To identify ICEs in Pseudomonas genomes other than those of P. syringae, the NCBI GenBank repository of Pseudomonas genomes (excluding P. syringae) was interrogated in April 2017 with the ICEPsaNZ13 DEAD-box helicase protein sequence, the most highly conserved gene among all PsICEs. Forty-four ICEs carrying DEAD-box helicases were identified in 41 Pseudomonas genomes, 82% of which were P. aeruginosa (Table  S4 ). All ICEs were integrated in one of the two att sites described for PsICEs, with the exception of an ICE in P. aeruginosa PA38182, which did not harbor recognizable att sites. Thirty-three ICEs were part of the pKLC102/PAGI-2 family of ICEs  [42]  (Table  S2 ).
PsICEs conserve their structure despite frequent inter-ICE recombination ICE sequences are typically composed of conserved backbone genes, predicted to be involved in the ICE life cycle, and variable accessory (or cargo) genes. Identification of the PsICE backbone was guided by identification of the set of genes present among at least 93% of all ICEs using ROARY  [23]. The backbone consists of a set of 62 genes (~56 kb) predicted to be involved in ICE maintenance, regulation and movement, as well as a number of conserved hypothetical proteins (Table  S3  and Figure  S3 ). Homologs of these genes in other ICEs have been shown to encode conjugation machinery (pil), ICE transfer (tra), partitioning (par) and integration (int, xerC) functions  [6]. The relaxed criterion chosen here (present in at least 93% ICEs) reflects the possibility of misassembly or gene deletion events (Figure  S3 ).
978, p < 0. 001) (Figure  S9 ). For each carbon source, the number of differentially expressed genes is highest in Psa NZ13∆dctT. This indicates that DctT functions, either directly or indirectly, as a repressor of genes on Tn6212. Given that DctT is a predicted transporter for di-carboxylic acids, it seems likely that information gathered via the transporter, perhaps concerning the nature of the external environment, is necessary for Tn6212 to coordinate ensuing effects on host cell gene expression. Making sense of the myriad transcriptional changes poses a major challenge. Of particular interest are those genes causally responsible for the observed changes. Figure  4C  shows the proportion of genes differentially expressed by Tn6212 when grown on succinate and connection to various cellular functions as defined by the KEGG database resource  [39].
Data are ranked by the proportion of genes with significantly altered patterns of expression and the entire dataset with graphical mapping to KEGG pathways can be viewed at http://www. normalesup. org/~doulcier/collab/2020_ICE/kegg/. The number of genes whose expression is differentially affected by Tn6212 when grown on glucose, for the same set of KEGG pathways, is shown for comparison. When grown on succinate, Tn6212 significantly, and with primarily positive effects, affects the expression of numerous genes in multiple KEGG pathways involved in translation (tRNA biosynthesis, one carbon pool by folate, sulphur metabolism, protein export, ribosome), post transcriptional control (RNA degradation), energy metabolism (oxidative phosphorylation, sulfur metabolism), carbohydrate metabolism (amino sugar and nucleotide sugar biosynthesis, TCA cycle), metabolism of cofactors and vitamins (thiamine, riboflavin, and biotin metabolism) and DNA metabolism (DNA replication, mismatch repair, homologous recombination).
In some instances, these show matches to known genes, but many are either function-unknown, or have no orthologues in databases. Of note is the relatedness of PsICEs to those from P. aeruginosa raising the possibility that ICEs move genes between plant and opportunistic human pathogens, although minimally-overlapping ecological niches likely limit ICE movement across larger phylogenetic distances. Many PsICEs carry accessory genes that confer a selective advantage to agricultural plant pathogens, like antimicrobial resistance and virulence-associated genes. The identification of cargo genes relevant to the bacterial host niche is not unusual, STX-R391 ICEs of clinical origin typically carry antibiotic resistance genes  [53] , while STX-R391 ICEs from free living marine bacteria Alteromonas mainly encode for metal resistance and restriction modification systems  [54].
