dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: TIAregistry. org is an international cohort of patients with transient ischemic attack (TIA) or minor stroke within 7 days before enrollment in the registry. Main analyses of 1-year follow-up data have been reported. 5 We conducted subanalysis on the baseline and 1-year follow-up data of Japanese patients. Methods: The patients were classified into 2 groups based on Japanese ethnicity, Japanese (345) and non-Japanese (3238), and their baseline data and 1-year event rates were compared. We also determined risk factors and predictors of 1-year stroke. Results: Current smoking, regular alcohol drinking, intracranial arterial stenosis, and small vessel occlusion; and hypertension, dyslipidemia, coronary artery disease, and extracranial arterial stenosis were more and less common among Japanese patients, respectively.
TIAregistry. org was an international multicenter-cooperative, investigator-driven, observational study. We proposed the adoption of a new concept termed acute cerebrovascular syndrome (ACVS), which includes TIA in acute setting and AIS;  1, 2  grouping the two conditions together promotes the recognition of TIA in acute setting as a medical emergency equivalent to AIS. We have previously published the main results of 1-year follow-up data analyses of TIAregsitry. org:  5  urgent intervention by stroke specialists in dedicated units by the recognition of TIA as an ACVS contributed to substantially reduce the risk of subsequent stroke and other cardiovascular events during recent 10 years. However, our results also indicated the need to further explore how the residual risk of stroke might be reduced aside from stricter adherence to the current optimal treatment guidelines.
We subsequently performed an Asian subanalysis of TIAregistry. org because we suspected that the relevance of the following risk factors for stroke might differ according to race or ethnicity: stroke subtypes, genetic background, diet, lifestyle, socioeconomic status, and stroke care system. 6 Results of this subanalysis demonstrated that substantial racial or regional differences exist among various clinical and investigational parameters, as well as their impacts on the risk of stroke. We further speculated that these differences might also exist between Japanese and non-Japanese Asian patients. We therefore conducted a Japanese subanalysis of TIAregsitry. org. Specifically, we compared the baseline and 1-year follow-up data of Japanese and non-Japanese patients with TIA or minor stroke within 7 days before enrollment in the registry in the context of contemporary care systems.
Data Collection Stroke specialists prospectively collected patient data with a standardized web-based case report form at the time of evaluation of the qualifying event (baseline), and at 1, 3, and 12 months following baseline; though the original dataset features additional evaluations every 12 months thereafter for 5 years, the present study analyzed data up to the 12-month evaluation. The baseline data included clinical symptoms, medical history, living and socioeconomic data, physical examination, lab tests including standard blood chemistry, brain and artery imaging, cardiac workup, and clinical management (medical treatment and revascularization procedure). An etiologic subtype of ischemic stroke was assigned according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification.
Baseline Data Of the 4789 patients enrolled in the TIAregistry. org, 4583 patients were included in the analysis; 173 did not meet inclusion criteria, while 33 lacked follow-up data. Missing data was due in almost all cases to the emergence of another cause for their TIA-like event. Of the remaining 4583 patients, 345 (7. 5%) patients were Japanese (mean age, 66 § 13 years; 60% men). Ethnically non-Japanese patients identified as European native (n = 3317), Eastern Asian (n = 725), Middle or Proximal Eastern and Maghreb (n = 69), American Hispanic or Latino (n = 63), Black or African American (n = 38), and other (n = 26). Non-Japanese Asian patients were from China (n = 518), Korea (n = 99), Taiwan (n = 66), Thailand (n = 37), and Malaysia (n = 5). Table  1  presents the baseline characteristics of the 2 groups of patients.
Data regarding risk of stroke at 7-, 30-, and 90-days were not available among non-Japanese Asian patients. The age, sex, and mRS adjusted hazard ratio of 1-year stroke among Japanese and non-Japanese patients according to baseline characteristics are presented in Table  4. Significant risk factors for Japanese patients were diabetes and regular alcohol consumption; those in the non-Japanese population included age, sex (men), history of smoking, and a history of stroke or TIA. There were no significant risk factors at baseline for 1-year stroke among non-Japanese Asian patients (Supplemental Table  3 ). Risk of 1-year stroke according to the TOAST classification for both Japanese and non-Japanese patients was significantly higher in patients with large artery atherosclerosis (LAA) than patients with other subtypes (Table  5 ).
