dataesr/scientific-paragraphs-categorization huggingface dataset:
Mixing State of Particles at Different Sampling Sites Entropy metrics were used to quantify mixing state for each sample analyzed here. Figure  9  shows the mixing state index (χ) corresponding to particles in each sample. In this case, the variation in mixing state index is small, with all samples having a χ bounded between 0. 8 and 0. 9. This is a result of D α and D γ consistently being around 3. 4 and 3. 9, respectively. In the previous study by O'Brien et al. [25] , similar sets of STXM and SEM/EDX data were collected for the CARES field campaign. In that study, the same diversity and mixing state parameterization was used except that STXM data (elements C, N, and O) and SEM/EDX (elements Na, Mg, S, P, Cl, K, Fe, Zn, Al, Si, Mn, and Ca) data were analyzed independently. They found that mixing state index values for heavier elements usually ranged from 0.
4-0. 6 with some values as high as 0. 9. The values of χ for C, N, and O generally ranged from 0. 75 to 0. 9. The mixing state indices retrieved exclusively from STXM data closely matches the values determined in this paper. This suggests that χ is almost entirely determined by C, N, and O due to these three elements dominating the mass of the individual particles and the sample as a whole. A point of note is the small spread of D γ values within a given sampling site. With D γ representing bulk diversity, these values serve to compare the average elemental composition (for the 14 elements chosen) of all aerosols, condensed down into one number. For a given site, samples analyzed were taken during the same season of the same year with similar wind trajectories, sampling times and sampling duration.
It is expected then, that there will be a consistency in how much of any given element is present in the aerosol population, based on how much of each element is emitted and included in the particulate material. For D γ to vary wildly from one day to another, or from one sampling period to another, would require an event or aerosol source producing substantially more of one element than usual. In the previous study by O'Brien et al. [25] , similar sets of STXM and SEM/EDX data were collected for the CARES field campaign. In that study, the same diversity and mixing state parameterization was used except that STXM data (elements C, N, and O) and SEM/EDX (elements Na, Mg, S, P, Cl, K, Fe, Zn, Al, Si, Mn, and Ca) data were analyzed independently. They found that mixing state index values for heavier elements usually ranged from 0.
4-0. 6 with some values as high as 0. 9. The values of χ for C, N, and O generally ranged from 0. 75 to 0. 9. The mixing state indices retrieved exclusively from STXM data closely matches the values determined in this paper. This suggests that χ is almost entirely determined by C, N, and O due to these three elements dominating the mass of the individual particles and the sample as a whole. A point of note is the small spread of Dγ values within a given sampling site. With Dγ representing bulk diversity, these values serve to compare the average elemental composition (for the 14 elements chosen) of all aerosols, condensed down into one number. For a given site, samples analyzed were taken during the same season of the same year with similar wind trajectories, sampling times and sampling duration.
Focusing on samples collected where both stage 7 and 8 data were analyzed, average D α values appear to be larger for stage 7 particles. Samples collected at site T3 were expected to have a lower mixing state than either the ATTO or the ZF2 site. This hypothesis was borne from the quantity of fresh emissions in Manaus, specifically soot production from combustion, which would serve to drive the mixing state downwards towards total external mixing. However, the end result of the error calculations in Section 2. 7 is that the values of χ for each point in Figure  8a  become statistically unresolvable, as can be seen from the large error bars. This is also not an issue that would be solved with any reasonable amount of extra data collection but is instead mainly the consequence of the intrinsic spread in D α.
Conclusions Presented here is a quantitative combination between two complementary per-particle spectromicroscopy techniques, STXM/NEXAFS and SEM/EDX, on the exact same data set. STXM/NEXAFS data was collected at the C, N, and O K-edges on a sub-particle level. This allowed not only the quantitative determination of C, N, and O absolute masses, but also carbon speciation and morphology. SEM/EDX allowed the approximate composition of the inorganic fraction to be determined and then quantified along with the STXM data. The combination of these two techniques enables almost all atmospherically relevant elements to be quantitatively probed on a per-particle basis. The potential issue with S detection discussed above could be mitigated entirely in future measurements by conducting STXM measurements at the S L-edge to obtain S mass fractions.