Data Collection Data have been collected through face-to-face interviews by using two types of questionnaires each for kiwi producers (hereafter, 'farmers') and beekeepers. A small group of 5 kiwi producers and 6 beekeepers were interviewed to test and calibrate the questionnaire, but only minor changes were necessary to be applied. Both questionnaires had a similar format and were inspired and adapted by the study by Breeze et al. (  2019 )  [25]. The first part of the questionnaire was the same for both groups, referring to the demographic characteristics of the respondent (age, gender and educational level), number of hectares or beehives, whether they were amateurs or professional (for beekeepers) and the location of agricultural activity. The following section, regarding farmers, assessed the pollination services deficit attitudes, the implemented strategies to ameliorate pollination services (if any) and the prices for these services.
Data Collection Data have been collected through face-to-face interviews by using two types of questionnaires each for kiwi producers (hereafter, 'farmers') and beekeepers. A small group of 5 kiwi producers and 6 beekeepers were interviewed to test and calibrate the questionnaire, but only minor changes were necessary to be applied. Both questionnaires had a similar format and were inspired and adapted by the study by  Breeze et al. (2019)   [25]. The first part of the questionnaire was the same for both groups, referring to the demographic characteristics of the respondent (age, gender and educational level), number of hectares or beehives, whether they were amateurs or professional (for beekeepers) and the location of agricultural activity. The following section, regarding farmers, assessed the pollination services deficit attitudes, the implemented strategies to ameliorate pollination services (if any) and the prices for these services.
Out of the 334 beekeepers in total in the region, 49 questionnaires were collected, representing 14. 6% of the total, whereas for the kiwi producers from the Cooperation Municipality of Nestos, which has in total 100 ha of kiwi orchards, 5 members (8. 3%) were interviewed, who cultivated, in total, 20 ha of kiwi (20%). In addition, 31 farmers (12. 4%) were surveyed from the EAS Kavala cooperative, which had in total 250 members owning 169. 8 ha of kiwi orchards or 48. 5% of the total surface area of the cooperative. Furthermore, 11 kiwi producers from the Nespar cooperative (21. 5%) managing 56. 6 ha (37. 7%). Overall, 13% of the total kiwi cultivated area in the valley of Kavala, which accounts for 1900 ha, was covered. In total 104 questionnaires were collected, but only 96 were accepted (47 kiwi producers and 49 beekeepers) after the data validation process.
Additional information regarding the sample's characteristics can be found in Table  1. Descriptive statistics were extracted from all of the questions, and the most significant ones are visualized in the results section. Additionally, a Pollination Services indicator was created to assess the attitude of farmers towards pollination services. This indicator uses 4 questions from the survey related to (1) the perceived pollination services deficit, (2) the willingness to pay or willingness to increase the amount of money for pollination services, (3) the implementation of measures for encouraging wild pollinators (4) and planning to further support the existing wild pollinators by adopting alternative agricultural practices. Through the above-mentioned variables, the data type was collected, meaning that farmers either have a positive (1) or a negative (0) attitude towards PS.
Physocephala texana has been recorded parasitizing honey bees (A. mellifera), alkali bees (Nomia melanderi), and sand wasps (Bembix spp. ), and P. marginata has been recovered from honey bees and a leafcutter bee (Megachile mendica) as well  (Gibson et al. 2016; Parsons 1948). A modeling study, based on field-data of foraging and risk of conopid parasitism, suggested that conopids may not dramatically affect reproductive output of bumble bee colonies when resources are abundant, but may interact with low resource conditions to the significant detriment of colony demographic performance  (Malfi et al. 2018). Infection rates can be high; a study of Swiss bumble bees found on average 13. 2% of workers and 7. 1% of males contained a conopid pupa, with a maximum of 46. 7% of workers at one site  (Schmid-Hempel et al.
ACKNOWLEDGEMENTS This paper derives from a White Paper produced by the  Imperiled Bombus Conservation Task Force of the Pollinator Partnership 's North  American Pollinator Protection Campaign : https://www. pollinator. org/ nappc/imperiled-bombus-conservation. LLF was supported by the  National Science Foundation  ( DBI-2010615 ). BMS was supported by the  US Department of Agriculture  ( NIFA  grant  2017-67013-26536 ). We appreciate the thoughtful reviews of an earlier version of this manuscript from two anonymous reviewers.