In this paper, we reexamined the typical analysis methods of a visuomotor sequence learning task, namely the ASRT task (J. H. Howard & Howard, 1997). We pointed out that the current analysis of data could be improved by paying more attention to pre-existing biases (i. e. by eliminating artifacts by using new filters) and by introducing a new data grouping that is more in line with the task's inherent statistical structure. These suggestions result in more types of learning scores that can be quantified and also in purer measures. Importantly, the filtering method proposed in this paper also results in higher individual variability, possibly indicating that it had been masked previously with the usual methods. The implications of our findings relate to other sequence learning tasks as well, and opens up opportunities to study different types of implicit learning phenomena.
e. higher order learning). We only know that there is a little extra to triplet learning. And albeit being little, this extra is not marginal; this measure differentiates between age groups  [4]. From modified versions of the ASRT task we also know that higher order statistical learning is possible: it has been shown that even third-order statistical regularities can be learned by humans, and also that such learning is reduced in the old compared to the young  [21, 22] , just as the higher order learning measure is reduced in elderly. But, of course, this does not exclude the possibility that pattern learning also occurs in the ASRT. Despite the uncertainty that remains about this measure, it is still surprising that only a handful of studies quantified it at all, as it costs nothing to do so and it opens new opportunities for data interpretation.
Statistical properties and analysis methods of the task We have talked about how pattern learning is confounded by statistical learning, and how unclear it is what constitutes "higher order learning". The story is however even more complex. For example, in the typical analyses of ASRT data no distinction is being made between joint probability learning (how frequent a particular combination is, e. g. 1-2-2) and conditional probability learning (how often does 1-2-. end with 2), and although the terminology points to the former (e. g. the terms "low-frequency triplet" and "high-frequency triplet"), the way we typically analyze data is more in line with the latter (since we analyze reaction times given to the final elements of triplets; i. e. we measure whether a particular response is faster following a specific set of trials contrasted with different sets of trials).
Along with the description of the learning measures of these models, we will present data confirming and illustrating the theoretical considerations reviewed so far. Second, we will compare the models (Model 1 to Model 5) in terms of goodness of fit, which might corroborate our proposals from a different perspective (i. e. how efficient a Model is in capturing the different aspects of learning) on the ASRT task. Although we will present and compare findings got with different filtering methods (No Filter, Triplet Filter, and Quad Filter), the main focus of this section will be on differences between Models (Model1-5) within Filtering Methods, and not vice versa. The reason for this is that the superiority of one filtering method over another cannot easily be justified via statistics (i. e.
This way 32 of the participants got the sequence 1-r-2-r-3-r-4-r, 29 participants got 1-r-2-r-4-r-3-r, 31 participants got 1-r-3-r-2-r-4-r, 33 participants got 1-r-3-r-4-r-2-r, 29 participants got 1-r-4-r-2-r-3-r and 26 participants got 1-r-4-r-3-r-2-r. EPRIME 2. 0 was used as a stimulus presentation software  [47].
e. Model 5, LR, H1R, H1P and H2P) for each participant to minimize the effect of outliers. This way 4. 5% of the remaining data was removed when using no filter; 16% when using the triplet filter and 64. 6% when using the quad filter (the high percentage of excluded trials using the triplet filter and quad filter results from the filters themselves, not from so many Z-scores having a high absolute value). At the end, an average of 2710 trials were analysed per participant when using no filter; an average of 2384 trials when using triplet filter and an average of 1006 trials when using the quad filter. We computed individual adjusted R 2 -s for each epoch of each participant as a way of assessing the goodness of fit of each Model; since there were nine epochs, this resulted in nine values per participant.