Signals were developed using NovaRED Peroxidase Substrate Kit (Vector Laboratories). Coverslips were affixed to slides using Permount Mounting Medium (Fisher Scientific). Immunofluorescence was performed using antibodies against DDX4 (Cell Signaling; 8761; 1:200), smooth muscle actin alpha (ACTA2) (Abcam; ab76549; 1:2000), and SRY (sex determining region Y)-box 9 (SOX9) (Millipore; AB5535; 1:1000)  [16]. Secondary antibodies were conjugated with Alexa Fluor 594 or 488. Invitrogen Pro-Long Gold Slowfade media containing 4′,6-diamidino-2phenylindole (DAPI) was used to mount the slides. Negative controls where primary antibodies were replaced by species and isotype-matched immunoglobulin Gs (IgGs) were included. Results were examined and recorded using an Olympus fluorescence microscope (IX47) with appropriate filter sets, equipped with a cooled CCD camera (XM10) and cellSens imaging software.
TGFBR1-CA G9Cre mice did not show increased apoptosis in the oocyte It has been suggested that loss of oocytes during follicular development may alter the differentiation and cell fate of ovarian granulosa cells  [33]. To determine whether TGFBR1 activation promotes oocyte apoptosis, we examined fragmentation/damage on DNA suggestive of apoptosis using an In situ Apoptosis Detection Kit and ovarian samples collected at PD3-PD21. To validate the experimental procedure, we showed that ovarian samples of positive controls treated with DNase I to generate fragmented DNA contained strong immunoreactive signals (Fig. 6a ), while negative controls showed only background staining (Fig. 6b ). The analysis did not show alterations of oocyte apoptosis in TGFBR1-CA G9Cre ovaries at PD3-PD12 (Fig. 6c  and d  and data for PD5, PD7, and PD12 not shown).
TGFBR1 CA was detected using an anti-HA antibody. n = 3. Each lane represents an independent sample. f-i X-gal staining using ovaries from Rosa26/Gdf9-iCre mice (f-h) and Rosa26 control mice (i). Panels g and h are higher magnification images of two different fields of panel (f). At least 3 independent samples per group were used. j Real-time PCR analysis of the expression of Gli1, Gli2, and Tgfbr3 using ovaries from 8-week-old control and TGFBR1-CA G9Cre mice. Data are mean ± s. e. m. n = 4-5. * P < 0. 05 and *** P < 0. 001. k-n RNAscope in situ hybridization analysis of Gli1 mRNA distribution using 8-week-old control (k) and TGFBR1-CA G9Cre ovaries (l). n = 4. Positive and negative controls using TGFBR1 CA flox/+ ovaries were shown in (m) and (n), respectively. Sections were counterstained with hematoxylin.
Eligibility criteria The study will include systematic reviews that summarize information on determinants of effectiveness (review 1) and cost-effectiveness (review 2) of BCS. Systematic reviews with any type of comparison group will be included if they: (a) Assess the following screening approaches: film or digital mammography, ultrasonography, CBE, or BSE (b)Focus on a population of women older than 18 years without symptoms of breast cancer and with an average risk in this disease (c)Use a systematic approach for literature search using the preliminary specified inclusion/exclusion criteria (d)Conduct a literature search on more than one country (e) Provide an individual assessment and systematic presentation of the characteristics and outcomes for the included studies (forest-plot, table, or any other structured data presentation) (f ) Present the targeted primary or secondary outcomes (g)Have a full-text publication Since resource use and cost of breast cancer defines cost-effectiveness of screening, reviews will be also included if they (1) focus on a female population older than 18 years old, diagnosed with breast cancer at any stage and with average risk of disease progression; (2) include common treatment approaches (containing surgeries, radiation, and pharmacological interventions) used in actual clinical practice to treat treatment-naïve, refractory, and relapsed patients with breast cancer, and (3) assess the relevant primary or secondary outcomes.
