dataesr/scientific-paragraphs-categorization huggingface dataset:
Sites and Variables The literature search produced 67 papers (Supplementary S1) reporting assemblage and prevalence data falling within our constraints with a total of 162 unique sites in Asia, Europe, North America, and South America  (25, 17, 88, and 32 , respectively) that were used for analysis. The abundance range (20 ≤ n ≤ 200) was determined to capture the most normal distribution of assemblage abundances (mean = 102; median = 55; SD = 176). Significant differences existed between the MPD, MNTD, and PD, (MPD:MNTD t 161 = -7. 31, p < 0. 001; MPD:PD t 161 = 36. 54, p < 0. 001; MNTD:PD t 161 = 29. 18, p < 0. 001). Therefore, all measures of phylogenetic relatedness were used as individual descriptors in the analysis. However, though the mean Chao estimates (ACE and CHAO1) of species richness based from raw abundance (n) were each different from raw richness (s) (t 161 = 6.
86, p < 0. 001; t 161 = 4. 87, p < 0. 001, respectively) and from each other (t 161 = 4. 69, p < 0. 001). These estimates often inflated site richness or were incalculable altogether. Thus, we used the raw species richness value reported in the literature in our dataset. Variables accounting for compressed phylogenetic and diversity characteristics are shown in Figure  1. As expected, the PCA of the assemblage diversity characteristics indicated strong correlations between (s), (PIE), and (H') (Figure  1a ). This can be attributed to the relationship and mathematical influence these metrics may share in their calculation. However, the phylogenetic assemblage descriptors appeared to have independence (Figure  1b ). t161 = 4. 87, p < 0. 001, respectively) and from each other (t161 = 4. 69, p < 0.
001). These estimates often inflated site richness or were incalculable altogether. Thus, we used the raw species richness value reported in the literature in our dataset. Variables accounting for compressed phylogenetic and diversity characteristics are shown in Figure  1. As expected, the PCA of the assemblage diversity characteristics indicated strong correlations between (s), (PIE), and (H') (Figure  1a ). This can be attributed to the relationship and mathematical influence these metrics may share in their calculation. However, the phylogenetic assemblage descriptors appeared to have independence (Figure  1b ).
While steps were taken to standardize the dataset extracted from the literature, there are obvious differences embedded in the methods, techniques, sampling effort and other factors that created a heterogeneous dataset. However, despite the inherent variation created by the intrinsic nature of the dataset, what is worth emphasizing is the consistent response of phylogenetic variables as important among the contrasted models. Though exploratory, our study indicates that a potential avenue to move forward in the diversity versus pathogen prevalence debate is to ascertain the effects of the phylogenetic legacy of species on the main drivers (i. e. , host density, contact rates, and transmissibility) of disease transmission  [40]. Though it may be unusual to find a high prevalence in diverse assemblages, it is important to consider the species interactions and spillover maintenance of hantavirus infection within the context of community scale.
Participants This study involved a group of 10 elite male soccer players belonging to the squad of a Serie A club in Italy qualified to compete in the UEFA Europa League. The mean ± SD age, body mass, and height were 27. 3 ± 2. 8 years, 78. 1 ± 4. 6 kg, and 1. 83 ± 0. 08 m, respectively. Data were collected at the end of the domestic competition, at the beginning of the pre-season, and 6 weeks and a half later during the competition period. To be eligible for the study, players were required to meet the following criteria: (i) have a current professional contract with the first team, (ii) be injury free at the moment of the initial assessment, (iii) have completed >90% of the pre-season training sessions. Players had extensive previous experience in eccentric-overload training using flywheel devices throughout the entire previous season (one or twice a week).
Anthropometric and body composition analysis Body mass was measured with an electronic scale (OHAUS Corp. , Florham Park, NJ) and stature with a stadiometer (Seca 213, Hamburg, Germany). Body composition (FM and FFM) was assessed by DXA (Hologic QDR Series, Delphi A model, Bedford, MA, USA) using Hologic APEX software, version 13. 3:3, and according to the manufacturer's recommended procedures. Before any measurements, the DXA was calibrated each day with phantoms, as per the manufacturer's guidelines. The participants assumed a stationary, supine position on the scanning table, with hands level with the hips and feet slightly apart, as in a recent study  (Suarez-Arrones et al. , 2018b). The athletes removed metal objects or jewellery from their body and wore the same minimal clothing (underwear) for each scan.