001), the Quad Errors. Warm-up trials were not analysed, neither were trials 6-8 in a block (since it is only from trial 9 that the first full ASRT-quad is reached). This way 9. 4% of trials were excluded. Additionaly, only those data points were included that were preceeded by at least three correct responses in a row (this way it could be ensured that the sequence of buttonpresses corresponded to the intended combinations before a critical trial); this resulted in the removal of additional 13. 9% of the remaining trials. When no filtering was applyied, an average of 2981 trials were analysed per participant; triplet filtering resulted in an average of 2610 trials, while quad filtering in an average of 1113 trials per parcitipant. The goodness of fit of the different models were than calculated in the form of Cramer's V values (data from the nine epochs were collapsed into a single category due to the small number of errors) separately for each filtering method.
Moreover, in the case of triplet learning measures, a typical result is that decreased means are associated with increased standard deviations (e. g. Model 2 L-H, Model 3 LR-HR, Model 4 L-H1, Model 5 LR-H1R). This pattern of results was not only consistent across reaction times and error data (see Table F in S1 File), but also across the six ASRT sequences when assessed separately (SD and CV in Tables H and I in S1 File and Tables K and L in S1 File; while Table G and Table J in S1 File shows the means of individual effect sized broken down by the six possible ASRT sequences. Note that CV values could be inflated in cases when means approach zero). Why did these differences arise? In an optimistic scenario, they are the result of quad filtering making it possible for us to detect previously undetectable (masked) individual differences in learning capabilities.
Does higher variability go in hand with lower reliability? Reliability assesses whether the outcome of a test would be similar when repeated. Unsystematic noise decreases reliability, while systematic patterns in variation increase it. Thus, the higher the systematic variation in our data (relative to unsystematic noise), the higher the reliability indices will be. The problem here is that pre-existing biases-that we aim to reduce with more strict filtering methods-may actually introduce systematic variability rather than unsystematic noise. That being said, it is entirely possible that by reducing the variability that is attributable to pre-existing biases, reliability indices drop; and this is exactly what we have found. We calculated split-half reliability of all of the measures by randomly assigning each keypress to one of two categories; the individual effect sizes were then computed for both sets, and the correlation of the two values was computed.
Thus, in this case, a single Cramer's V value was calculated for We assigned each trial one of two possible codes, and the resulting two sets were analysed separately (thus learning, fatigue, etc. affected each set similarly). In the case of reaction times, learning scores were computed for each Session (epochs 1-3, epochs 4-6 and epochs 7-9), and then averaged. In the case of accuracy, a single Cramer's V was calculated for Epochs 1-9 for both sets (data from the nine epochs collapsed due to low overall error rates). The correlation between the two subsets is shown in the table (Pearson correlation coefficients). https://doi. org/10. 1371/journal. pone. 0221966. t003 each participant (data of Epoch 1-9 collapsed into a single category), and these values were compared to zero using a one-sample t-test.
To assess the second question, we identified participants with individual Cohen's d values and Cramer's V values exceeding the limit of 0. 2 and 0. 05, respectively. We also quantified the percentage of participants whose effect sizes exceeded these limits but the difference was in the unexpected direction (presumably false negatives). If the ratio of false positives is similar to the ratio of false negatives, then the percentage of true positives could be gauged by subtracting the (false) negatives from the positives. In the case of reaction times, we also calculated the percentage of reliable learners by identifying participants who had shown at least a small learning (Cohen's d > 0. 2) in at least 5 (out of 9) epochs. Such a detailed analysis was not possible in the case of accuracy data due to the low overall error rates.
Discussion The ASRT  [4]  task is a visuomotor sequence learning task designed to measure implicit learning and memory. In this paper, we discussed in detail the many possible information types that could be learned (such as pattern learning and different levels of joint frequency learning and conditional probability learning), and our concerns that these types of learning are not sufficiently differentiated by the currently used analysis methods. Moreover, as we have shown, the learning measures that are typically extracted from data might be biased by preexisting tendencies to certain stimulus combinations, indicating that the ASRT does not measure (only) what it supposed to. We provided a presentation of how different analysis methods and filtering methods result in different levels of artifacts and biases, a hopefully practical aid for the (re)interpretation of the results obtained with the task.