Search strategy A comprehensive systematic search of "reviews" will be conducted in Scopus, Embase, and Medline (via PubMed) databases from inception to February 2017 with no language restrictions. The search strategy for Medline via PubMed is described in an additional document file (see Additional file 2). The keywords "breast cancer AND screening" are searched in the titles and abstracts of reviews of the Cochrane database. In addition, the search will be augmented by identifying further studies from reference lists of identified relevant studies or reviews, by hand searching in the specialized journals, and by contacting the prominent experts in the field. A search for grey literature will be conducted on the websites of the American Society of Clinical Oncology (ASCO)  [15] , the American Cancer Society  [16] , the International Agency for Research on Cancer [17], and the ESMO  [18].
Data management Systematic reviews obtained from the search are exported to EndNote X8 TM (Clarivate Analytics, Philadelphia, USA) where duplicates will be identified and removed. Two trained research assistants will screen all titles and abstracts of publications independently, with at least 10% of all abstracts also screened by the principal researcher (OM). The full texts of publications will be then screened for eligibility independently by the two investigators. All disagreements will be solved by consensus between the reviewers. Native speakers assist with data extraction if an article is written in a language other than English. A data extraction sheet developed in Microsoft Excel® 2010 will be tested on two randomly selected studies. All data will be extracted by one researcher and confirmed by the second one.
The authorship, methods, and results sections of all the included reviews will be explored in details to identify the following: Duplicates: reviews published in different journals and with possible differences in methods description, additional/sub-group analyses, and/or data, but presenting the same results for the main outcomes. We will include the latest review if a duplicate is identified. An update of another study: review-publications updating previous reviews or fully including the results of previously published reviews. All updates of the studies are included into data extraction, while only the latest updated review will be combined into the summary of the latest evidence. Related publications: studies related to each other by time and authorship, but presenting fully or partially different information.
Data analysis We will use Cohen's kappa coefficient to define the strength of agreement between the two evaluators. The analyses reported in the included systematic reviews will be extracted and reported in a systematic (table) format together with reviews' descriptive characteristics and evaluations on their quality. A separate summary on outcomes for each of the analysed subgroups will be provided. We will discuss transferability of data from the systematic reviews considering both quality of the reviews and the included studies, transparency in reporting, geographic presentation in the reviews, variability in outcome values reported by the reviews and systematic differences in outcomes between countries. Depending on the results of our systematic review of reviews, primarily in relation to data heterogeneity, we will also conduct meta-meta-analyses.
For this, we will select the reviews included with the relevant outcomes and code their data for three age categories of women (<49; 50-69; >70). We also aim to assess (1) an impact of the quality of the study on the results of metaanalyses and (2) a difference in outcomes between the designs of studies included into a systematic review. We will apply a fixed effects model for those interventions and population groups where heterogeneity is small and a random effects model when heterogeneity leads to higher I 2 index considering possible variation in values related to geographic differences in programme implementation, demographic, economic parameters, and healthcare coverage. To address an overlapping of metaanalyses, we will quantify the uniqueness of each of the studies prior to meta-analyse the effect.
Signal processing Angular velocity was recorded using digital gyroscopes with the sampling frequency f s =200 Hz, calibrated and directly processed by custom-made Matlab script (Matlab 7. 6. 0. , R2008a). The examples of recorded signals for one healthy control (CTRL) and two PD patients are presented in Fig. 2. Fig. 2  The examples of recorded gyro signals for: two PD patients and one CTRL subject. Firstly, tapping performance was described with parameters typically used for tapping description  [19] :  duration of the taps t T -expressed in seconds,  tapping cadence c T -expressing the number of taps in the observed 15 s long sequence,  angle that index finger forms relative to the "zero posture" of the fingers α Texpressed in degrees. Additionally, Continuous Wavelet transform (CWT), Welch's averaged modified periodogram method of spectral estimation and Spectral Arc Length method (SPARC)  [16]  were applied on the observed 15 s long sequences of the signal.