The athletes were also instructed to follow a standard protocols of food and fluid to ensure hydration was optimal before each scans  (Bilsborough et al. , 2014). All scanning and analyses were performed by the same operator to ensure consistency and in accordance with standardised testing protocols recognised as best practise  (Milsom et al. , 2015; Nana et al. , 2016; Rodriguez-Sanchez & Galloway, 2015). Whole-body data are reported as total body, excluding the head  (Milanese et al. , 2015). Scans were performed in the morning before training and fat-free mass and fat-mass along with other parameters were calculated.
In soccer, an excess of adipose tissue loads the player with useless extra weight  (Kraemer et al. , 2004)  and likely contributes to greater energy expenditure during a match and impaired power and acceleration performance  (Rico-Sanz, 1998). Therefore, to enhance soccer performance, players need to begin the in-season period (competition) in the best possible condition for success, with optimal levels of FM. In the present study, whole-body data by DXA showed substantial decreases in FFM (-2. 1%) after the off-season period. Similar results were found by Reinke et al. (Reinke et al. , 2009)  after a DTP avoiding physical activity for at least three weeks, with a significant reduction in lean mass (-3%). When FFM was evaluated by body segments, our results only detected decreases in the arms and trunk regions, while lower limb FFM after the DTP remained unchanged.
This is the reason why it still does not exist an exhaustive Rho-dependent terminators prediction program. Results: In this study we introduce RhoTermPredict, the first published algorithm for an exhaustive Rho-dependent terminators prediction in bacterial genomes. RhoTermPredict identifies these elements based on a previously proposed consensus motif common to all Rho-dependent transcription terminators. It essentially searches for a 78 nt long RUT site characterized by a C > G content and with regularly spaced C residues, followed by a putative pause site for the RNA polymerase. We tested RhoTermPredict performances by using available genomic and transcriptomic data of the microorganism Escherichia coli K-12, both in limited-length sequences and in the whole-genome, and available genomic sequences from Bacillus subtilis 168 and Salmonella enterica LT2 genomes.
Intrinsic terminators can be identified using bioinformatics approaches with on line algorithms/tools. Among them, currently available and most performing tools for prokaryote intrinsic terminator prediction include TransTermHP  [22] , RNIE  [23] , the commercial program Softberry's FindTerm  [24]  and ARNold  [25]. Conversely, Rho-dependent terminators have so far proved difficult to predict computationally because the sequence features required for the function of Rho are complex and poorly defined, in contrast to intrinsic terminators features  [1]. In fact, only recently the first prediction model for Rho-dependent termination of transcription was proposed  [26] , but it was designed specifically for predicting only RUT sites, omitting the presence of RNAP pausing sites where Rho induces RNAP release from RNA.
RhoTermPredict is the first program implemented for an exhaustive search of Rho-dependent terminators, which functions in two steps to specifically identify this type of transcription termination sites within a genome sequence. Our aim was to create a program for the prediction of such elements in a prokaryotic genome based on a conserved structured motifs search, in a similar way to our previous work regarding the promoter prediction algorithm G4PromFinder  [27]. This novel algorithm searches for proposed C > G content RNA motif  [1, 5, 9, 28]  as possible C-rich element in Rho-dependent transcription terminators, followed by a possible pause site for RNAP. We tested RhoTermPredict performances by using available genomic and transcriptomic data of the model microorganism E. coli K-12 and a list of Rho-dependent terminators obtained by  [18].
Implementation Programming language and data sets RhoTermPredict algorithm was written in the Python (v. 3. 6) language  [32] , and requires libraries BioPython, numpy, re and openpyxl. It accepts as input bacterial genome sequences, and provides as output the coordinates of putative Rho-dependent terminators elements (RUT and RNAP pause sites) with a score assigned to them that indicates the probability that the extracted region actually corresponds to a Rho-dependent terminator (see below for the scoring assignment method). For Rho-dependent terminator predictions, we used available genomic sequences of the model microorganism E. coli K-12 substr. MG1655 (National Center for Biotechnology Information, accession code NC_000913. 3) (see below). For the prediction quality evaluation, we used E.
