dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Robustness is a fundamental problem in statistics, which aims at using statistical procedures that remain stable in presence of outliers. Historically, outliers were mistakes in data collection, or observations of individuals belonging to a different population than the population of interest. Robustness became even more important in the modern context where automatically collected datasets are often heterogeneous. Moreover, some strategic datasets are susceptible of malevolent manipulations. In the statistical literature, the developmental of robust estimation methods for regression models generally focusses on the construction of Z-estimators (Van der Vaart, 2000, Chapter 5) for which each individual observation can only have a bounded impact on the estimating equations, and which therefore have a bounded influence function  (Hampel, 1974).
The number of terms in the criterion minimized in (2) is O(n 2 ), which limits the applicability of θn in large datasets (see however Section 2. 4 for a possible approach to compute θn for moderate values of n, i. e. for n equals to a few thousands). For large scale problems we propose the alternative estimator θn , defined by θn ∈ argmin θ∈Θ n i=1 l(θ, X i , Y i ) (3) with l(θ, x, y) = E Y,Y ′ iid ∼P g(θ,x) k Y (Y, Y ′ ) -2k Y (Y, y) for all θ ∈ Θ, x ∈ X and y ∈ Y. The criterion in (3) involves only n terms but, on the other hand, since θn cannot be interpreted as the minimizer of a measure of discrepancy between P n θ and P n , our general theory for θn does not apply to this estimator. Theoretical results for θn are provided in the next section, but they are weaker than those obtained for θn.
Study Design Data for this study were derived from a cohort of previously recruited participants (n = 200) from Kingston NHS Foundation Trust in the cross-sectional phase of the preceding VMATT2 study, which sought to demonstrate the psychometric properties of SPUR-27 in patients living with type 2 diabetes. Subsequently, observational cohort data for recruited participants were collected over a 12-month period that comprised hospital admission data both 6 months before and after participation in the VMATT2 study. Hence, this study followed an observational design in line with the STROBE  36   The recruitment of the first and final participants for the VMATT2 study took place in January 2020 and October 2021, respectively, hence the period of observational data collected ranged between July 2019 and March 2022.
Variables of Interest The outcome variables of interest included the number of admissions 6 months before and after completing the SPUR-27 tool, as well as number of early readmissions (admissions occurring within 30 days of discharge) within the observational period. The count outcomes were further dichotomized to indicate whether the patient was admitted or re-admitted early, which is useful clinically when considering those patients who may benefit most from any targeted intervention. Clinical data, which included admission data during the observational period, were derived from the EHR. The main variable of interest assessed as part of the model was medication adherence (derived from the SPUR-27 tool). The Medication Adherence Rating Scale (MARS),  38  Beliefs about Medicines Questionnaire (General/Specific) (BMQ-G/BMQ-S)  39  and Diabetes Treatment Satisfaction Questionnaire (DTSQ)  40  were validated PROMs previously implemented in the VMATT2 study as comparators for the new SPUR-27 tool.
Statistical Analysis All descriptively reported data were analysed using SPSS version 26. 0 for Windows. For modelling count outcomes, a Poisson or negative binomial model was employed. Akaike's information criterion (AIC) was used to select the appropriate type of model. Potential non-linear effects were modelled as fractional polynomial or restricted cubic spline terms. A link test was used to further check the final model specifications. For the binary version of the outcomes, the same set of covariates were used in logistic regression models. Given the relatively small sample size used in this study,  41  Firth's penalized maximum likelihood estimator was employed for its "remarkably stable performance". 42 seudo R 2 was used for measuring model goodness-of-fit (McFadden's for count outcome and Tjur's for binary outcome models).
Results Sociodemographic data were available for the entire cohort previously recruited during the VMATT2 cross-sectional study (n = 200). All participants provided their consent to participate in the follow-up observational study (100% response rate). Participant age, education, and income were collected as ordinal data and are reported as such. The modal age was 70-79 years (n = 74, 37. 0%), education was reported predominantly at GCSE level or equivalent (n = 85, 42. 5%) and almost three-quarters of participants indicated that they were retired (n = 146, 73. 0%). Participants identifying as female represented 36. 0% (n=72/200) of the cohort. Most participants were White (n = 152, 76. 0%). BMI data were available for all participants (mean ± SD, 28. 4 ± 5. 5) and indicated that a large proportion of participants were above their recommended weight.