We used beads to verify that this was the case in our instrument within the size range typical of an E. coli cell (~2μm, Additional file 1: Fig. S1 ). We therefore used FSC as a proxy for cellular size (S). As we followed the original layout of the Keio collection  [11] , our strains are not distributed randomly amongst plates. In fact, genes with similar functions were occasionally grouped together in the same plate. For example, many genes encoding chemotactic and flagellar proteins are clustered in plate #45 (Additional file 1: Fig. S2 ). This nonrandom arraying of strains could have introduced bias in our measurements. However, we did not observe significant plate-specific shifts of median fluorescence in relation to the whole dataset distribution (Additional file 1: Figs. S3-S6 ). Re-arraying of 180 strains and additional analysis further confirmed this conclusion (see Additional file 1).
Therefore, to avoid the risk of introducing processing bias in the dataset, no further data normalization was performed. The average fluorescence of mVenus and mCherry varied approximately four-fold and was strongly correlated across the 3. 824 strains (r = 0. 90, Fig. 1a ) and with S (correlation 0. 67 and 0. 61 for mVenus and mCherry, respectively) (Fig. 1a ). A change in cell size could indirectly affect heterologous gene expression in cases in which proteins are not sufficiently split between daughter cells during cell division (growth feedback)  [9]. This scenario was supported by the observed positive correlation between FSC and fluorescence output (Fig. 1a ). To quantify the specific effects of gene knockout on heterologous gene expression we needed to account for the influence of cell size variations (S).
Most KEIO knockouts show a single-feature phenotype To ease the analysis of associations between variables, we binned strains into groups of extreme phenotypic values (top and bottom 5% quantiles giving respectively S high / S low and E high / E low ). These extreme values were homogenously distributed across the dataset (Fig. 1a , cyan dots), showing that the regression procedure did not introduce systematic biases. About 192 genes showed an extreme S or E value, whereas the number of genes with a G spec phenotype was 384. Amongst the set of unique 578 strains thus selected, 81% presented exclusively one extreme E or S phenotype. A S high phenotype was very rarely combined with extreme expression (E high or E low , ~2% each). In contrast, the S low phenotype was significantly associated with E phenotypes (~7% each, p < 10 -4 , Fig.
2  Groups #7-11) and predominantly reduced expression (E low , Fig. 2  -Groups #12-16). To obtain a broader understanding of this landscape, we performed another functional enrichment analysis amongst the strains populating these regions  [16]. Functional terms passing both a p-value (p < 0. 1) and a Z-score (z > 0. 5) threshold were defined as differentially enriched (Fig. 3 ) (Bioconductor package CompGO, Additional file 1). The S high -E high region harbored strains deleted of key bacterial growth functions including cell division and important housekeeping cytosolic cellular processes (GO:0009987 and GO:0044444, Cellular Components) (Fig. 3  yellow circles). A detailed inspection of child GO Biological Processes (BP) revealed enrichment for functions involved in iron-sulfur cluster assembly (GO:0016226, iscA, sufC, cyaY, ygfZ), mRNA degradation (GO:0006402, rnr, pnp), chromosome condensation (GO:0030261, hupAB) and DNA-templated transcriptional regulators (GO:0006335, oxyR, mfd).
Genome-wide profiling of inter-individual variation in H3K27ac in the entorhinal cortex We generated H3K27ac ChIP-seq data using post-mortem entorhinal cortex tissue dissected from 47 elderly individuals (average age = 77. 43, SD = 9. 66, range = 58-97) comprising both AD cases (n = 24, mean Braak stage = 6. 00, SD = 0. 00) and age-matched low pathology controls (n = 23, mean Braak stage = 1. 30, SD = 1. 11) (Supplementary Table  1 ). Raw H3K27ac ChIP-seq data is available for download from the Gene Expression Omnibus (GEO) (accession number GSE102538). After stringent quality control (QC) of the raw data (see Methods), we obtained an average of 30,032,623 (SD = 10,638,091) sequencing reads per sample, with no difference in read-depth between AD cases and controls (P = 0. 93, Supplementary Fig.