Dataset of rho-dependent terminators, and construction of positive and negative sequence datasets To test the reliability of RhoTermPredict predictions we used a total of 1264 regions containing Rho-dependent termination sites (defined as "BCM significant transcripts", BSTs) obtained by  [18]  growing up the microorganism E. coli K-12 with or without the specific Rho inhibitor bicyclomycin at a concentration that reduces Rho function without affecting the rate of cell growth. Therefore a differential expression of these BSTs regions indicates the presence of a Rho-dependent transcription terminator. Starting from the genome of E. coli K-12 (NC_ 000913. 3) and the just-indicated BSTs regions, we generated a Rho-dependent terminator set (positive set) consisting of 300 nt long sequences immediately upstream all the BSTs regions.
RNA-sequencing, reads mapping quality assessment Ribosomal RNAs were depleted using RiboZero Gram negative kit (Epicentre,Illumina) and strand-specific sequencing libraries were constructed using the Script-SeqTM v2 RNAseq library preparation kit. After that, the purified cDNA library was sequenced on an Illumina GAIIx/Solexa or MiSeq platform (Illumina, San Diego, CA) with 76-bp paired-end reads. The BAM files for each condition analysed are publicly available at Sequence Reads Archive (SRA) under accession number BioProject PRJNA483864. Alignment to the reference strain of E. coli K-12 genome (Ref Seq NC_000913) was done using bowtie2 (with sensitive options, corresponding to -D 15 -R 2 -L 22 -i S,1,1. 15; see the Bowtie2 manual for the explanation of the flags -D,-R,-L,S, http://bowtie-bio.
6, as reported in Table  1 ), while in Fig. 3  we show the distributions of distances occurring between the 3′-end points of Rho-dependent terminators RUT sites and the 5′ end points of relative annotated BST regions. Then we evaluated RhoTermPredict performances on terminator prediction using a positive set of sequences including the 300 nt long regions immediately upstream of all the BSTs regions, and a negative set of sequences represented by IRs of E. coli K-12 genome, located between two divergently oriented CDSs with length between 200 and 300 nt (see Implementation section for details). To use without bias the positive and negative sequence datasets, which originally did not have the same size, we randomly selected 195 sequences (negative set size) of the positive sets, and performed the tests 10 times with different series of randomly selected sequences to obtain mean values (Table  2 , columns 1 and 2).
Conclusions Based on our outcomes, we could conclude that the algorithm RhoTermPredict is specific on searching for Rho-dependent terminators and could be used in combination with an intrinsic terminators prediction tool/ program for the prediction of all transcription terminators in a bacterial genome. The action of Rho is largely unknown in most microorganisms; in fact the Rhodependent transcription termination was studied in a depth and exhaustive way only on E. coli, S. enterica and B. subtilis. For this reason an exhaustive list of putative Rho-dependent terminators, necessary for the creation of the positive set for the validation of the algorithm, is available only for the three previous analyzed genomes and, so, we could not use other genomes. But we could say that RhoTermPredict is certainly performant in E.
is paper describes the redesign, field-testing, and convergent validity of a practical tool-Physical Activity Campus Environmental Supports (PACES) audit. Methods. e audit includes two parts: (1) PACES-Programs, which is comprised of questions regarding populations served, fees, programs (recreation/fitness classes and intramurals), proximity, adequacy of facilities, and marketing, and (2) PACES-Facilities, which is comprised of questions regarding built environment (aesthetics, bike racks, stairs, and universal design), recreation equipment, staff, amenities, and access. Each item criterion is specifically scored using a fivepoint, semantic-differential scale ranging from limited to extensive environmental support. A few questions utilize select all that apply for a summed score. PACES training, interrater reliability, and data collection are all accessible via an online portal.
In 2009, the PACES audit was conducted at thirteen universities in the United States by trained researchers. PACES audit categories included built environment (bike racks, health promotion signage, and stairwells) and campus recreation programs (availability and quality of equipment, exercise spaces, courts/fields; availability of health education and intramural programs; and recreation facility hours, staff, and amenities). Data were collected with a simple checkoff paper survey tool. To more effectively evaluate and compare the quality and extensiveness/completeness of campus recreation facilities and programs, the purpose of this study was to update/ redesign and validate PACES. e updated PACES will be more accessible for monitoring and evaluating campus recreation facilities and programs, with a more user-friendly online data collection format and scored results.