Almost two-thirds (n=124/200, 62. 0%) of participants did not meet a HbA 1c target of ≤7. 0% (53mmol/mol). The mean ± SD number of antiglycaemic agents and comorbidities were 1. 9 ± 0. 9 and 6. 6 ± 2. 7, respectively. The modal IMD rank by decile was 9 indicating (mean ± SD, 7. 3 ± 2. 2) that the cohort was broadly affluent based on the level of deprivation attributed to their postcode during the observational period (Table  1 ). Retrospective admission data were available for all participants (n = 200, 100%); however, the complete prospective data set (6-month period following completion of SPUR-27) were limited to a sample of 190 participants as a result of mortality prior to discharge or missing data from the EHR (n = 10) (Table  2 ). In total, 425 admissions were recorded during the observational period.
Of those admissions, 254 (59. 7%, n=254/425) were recorded as early readmissions. These early readmissions were reported in 98 participants, providing an early readmission rate of 48% (n=98/200) for this sample. Over half (51. 5%, n=103/200) of the participants had ≥1 recorded admission in the 6 months prior to completion of the SPUR-27 questionnaire. The figure was similar for admissions in the 6-month follow-up period (56. 8%, n=108/190). In total, 71% (n=142/200) of the cohort experienced a minimum of one admission within the observational period with ≥1 early readmission being recorded in 69% of cases (n=98/142). Data for average PROM scores were converted to percentages to assess against the crude cut-off score of 80% that is commonly assigned to the calculation of MPRs to determine whether patients are adherent to their medicines (Table  2 ).
4 N/m. The images obtained were analysed with Gwyddion software (version 2. 62). The XPS analyses were carried out at room temperature on Axis Nova spectrometer (Kratos Analytical-Manchester, UK) using the Al Kα line (1486. 6 eV) as an excitation source. Data analyses were performed using the software CasaXPS (version 2. 1. 0. 1). For calibration, the binding energy for the C 1s peak was set at 284. 8 eV, with a constant pass energy of 20 eV and resolution of 0. 22 eV. Raman spectra of the films were obtained by a Renishaw InVia reflex spectrometer at room temperature and in air atmosphere (λ exc = 514 nm, laser power = 10 mW and exposure time 20 s).
Sampling and analytical procedures 2. 4. 1. Gross wound area measurement The wound surface area was tracked over time on fish housed individually (18 fish/group) at 2, 4, 8 12, 16, 20, and 24 dpw as follow. To do so, fish were netted-out, anesthetized to complete loss of reactivity as previously described, placed onto a wetted petri-dish for imaging according to  Edirisinghe et al. (2019)  and returned to their original tank. The wound site was imaged using a digital camera (Leica® KL300 LED) connected to a stereomicroscope (Leica® S8 APO, Germany) for computer-assisted image analysis (ImageJ v1. 6, National Institutes of Health, Bethesda, Maryland, USA). In brief, the apparent wound edge was manually traced using the freehand-draw tool in order to surround the wound area which was then automatically measured by the software based on a calibration image taken under the same set-up.
Gene Accession number Primer name Aneling temperature • C Primer sequence (5'-3') Tumor necrosis factor-α (tnf-α) AF025305 tnf-α-F 58 AGAAGGAGAGTTGCCTTTACCGCT tnf-α-R AACACCCTCCATACACCCGACTTT Interleukin-1β (il-1β) AY340959. 1 il-1β-F 58 TCAAACCCCAATCCACAGAG il-1β-R TCACTTCACGCTCTTGGATG Interleukin-10 (il-10) AY887900. 1 il-10-F 58 CCCTATGGATGTCACGTCATG il-10-R CATATCCCGCTTGAGTTCCTG Transforming growth factor β1 (tgfb1) XM_687246 tgfβ-1-F 58 CCCAAGGAACCAGAAGTAGAAG tgfβ-1-R GGATCTTCTATGGTGTGCTGAA Matrix metalloproteinase 9 (mmp9) AY151254 mmp-9-F 58 TTTGCCCTGATCGTGGATAC mmp-9-R GGGAAACCCTCCACGTATTT Matrix metalloproteinase 13 (mmp13) AF506756 mmp-13-F 58 GAGAAGGTTTGGGCTCTCTATG mmp-13-R TGAGTTGCTGTCTTCCTTGTAG Tissue inhibitor of metalloproteinase 2 (timp2b) BC056534 timp-2b-F 58 GCGTTATGTGGAGTCAGTCTT timp-2b-R CTCGATGTAGTCACACAGGTTTAT Heat shock protein 90α class B (hsp90ab1) NM_131310.