1 ). This represents, to our knowledge, the most extensive analysis of H3K27ac in the human entorhinal cortex yet undertaken. Using combined data from all 47 samples (see Methods) we identified 182,065 high confidence H3K27ac peaks; these are distributed across all 24 chromosomes (Supplementary Table  2 ) spanning a mean length of 983bp (SD = 682bp) with a mean distance between neighbouring peaks of 15,536bp (SD = 116,040bp). We validated the identified peaks using two independent ChIP-seq datasets: first, we obtained locations for frontal cortex (BA9) and cerebellum H3K27ac peaks from a recent analysis of autism and control brains  21  ; second, we downloaded peak profiles for multiple cell-and tissue-types from the NIH Epigenomics Roadmap Consortium 22 (see Methods). As expected, there was a very high overlap between H3K27ac peaks called in other neocortical datasets and our ChIP-seq data, with a notably lower overlap with H3K27ac data from non-cortical tissues (Supplementary Fig.
AD-associated differential acetylation in the entorhinal cortex We quantified read counts across every peak in each individual sample using HTSeq  23  and employed a quasi-likelihood F test, implemented in the Bioconductor package EdgeR 24 , to test for differences in H3K27ac between AD cases and low pathology controls (see Methods). Our analysis model controlled for age at death and neuronal cell proportion estimates derived from DNA methylation data generated on the same samples using the CETS R package  25  (Supplementary Table  1 , Supplementary Fig. 3 ). Subsequent sensitivity analyses excluded an effect of sex on our results, but revealed widespread effects of cell-type proportion estimates and age at death highlighting the importance of including these covariates (Supplementary Fig.
Subjects were approached in life for written consent for brain banking, and all tissue donations were collected and stored following legal and ethical guidelines (NHS reference number 08/MRE09/38; the HTA license number for the LBBND brain bank is 12293). All samples were dissected by trained specialists, snap-frozen and stored at -80 °C. A detailed list of demographic and sample data for each individual included in the final analyses is provided in Supplementary Table  1.
Data pre-processing and quality control Global sample anomalies were ruled out using fastqc  56  summary measures. All fastq files were aligned to the Homo sapiens reference genome (hg19, Broad Institute) using Bowtie  57. The output SAM files were converted to binary (BAM) format. All BAM files were sorted and indexed using samtools  58. PCR duplicates were removed using Picard (http://broadinstitute. github. io/picard/). Samtools was used to additionally remove nonuniquely mapped reads as well as reads with a sequencing quality score q < 30. Final read counts after QC for all 47 samples are shown in Supplementary Fig. 1. On average, we obtained 30,032,623 reads per sample (SD= 10,638,091; range=10,910,000-53,770,000) and individual read counts did not associate with disease status (P = 0.
Peak calling and read counts All filtered BAM files were merged into one grouped file and converted to tagAlign format using bedtools  59. Peaks were called on this merged file using MACS2  60  , keeping all duplicates, since duplicates were removed from each sample previously and any remaining duplicates would result from the same read occurring in more than one sample. From the resulting peaks those located in unmapped contigs and mitochondrial DNA were filtered out as well as peaks that did not meet a significance threshold of P < 1. 0E-07 for peak calling. The bed file of peaks was converted to gff format using awk and R, and reads for each individual sample were generated using HTSeq  23. Final filtering was performed using the Bioconductor package EdgeR  24  , excluding peaks with fewer than 2 samples showing at least 1 read per million, resulting in a total of 182,065 peaks to be tested.
Differential peak calling We used the quasi-likelihood F test  63  in EdgeR  24  to analyse peak differences between ADcases and controls, allowing us to correct for potential confounders in the analysis of differential peaks. Our analyses accounted for additional phenotypic variation across the samples, including age at death and neuronal proportion estimates based on DNA methylation profiles from the Illumina 450K HumanMethylation Array from the same samples, which were calculated using the CETS R package  25. To test for the effects of these two covariates and sex we ran three further analyses, omitting age at death and CETS and adding sex as a covariate to our main model. We imputed the median CETS estimate for one individual with missing DNA methylation data. To include age at death and CETS estimates in the EdgeR differential peak calling method, these variables were converted to five-level factors using the R function cut().