Overview. is paper is divided into two parts. Part one includes (1) development of inventory of items for the redesigned PACES audit; (2) expert, cognitive, and pilot testing; (3) survey analysis and revisions; and (4) fieldtesting. Part two validates PACES by comparing PACES-Facilities to PARA  [23]  and PACES-Programs to Healthier Campus Initiatives (HCI)  [25]. Data were collected between 2015 and 2017 and analyzed in 2018. is study was deemed exempt by Syracuse University IRB because it was an environmental audit not human subject research. is audit was designed to rate the quality and extensiveness of recreation facilities and physical activity programs. It can be used for municipalities, worksites, schools, and college campuses to evaluate one venue or to understand a more complete picture of the recreation facilities/programs for a specific environment by evaluating a number of venues.
Expert, Cognitive, and Pilot Testing. is audit was cognitively tested with seven research assistants. Each student independently attempted to apply/score the audit for two different facilities. Via a group discussion, each question was discussed to determine interpretation, clarity, and appropriateness of semantic or Likert scales. Five public health/recreation program experts reviewed the audits for content validity. e results from the cognitive testing and expert review improved wording of questions and response items. e audit was pilot tested twice. Changes based on pilot testing included dividing it into two surveys for ease of administration and refining the wording of a few questions/ responses (Summer 2014 at SU and Fall 2014 data not shown).
Two different assessments were completed on each campus PACES-Facilities and PACES-Programs audit. A PACES-Facilities survey was completed for each recreation facility on/off campus, whether it was for a main recreation facility, secondary/satellite facility, or one component of a facility. For campus programs, one PACES-Programs survey was completed per campus. Training and Interrater Reliability. Research assistants completed video training and practiced and performed interrater reliability (IRR) exercises. Each participating community had its student researchers complete IRR on two recreation facilities. Interclass correlations > 0. 80 were required for each team prior to data collection. Starting in 2017, the IRR procedures were converted to an online quiz.
PACES-Facilities and PACES-Programs audits have been validated for college campus-type work environments but might also be useful in a variety of settings including communities, worksites, colleges, and schools. A limitation of this study is that only college-educated populations on and near college campuses have used and tested the audit, and it therefore needs to be validated for use by other data collectors to determine the utility of PACES in communities beyond college campuses. Although many recreation facilities audits are perception-based  [17] [18] [19] [20] [21] 45] , and some contradict objective findings  [52] , the PACES audit should be further tested by comparing audit results with recreation facilities/program clients' perceptions. Future research should also evaluate the relevance and weighting of PACES items and the effectiveness of facilities and program supports in encouraging physical activity.
Insulin content and glucose-stimulated insulin secretion Insulin content and glucose-stimulated insulin secretion (GSIS) were measured as described  [39]. RNA isolation, reverse transcription, qRT-PCR and transcriptomic analyses qRT-PCR was performed as described  [32]. ACTB or PPIA transcript levels were used as housekeeping genes for normalisation. Primer sequences are listed in ESM Table  2. Global transcriptomic analyses were performed using the Affymetrix 2. 0ST gene chip as described  [32]  (Affymetrix-Thermofisher, Courtaboeuf, France). Microarray data and all experimental details are available in the Gene Expression Omnibus (GEO) database (accession GSE130208). Heatmap analyses were generated using webbased Morpheus tool (https://software. broadinstitute. org/ morpheus/; access date: 3 January 2019).
Human IAPP promoter analysis The 797 bp upstream sequence of the IAPP gene, which encodes islet amyloid polypeptide (IAPP), was extracted from NCBI Map viewer/Ace view, and scanned for the presence of SOX9 binding motifs using MatInspector (Genomatix software, https://www. genomatix. de/, access date: 3 January 2019;  [40] ). Results are presented in ESM Table  3. Measurement of NEFA levels by GC-MS Cellular saturated and unsaturated NEFA levels were determined by GC-MS as described  [41]. Briefly, cells were mixed with BF3 (14%)/ methanol and heated (100°C; 40 min). Then, NEFA were extracted using heptane/distilled water (1∶2). NEFA present in the supernatant were evaporated and solubilised in heptane. NEFA methyl esters (1μl) were analysed on GC-MS instrument (Shimadzu interfaced with a GC2010 mass selective detector).