Data Collection New-born screening hospital laboratories in England prepared DBS for the period of January 2014-December 2014. Each study participant was allocated a unique study number. Clinical details were recorded and stored in a secure password-protected database held on a UK Health Security Agency (UKHSA) laptop. Metadata included maternal age, severity of infant illness (defined as the need for Paediatric and Neonatal Intensive Care Unit (PICU/NICU) stay), and clinical outcome following disease (alive well, alive neurological impairment, death). All investigators apart from the UKHSA staff member responsible for collection of the DBS were blinded to the clinical information in the database.
Data Processing and Analysis A statistical analysis was performed using GraphPad Prism version 8. 0. 2 (GraphPad Software Inc. , San Diego, CA, USA). The means and 95% confidence intervals or median with interquartile range were calculated for each serotype depending on whether the data was normally distributed. Parametric (t-test) and non-parametric tests (Friedman, Mann-Whitney) were used to test for differences between antibody concentrations. A Deming regression was performed to estimate the systematic difference between antibody concentrations measured in the serum and DBS. A conditional logistic regression was used to assess the relationship between serotype-specific antibody titre and case/control status using cut-offs at 0. 3, 1, 3, and 10 ug/mL to represent a range on the log scale.
Case-Control Study We identified 211 DBS from 805 cases of GBS in England between January and December 2014 from the British paediatric surveillance unit (BPSU) study  [22]. DBS were retrieved from a total of 483 participants comprising 161 cases of invasive GBS disease and 322 controls. Of the 483 individual samples, 186 DBS samples (38. 51%) (61 cases and 125 controls) from three national screening laboratories were eluted and analysed, the remainder did not elute (Supplementary Material, Figures  S1  and S2 ). Gestational age and gender data was available for 98% of participants (61 cases, 125 controls). The baseline characteristics of the study participants are summarised below in Table  1. Prior to conducting further analyses on these samples, we sought to investigate potential parameters that would influence our results, this included haematocrit, storage temperature, freeze-thaw cycles, and the correlation between dried blood spots and serum from healthy adult donors.
To achieve a high detection rate, data normalization plays an important role in MLbased IDSs. In  [23] , the authors proposed a statistical method that can identify the most suitable normalization method for IoT and traditional network environments datasets that gives the highest accuracy for an IDS. Over the past few years, there has been a proliferation of efficient deep learning models like deep belief networks, deep convolutional neural networks, recurrent neural networks, and deep generative networks. These models have found application in the development of network IDSs  [3, 24]. [25]  introduced a deep learning model using stacked non-symmetric deep auto-encoders for accurate network intrusion detection. In  [26] , deep convolutional neural networks and weight-dropped long short-term memory networks are combined in big data environments to improve detection accuracy.
Authors in  [27]  developed a hybrid deep learning framework with convolutional and recurrent neural networks for predicting and classifying malicious network attacks. [28]  proposed a model that combines a convolutional neural network and a gated recurrent unit to address accuracy and class imbalance issues in intrusion detection. [29]  implemented an IDS using different recurrent neural networks. As for privacy reasons many users opt not to share their device-generated dataset with other devices. For this reason, the authors in  [30]  propose a Federated Learning-based approach for IoT intrusion detection problems. The imbalance between the attack and normal traffic has motivated the authors in  [31]  to propose an IDS called pretraining Wasserstein generative adversarial network. Studies have shown that running an IDS in a mobile device is energy and time-consuming and takes memory capacity.
Constraint  (15)  assures each SA is assigned only one detection level. Constraint  (16)  denotes that the security level for each SA should be selected such that it guarantees detection of 𝛿 𝑠 % of the cyberattacks. Constraints (  17 )-  (19)  ensure that the aggregated bandwidth, computational consumption, and monetary costs, respectively, of the selected levels among the SAs, do not exceed the maximum available network resources. The optimization problem assigns the detection level to the SAs such that the trade-off between maximizing the security detection efficiency and the QoS is addressed while also ensuring that the constraints are respected. 𝐏𝟏 is a Binary Linear Programming problem. Although in large dimensions it is NP-hard, due to the large coverage of SAs, and even for several hundred SAs it can be solved by using standard solvers such as CPLEX with low execution time on modest hardware  [41, 42].