Histopathological diagnostic Wound healing status at 1 and 4 dpw was characterized based on five main parameters: Presence of inflammatory cell clusters; necrosis; granulation tissue; re-epithelialization; and thickness of the newly formed epidermis. For each specimen, each parameter was appraised qualitatively and given a semiquantitative score on a 4-level scale as absent (0), slight (1), moderate (2) and marked (3). Further, the transversal thickness and area of the granulation tissue was quantified across all histological specimens at 4 dpw, corresponding to the expected maximum size of the newly deposited granulation tissue, by computerassisted image analysis (40 ×; ImageJ v1. 6, National Institutes of Health, Bethesda, Maryland, USA). In brief, the thickness of the granulation tissue was measured at three positions and averaged per histological specimen; while the freehand-draw tool was used to surround the transversal area of the granulation tissue which was then automatically measured by the software (Fig.
The expressed proteins were detected by chemiluminescence (Fusion Solo S, Vilber, Lourmat, France), quantified using the Evolution-CAPT software (FUSION software v17. 03, Vilber Company, San Sebastiàn, Spain), and normalized to β-actin level. For each sample, three independent analysis were carried out to quantify the average relative expression level of Hsp90, Tgf-β, Il-10, and Tnf-α.
Statistical analysis All analyses were performed using GraphPad Prism software version 5 for Windows (GraphPad Software Inc. USA). Prior statistical analysis, datasets were tested for normality (Kolmogorov-Smirnov test) and homogeneity of variance (Bartlett's test, examination of residual plots) and proportions arcsin transformed. Gross wound closure (WC c and WC d over time; Fig. 1 ) datasets were analyzed by two-way analysis of variance (ANOVA) to determine the main treatment and time effects with a post-hoc analyses Bonferroni test where differences occurred. Chisquare tests of independence were performed to evaluate the relationship between the test diets and the proportion of fish showing wound closure or expansion at 4 dpw compared to 2 dpw. Unpaired two-tailed t-tests were applied on the other datasets to compare the means of each treatment to the Control group per time points.
7  Overuse of CT in this population prolongs the emergency department visit, which has been shown to increase the rate of delirium  8  while also diverting resources from other emergency patients. Furthermore, not all emergency departments have 24-hour on-site CT scanning facilities, meaning that some of these patients may be transferred to another centre. The Canadian CT Head rule  [9] [10] [11] [12]  determines the need for head CT in patients with head injuries, but applies to patients who Methods: This prospective cohort study in 11 emergency departments in Canada and the United States enrolled patients aged 65 years or older who presented after falling from standing on level ground, off a chair or toilet seat, or out of bed. We collected data on 17 potential predictor variables. The primary outcome was the diagnosis of clinically important intracranial bleeding within 42 days of the index emergency department visit.
Interpretation: We derived a Falls Decision Rule, which requires external validation, followed by clinical impact assessment. Trial registration: ClinicalTrials. gov, no. NCT03745755. have experienced disorientation, amnesia or loss of consciousness. No research exists to guide neuroimaging use for older adults who hit their head on falling but do not experience these symptoms. Additionally, guidance from the literature is lacking for older adults who have fallen but cannot recount what happened or did not hit their head. Our aim was to derive a clinical decision rule that applies to all older adults presenting to the emergency department after a fall, and identifies patients who can have clinically important intracranial bleeding ruled out without ordering a head CT.