On the other hand, such comparisons could only be made based on a smaller number of trials being analyzed, which could, in turn, result in increased noise. The ASRT data analyzed in this paper consists of 45 blocks of 80 trials for each participant. Both shorter, e. g. [10] , and longer, e. g. [4, 16] , ASRT-s have been used in the past, but it is reasonable to assume that higher order learning, i. e. quad learning and/or pattern learning occurs over longer periods of time; Methodsthe results of  [4, 46]. The analysis method proposed in this paper should be applied to data gained from extended practice to verify this hypothesis. This could reveal individual differences even in the dynamics of joint frequency vs. conditional probability learning, which would be an important step in understanding the nature of implicit statistical learning.
Coating Thickness The mean thickness of the coating was calculated from the difference in mass between coated and uncoated fruit, the density of the coating material, and the surface area of the fruit: V = m ρ (1) A = 0. 809 × FM + 19. 405 (2) E = V/A (3) where, V is the volume of the film-forming solution (cm 3 ), m is the mass of the film-forming solution (g), ρ is the density of the film-forming solution (g cm -3 ), e is the mean thickness of the coating layer on each fruit (cm), FM is the mass of the fruit (g), A is the surface area (cm 2 ) of the fruit. Equation (2) was established from preliminary experiments. Thirty-six plums were scanned using a camera supported by a 3D ScanBook scan system along with ScanWare Enterprise 3. 8 software (Scanbull, Hameln, Germany). The measured surface area was set into relation to fruit mass.
Contact Angle Determination Contact angle (θ) is the angle described as a relationship between the surface tension and one of three phases: Liquid phase L, solid phase S, or vapor phase V according to the Young equation  [47]. The contact angle was measured by the sessile drop method: A droplet (about 1. 5 µL) of a test liquid was placed on a horizontal film surface (in stand-alone films). Measurements were done using a DGD-DX goniometer equipped with the DIGIDROP image analysis software (GBX, Romans-sur-Isere, France), according to the methodology of  [48]. The contact angle was measured on both sides of the drop and averaged. The contact angle and drop volume measurements were carried out over 120 s. The effect of evaporation was assessed on an aluminum foil considered as an impermeable reference surface and subtracted from the sample.
04 × 10 -4 m 2 ); e is the film thickness (m); and, ∆p is the water vapor pressure differential between the two sides of the film (Pa). Measurements were performed at least three times for each differential tested. The oxygen permeability was measured using the manometric method according the [57] standard using a GDP-C (Brugger, München Germany). The test chambers of the permeation cell were first degassed under vacuum, then the upper side was swept by a humidified oxygen flow at a rate of about 1. 33 mL s -1 at atmospheric pressure. The increase in pressure in the downside chamber during the test period was assessed and displayed by an external computer. Data was recorded and permeance was calculated by GDP-C software (with temperature compensation). The sample temperature (25 • C) was adjusted using an external thermostat Haake F3 with water bath K (Thermo Fisher Scientific, Karlsruhe, Germany).
1a ). All four CRISPR repeat sequences are exclusive to the Sulfolobales, including the genera Sulfolobus, Saccharolobus, Acidianus and Metallosphaera (Supplementary Table  1 ). The temporal dynamics of the CRISPRome was analyzed in two parallel series of enrichment cultures established from environmental samples J14 and J15 (ref. 48 ), in media that favor the growth of Sulfolobus/ Saccharolobus and Acidianus species prevalent in terrestrial hot springs and that grow well under laboratory conditions (Supplementary Fig. 1 ). More than 25 million spacers were sequenced from all the samples (Supplementary Table  2 ), which after clustering of sequences with 85% identity resulted in 40,704 unique spacer clusters (Supplementary Data 1). The clustered spacer collection obtained here from a single sampling site dwarfs (sixfold increase) the size of the Sulfolobales spacer database from strains (n = 6699 unique spacers) that have been previously isolated from geographically diverse locations (Fig.