Methods. We used data from the 2007-2010 United States National Health and Nutrition Examination Survey (NHANES) database and compared RA prevalence between subsamples with the presence of any two conditions among depression, obesity, and hypertriglyceridemia (HTG). Each subsample was further divided into three categories based on the serum level of the inflammatory marker C-reactive protein (CRP) and analyzed for statistically significant differences using three-way χ 2 tests of independence.
Since chronic, systemic inflammation with dysregulated immunity is a hallmark of RA and underlies most of its clinical manifestations, we speculate that comorbidities with a known inflammatory link would be highly pertinent in this context. 11  While many different comorbidities among RA subjects have been studied in detail, less information is available on whether the presence of multiple comorbidities poses a risk for the development or progress of RA. Depression is identified as a major RA comorbidity with a prevalence of 13-42% in RA patients and is associated with a poorer outcome with increased pain, disability, and risk of mortality. [12] [13] [14]  Analyzing longitudinal data from the National Health Insurance Research Database of Taiwan, Lu et al. reported that the incidence of depression among RA patients was 15.
Moreover, the frequent occurrence of these conditions in subjects with RA is suggestive of a common molecular underpinning potentially mediated through inflammatory mechanism. 26  The objective of this work is to investigate any potential relationship between depression, obesity, and HTG -each of which independently relates to chronic inflammation -in their association with RA. Using publicly available data from National Health and Nutrition Examination Survey (NHANES), we explored whether the coexistence of any two of these conditions synergistically enhances the association with RA. Considering their common connection with an inflammatory mechanism, we also studied if such association is related to the general inflammatory status of the subject.
Study Population The CDC-operated online portal NHANES offers detailed health data, obtained through interviews and physical examinations, on samples drawn from the US population. It employs a stratified, multistage, nationally representative cross-sectional survey design in order to provide countrywide inference. The survey protocol and data collection methods of NHANES data used in this study were approved by the National Center for Health Statistics Ethics Review Board (protocol #2005-06). The types of data collected by NHANES include demographic variables, socioeconomic condition (SEC), questionnaires, and bio-specimen examination. The participants are represented by a unique number in each dataset to protect their identity.
Data Collection Analyses were conducted on data from the 2007-2008 and the 2009-2010 NHANES datasets to examine the relationship of inflammation (measured by CRP) with the three primary factors of interest: depression, obesity, and triglyceride. These years were chosen because of their detailed information on CRP, which is not present in later years of NHANES data. Response rates for participation in both interviews and physical examinations were close for each two-year cycle and ranged from 75% to 80%. Subjects younger than 18 years were excluded from the analysis to prevent inclusion of juvenile RA and subjects with ages greater than 79 years were excluded as NHANES does not differentiate ages 80 years or above.
Other Variables CRP: CRP measurements in serum samples were obtained using latex-enhanced nephelometry. Measured CRP levels were further divided into three risk categories of low (< 1 mg/L), medium (1-3 mg/L), and high (> than 3 mg/L) based on the guideline provided by the Centers for Disease Control and Prevention (CDC) and the American Heart Association (AHA). 33  Demographics: Age, sex, and median family incomes were obtained from the NHANES database.
Statistical Analysis Each data point of the NHANES data was adjusted by implementing appropriate sampling weight according to the National Center for Health Statistics analytical guidelines to account for the potential bias created by the sampling procedure. 34  We used weights for triglycerides, the most discriminatory dataset used in our study, and then adjusted them to account for the inclusion of both the 2007-2008 and the 2009-2010 datasets. We have presented here the analyses both with and without the implementation of sample weights. All P-values presented here are from two-sided tests. P-values less than 0. 05 were considered statistically significant. Inter-group difference in abundance was evaluated using a Z-proportion test for dichotomous indicators and a two-way chi-square (χ 2 ) test was used for non-dichotomous indicators.