18  The treating physician completed a standardized data collection form (Appendix 1, available at www. cmaj. ca/lookup/ doi/10. 1503/cmaj. 230634/tab-related-content) at the time of in itial patient assessment, and before the results of the head CT were available (the physician was therefore blinded to the outcome). Additional data were collected by trained on-site research assistants following standardized validation procedures for all data from charts.
dPCR assay design A novel dPCR assay specific for Nonionella sp. T1 was designed in this study (Table  1 ). Partial sequences of SSU ribosomal RNA (rRNA) for Nonionella sp. T1 and four other nonionellids from the phylogenetic study by  Deldicq et al. (2019)  were downloaded from GenBank, 68 sequences in total, and aligned using Geneious software. Forward primer, reverse primer, and a fluorescent probe were designed with a perfect match to all Nonionella sp. T1 sequences, as many mismatches as possible to the other clades, and following dPCR primer and probe requirements according to Bio-Rad Droplet F I G U R E 3 Map of the Swedish west coast study area, with its fjords and the sampling stations therein. From north to south; The Gullmar fjord (G-stations), the By FJORD (B-stations), and the Hake fjord (H-stations).
The sediment samples were taken at the mouth (M), centre (C), and head (H) of each fjord. Bathymetric data by  GEBCO Compilation Group (2022). Digital PCR Applications Guide  (Bio-Rad Laboratories, Inc. , n. d. ). These primers (listed in Table  1 ) amplify a 115 bp-long fragment of the SSU rRNA gene, with the probe being complementary to this fragment. To ascertain the specificity of the T1-1 assay, single-cell DNA extractions were tested, including three samples of Nonionella sp. T1 and three samples of N. labradorica (mentioned above). A trial run of the dPCR method yielded positive results for all Nonionella sp. T1 samples, and zero positives for N. labradorica. These results are shown in Figure  S1  as an example of dPCR output.
CONCLUSIONS The dPCR and morphological results of this study provide evidence that the NIS Nonionella sp. T1 is generally absent from the open Skagerrak. Morphospecies analysis demonstrates that it has a significant presence in the Swedish Gullmar and Hake fjords, comprising on average 15% of the living foraminiferal community in the Gullmar fjord and 36% in the Hake fjord. The results of both molecular and morphological methods indicate that Nonionella sp. T1 is completely absent from the By fjord. Overall, both datasets show a high level of agreement between dPCR and morphospecies analyses. Nonionella sp. T1 seems to be an opportunistic, highly adaptable species, utilizing heterotrophy, denitrification, and an efficient reproductive strategy to thrive in a variety of locations. These factors have likely allowed it to gain a competitive advantage over native foraminifera and facilitated its successful establishment on the Swedish west coast.
ACKNOWLEDGEMENTS The authors would like to thank the crew and captain of  R/V Skagerak ;  Martin Hassellöv ,  Karin Mattsson ,  Kjell Nordberg ,  Per Hall ,  Astrid Hylén ,  Sebastiaan van de Velde , and all other participants of the FACTS expedition 2020 and the Swedish fjord expedition 2021 for collecting sediment samples in the field. Sophie Quinchard  ( LPG, University of Angers, France ) is acknowledged for her technical help in molecular biology. We are thankful for financial support from the  Swedish Environmental Protection Agency and Swedish Agency for Marine and Water Management  (contract  802-0107-18 ) to  Per Sundberg. Two anonymous reviewers provided positive feedbacks with constructive and thoughtful comments, which helped to improve the article. The authors received financial support from  Swedish Environmental Protection Agency and Swedish Agency for Marine and Water Management  grant no:  802-0107-18.
Background: Urinary incontinence and other urinary symptoms tend to be frequent at menopause because of hormonal modifications and aging. Urinary symptoms are associated with the genitourinary syndrome of menopause which is characterized by physical changes of the vulva, vagina and lower urinary tract. The treatment strategies for postmenopausal urinary incontinence are various and may include estrogens, anticholinergics, and pelvic floor muscle training. A comparison of these treatments is difficult due to the heterogeneity of adopted protocols. We systematically reviewed the evidence from randomized controlled trials (RCTs) focusing on treatment of postmenopausal women with urge incontinence. Methods: We conducted a systematic review and meta-analysis by searching PubMed and EMBASE databases for randomized controlled trials (RCTs) reporting results of treatments for postmenopausal urinary urge incontinence.
Search strategy Two electronic databases (PubMed and EMBASE) were searched for records reporting RCTs published up to 31 January 2023. The search was performed using strings based on broad MeSH terms (e. g. , female AND menopause AND (dysuria OR urination disorders OR incontinence) AND treatment). Relevant data were also searched by browsing the reference lists of reviews and clinical trial reports, or through other sources (e. g. , clinicaltrials. gov; https://www. clinicaltrialsregister. eu/).