To assess the provenance of the spacers, we matched the Beppu spacer set against the available Sulfolobales genomes, viruses and plasmids (Fig. 1c ). Using the threshold of >85% identity over the full length of the alignment, protospacers were identified for ~6% of spacers, a value that is close to the ~7% mean observed in previous analyses of the global dataset of spacers from all available sequenced genomes  52. Unexpectedly, protospacers associated with the CRISPR-C array were overrepresented in plasmids (chi square test, P-value < 10 -5 ) and underrepresented in viruses (chi square test, P-value < 10 -36 ), suggesting specialization among the distinct CRISPR consensuses to combat different types of MGE. The CRISPRome of the Sulfolobales community from Beppu included spacers against 53 viral genomes isolated from all over the world, but the most frequently targeted ones were those sequenced from the same sampling site  48  , further indicating local adaptation of the Sulfolobales viruses.
1 ). Among these, SPV1 and SPV2, icosahedral, non-lytic viruses of the family Portogloboviridae  54, 55  , are by far the most abundant in the respective samples, accounting for ~90% of all virome reads  48. To understand the reasons underlying the dominance of SPV1 and SPV2 and their exclusivity to the corresponding samples, we focused on the comparison of spacers targeting the two viruses in J14 and J15, respectively. Notably, the genomes of SPV1 and SPV2 are 92% identical to each other  48  and mapping of the CRISPR spacers from our dataset showed that genomic location of sequence divergence between SPV1 and SPV2 specifically coincides with targeting by CRISPR spacers (chi square test, Pvalue < 0. 01; Fig. 3a ). Thus, CRISPR targeting is an important factor driving the genome evolution of portogloboviruses.
Characterization of virus-borne mini-CRISPR arrays. Six spacers associated with the CRISPR-A repeat and matching SPV1 and SPV2 were the most abundant in the corresponding enrichment cultures (Supplementary Data 2). Unexpectedly, 3 of these spacers matching SPV1 (100% identity) were sequenced from the J15 enrichment culture dominated by SPV1, and conversely, the 3 spacers matching SPV2 (100% identity) were sequenced from the J14 enrichment dominated by SPV2. Furthermore, abundant CRISPR spacers in our data could be assembled into long (>3 spacers) CRISPR arrays (see above and Supplementary Text). However, despite being among the most abundant in our dataset, the 6 SPV1-and SPV2-matching spacers could not be reconstructed into long arrays, but instead appeared to be located within mini-CRISPR arrays each carrying 1 or 2 spacers.
Remarkably, two of the three spacers carried by SPV2 target SPV1, whereas only one of the three spacers carried by SPV1 targets SPV2 (Fig. 3b ), with another one targeting a pRN1-like plasmid integrated in the S. tokodaii genome (BA000023, nucleotide coordinates 328508-335407). Importantly, the loci orthologous to the regions targeted by spacers in the viruses carrying the spacers contain either point mutations or deletions, preventing self-targeting (Supplementary Data 2). Notably, the SPV1 and SPV2 spacers target regions close to the mini-CRISPR arrays, although origins and consequences of this proximity remain unclear (Fig. 3b ). These findings prompted us to search for additional mini-CRISPR arrays in our CRISPRome dataset, resulting in 15 more candidates (Fig. 5a ). Three of the mini-CRISPR arrays were confirmed to be encoded within viral genomes by analysis of the previously sequenced viromes from J14 and J15 samples  48.
For CRISPR-A and -D repeats, we obtained 1. 5 and 2% of intersection (Supplementary Fig. 6 ), i. e. , 1. 5% of spacers bordered by CRISPR-A repeats (and amplified with A-specific primers) were also found in arrays bordered with CRISPR-D repeats. Importantly, this fraction was comparable to those for other repeat types. For instance, for CRISPR-A and -C, the intersection was 2. 8% and 2. 6%, suggesting that there is no preferential cross-amplification of spacers associated with CRISPR-A and CRISPR-D repeats. The association of homologous spacers with different CRISPR repeat consensuses can be explained by independent acquisition of the corresponding spacers from the same loci of the plasmid/viral genome by two distinct adaptation modules. This possibility was confirmed by analysis of spacer dataset from fully sequenced Sulfolobus islandicus genomes.