Results EndoC-βH1 cells are resistant to palmitate toxicity We first analysed the effect of palmitate on EndoC-βH1 cell viability. We did not observe lipotoxicity associated with morphological changes or obvious cell death (characterised by floating cells or debris) in EndoC-βH1 cells treated with 0. 4 mmol/l palmitate (C16:0). The concept of glucolipotoxicity, i. e. the deleterious effects of combined elevated glucose and NEFA concentrations, prompted us to study EndoC-βH1 cell viability following both high glucose and NEFA exposure. The efficiency of HG (30 mmol/l) treatment was validated by TXNIP mRNA upregulation (  [39]  and data not shown). Remarkably, we did not observe cell toxicity after palmitate incubation at low glucose (5. 6 mmol/l) or HG (Fig. 1a ). To strengthen our investigation, we measured caspase 3/7 cleavage as another marker of cells undergoing apoptosis.
Accordingly, we did not observe changes in caspase 3/7 cleavage activity upon palmitate exposure (Fig. 1b ). We then quantified PARP cleavage, another apoptosis-related measurement. Thapsigargin induced cell apoptosis as determined by increased PARP cleavage, but this was not the case with palmitate (Fig. 1c, d ). Finally, to survey the effects of palmitate over a prolonged period of time (up to 72 h) in real time, we used the xCELLigence system. Palmitate treatment did not decrease cell proliferation/survival, but, in fact, it increased it in a time-dependent manner (Fig. 1e ). These data indicate that long chain saturated NEFA such as palmitate, with or without HG, do not induce glucolipotoxicity in EndoC-βH1 cells.
(a) EndoC-βH1 cells were transfected with siSCD for 72 h, then treated with BSA or HG+PAL (30 mmol/l glucose, 400 μmol/l palmitate), and further co-treated with BSA, 400 μmol/l oleate or 400 μmol/l palmitoleate for 24 h. Apoptosis was measured by caspase 3/7 cleavage activity. (b-c) EndoC-βH1 cells were transfected with either siCTRL or siSCD for 72 h, then treated with BSA, HG+PAL, or HG+PAL with either oleate or palmitoleate (both 400 μmol/l) for 24 h; mRNA levels of IL8 (b) and ATF3 (c) were measured by qRT-PCR. The x-axis conditions below (c) also apply to (b). (d-j) EndoC-βH1 cells were transfected with either siCTRL or siSCD for 72 h, then treated with BSA, 400 μmol/l oleate or 400 μmol/l palmitoleate for 24 h. qRT-PCR data show mRNA expression of the beta cell markers INS (d), MAFA (e) and SLC30A8 (f), the inflammatory genes IL8 (g) and TNF (h), and the ER stress genes XBP1 (spliced variant) (i) and ATF3 (j).
Funding The work leading to this publication has received support from an  Aviesan-AstraZeneca  programme (SOG and RS), from the  Agence Nationale de la Recherche (ANR Betadiamark  to CM and RS), from the  Innovative Medicines Initiative 2 Joint Undertaking INNODIA  under grant agreement No. 115797 , supported by the Union's  Horizon 2020 research and innovation programme ,  EFPIA ,  JDRF  and the  Leona M. and Harry B. Helmsley Charitable Trust  (PM, RS), from the  Innovative Medicines Initiative 2 Joint Undertaking RHAPSODY (Risk Assessment and ProgreSsiOn of Diabetes) , under grant agreement No. 115881 , supported by the  European Union 's  Horizon 2020 research and innovation programme, EFPIA  and the  Swiss State Secretariat for Education‚ Research and Innovation (SERI)  under contract number  16.
This approach has previously been used for modeling in metastatic cancer  [15] [16] [17] [18] [19]. Life-Years (LYs), Quality-Adjusted Life Years (QALYs) and costs of care were estimated at any model cycle and projected over a 10-year time horizon, long enough to capture most future costs and outcomes of the interventions. This time horizon was consistent with maximum life expectancy for most patients initiating chemotherapy according to survival data available in the literature  [2, 3]  and previous economic studies published by the National Institute for Health and Care Excellence (NICE)  [20] [21] [22] [23]. Costs and outcomes were discounted at 4% per year. The incremental cost-effectiveness ratio (ICER) for pembrolizumab versus SoC platinum-based chemotherapy was calculated and expressed as cost per QALY gained and cost per LY gained.