Data collection and analysis (selection of studies and data extraction) Title and abstract screening to exclude records that did not meet the inclusion criteria were performed independently by 4 authors (2 authors for PubMed and 2 for EMBASE). Duplicate references were deleted. Full texts were downloaded for a second round of screening and to extract relevant information. Controversies were resolved by the research coordinator (AT). A PRISMA flow diagram was drawn to illustrate the results of the study selection process (Figure  1 )  (6). Data extraction was conducted by 4 authors using a standardized form. The following data were obtained from each study: author(s), publication year, study design, population, intervention(s), results. In case of missing or insufficient information, we performed sensitivity analysis, in order to consider the impact of missing data on the meta-analysis results.
Assessment of publication bias Publication bias was assessed by generating funnel plots if meta-analyses included at least 4 trials. The Begg's and Egger's tests were used to test funnel plot (a)symmetry and to confirm or exclude the presence of publication bias or small study effect. Data on funnel plots were presented as the natural logarithm of odds ratios. The MetaEssential 2 software (Rotterdam School of Management, Erasmus University, The Netherlands) was used for funnel plot analysis.
Data collection In the study sample, patient demographic and basic clinical data were retrospectively collected through medical records and ICU follow-up charts, by two research medical practitioners who were blinded to the conduct of BWS sessions. Patients' clinical data included injury type and severity, global motor assessment upon ICU discharge (quadriplegia, hemiplegia, hemiparesis, no impairment), duration of ventilation, duration of stay in ICU and in hospital, and delay before walking without suspension. Regarding BWS walking sessions, all the data was collected without blinding by one of the two physiotherapists of the unit, while performing the session. Data regarding BWS session was collected on standardized forms, which included pre-session and post-session clinical evaluations, and description of sessions.
Data analysis Characteristics of the sample were described; median and interquartile range (IQR) were used, since distribution of most quantitative variables were non-normal. Analysis of BWS walking sessions pooled all sessions performed. Descriptive statistics were presented, along with histograms of pre-post session changes for clinical parameters (BP, HR, SpO 2 , pain). In order to represent delays before (1) weaning of ventilation, (2) first BWS walking, and (3) first unsuspended walking, survival curves using the Kaplan-Meier method were computed  [17]. The curves showed the probability of reaching each successive goal at increasing times from initial intubation, goals being treated as events. The maximal delay considered was 150 days, information on walking ability and their delays was obtained by contacting post-hospital care centers when necessary.
If the information on post-hospitalization walking ability was unavailable (n = 2 cases), the patient's data was rightcensored at the date of hospital discharge-the survival analysis considered these cases as lost-to-follow-up from the date of censoring, and subsequently calculated proportions of patients who reached unsuspended walking with the remaining patients. Statistical analyses were computed with R ®  [18] , version 3. 6. 0; survival curves used the package Survival  [19].
After 3 weeks, the surface water and the leachate at the bottom of the monoliths were collected from the drainage tube (Fig. 1 ) with a syringe, and the total volume of leachate recorded and kept at 2 • C for further analysis. Meteorological data (rainfall and air temperature) were recorded at the nearest meteorological station, which was within 4 km of the field site. The air temperature inside and outside the chamber was measured at each monolith and used to correct the concentrations of N 2 O inside the chamber. The microclimatic conditions were measured using thermal and soil moisture microclimatic sensors (TMS-4, TOMST Ltd. , Prague, Czech Republic). The sensors are stand-alone, fully automatic, and measured the temperature at 15 cm above-ground, near the ground and 8 cm below-ground, and soil moisture at an interval of 15 min  (Wild et al.
Acknowledgements This study was supported by an  NWO   ALW grant  awarded to GBDD (grant number  ALWOP. 448 ). The authors thank  Barenbrug BV ,  The Netherlands  for the seeds used in this experiment. Thanks to the staff of the Unifarm, especially,  John van der Lippe  and  Frans Bakker  for their support designing and in the construction of the monolith cores with leaching collection, and in the field. Thanks also to  Caio Zani ,  Gijs Smits van Oyen ,  Noor den Hartog  and  Peter Garamszegi  for their assistance with the field and laboratory work, and  Natalie Oram  for her advice on R scripts and analyses.