dataesr/scientific-paragraphs-categorization huggingface dataset:
Holter recordings of patients under the age of 18 years were excluded since this research focused on AF diagnosis in adults. Furthermore, corrupted recordings were also excluded. Overall, out of 2891 UVAF-train recordings, 106 (3. 7%) were excluded due to low quality or missing annotations as summarized in Fig. 1. No recordings were excluded from the test sets. The PhysioZoo software  11, 12  was installed on a server within the Technion domain and accessed remotely by a fellow in cardiology annotator (MA), who annotated the test set Holter ECGs. Supraventricular arrhythmias were divided and annotated, using our annotation protocol, into five categories: (1) AF, (2) AFL, (3) atrial tachycardia (AT), (4) Other supraventricular tachycardias such as Wolf-Parkinson-White and intranodal tachycardias and (5) other, such as NSR, that were not labeled.
In our context, the average inference time for a window classification was 0. 04 ms for ArNet2 versus 0. 54 ms for XGB. Thus, in addition to the performance gain of ArNet2 over XGB we also report on a gain in running time. Previous research have shown interesting results in AF classification from beat-to-beat interval  13, 14. However, we could not objectively benchmark their models against ArNet2 since their codes are not open source and the test databases they report on differ from our test databases. AFL events were identified in 2. 94% of the UVAF, 7. 22% of the SHDB, 14. 13% of the RBDB and 0. 43% of the CPSC recordings. When assessing ArNet2 across sexes, performance was higher for females than males. The vast majority (83. 34%) of AFL windows in the combined test set belonged to the male group and only 16.
66% belonged to the female group. Thus, the prevalence of AFL among men was significantly higher than in women in our test set. When assessing ArNet2 for different age groups, the percentage of AFL windows out of the total AF l windows per age group was found to be 0. 8% for adults aged ≤60 years, 10. 5% for adults aged 61-75 years and 6. 3% for the >75 age group. Taken together, the relative prevalence of AFL to AF was significantly higher in the elderly population (>60 years). The observations made for ArNet2 performance across geography, sexes and age groups were consistent for XGB and ArNet (Fig. 3 ). The observations on performance presented by patient sex are consistent with a previous single-center study conducted by a group from Wisconsin (USA)  15  , who analyzed medical records from a dataset of 58,820 patients.
However, there is limited research comparing AF l events detection based on beat-to-beat intervals versus on raw ECG data and thus it remains to be experimentally proven that analyzing the raw ECG versus the beat-to-beat interval variation would yield better results. Taken together, epidemiological consideration on the relative prevalence of AFL to AF across different geography groups and across age groups is warranted to better define the population sample expected to benefit from data-driven models based on the beat-to-beat interval variation versus the raw ECG waveform. In terms of median E AF % ð Þ j j , ArNet2 better performed on non-AF l and AF sev patients then for other AF l severity levels for all datasets, Supplementary Table  7. Fig. 5  The probability output of ArNet2 for the combined test set FP windows grouped to the true window labels.
We have shown the superiority of ArNet2 over classical machine learning approaches taking engineered features such as CosEn as input  9. ArNet2 also significantly outperformed (p < 0. 001) ArNet (Fig. 3  and Supplementary Table  1 , Supplementary Table  4  and Supplementary Table 6 ). The third main contribution of this research was the evaluation of ArNet2 on a large dataset of 1,825 consecutive recordings collected from Rambam Hospital Holter clinic. While considering an AFB decision threshold at 4%, ArNet2 yielded better performance for female than male patients for all performance measures. For the age groups, the AUROC, Se, Sp and NPV were higher for the adults aged ≤60 years than the older >60 years groups while the PPV, F1 and AUPRC were higher for the older groups. This is due to the very low prevalence of AF l recordings in the ≤60 years group (2.
For this purpose, the development of data-driven models based on raw ECG data may be necessary. Overall, the datasets for our analysis totaled 4298 recordings and over 99,705 h of continuous data. We demonstrated that our new model is robust and generalizable across geography, ages and sexes being consistently significantly superior to benchmarks. We attributed differences in performance across geography groups, sex and age to the relative prevalence of AFL. However, other factors such as the prevalence of ectopic beats may also play a role in the misclassification of some windows. Since AF and AFL have different clinical management pathway, our findings support the need to discriminate between the AF and AFL classes in future work. Research is also warranted in documenting the relative prevalence of AFL to AF according to age as well as across geography.
Development set The UVAF  7, 8  consists of ECG Holter recordings of patients for whom the University of Virginia health system physicians ordered Holter monitoring between December 2004 to October 2010. Indications for the Holter recordings included palpitations (40%) or syncope and dizziness (12%). The dataset contains 2247 annotated recordings of individual patients over the age of 18 years. Beat labels were automatically generated by the medical monitor Philips Holter software. Part of these recordings (52. 3%) were manually overread for the presence of AF l as described in Carrara et al. 7  Recordings were recorded using Philips Holter software and digitized at 200 Hz. UVAF was used to train (UVAFtrain, n = 2147) and evaluate (UVAF-test, n = 100) the DL models.
Test sets The demographic characteristics of patients from the test sets are summarized in Table  1  and Supplementary Fig. 3. Age and sex were not statistically different across the test sets using the Kruskal-Wallis statistical test with Dunn's post hoc analysis. The age was statically different (p < 0. 001) between the training and test sets. We elaborated three test sets denoted UVAF-test, RBDB-test and SHDB-test. These include two newly created datasets: the Rambam Hospital dataset (RBDB) from Israel and the Saitama Hospital dataset (SHDB), Japan. In compliance with all relevant ethical regulations we obtained for the newly collected datasets, ethical approval for using the retrospective de-identified data was granted by the Saitama Medical University institutional ethics committee under IRB number 20-030 and the Rambam Health Care Campus institutional ethics committee under IRB: D-0402-21.
RBDB and SHDB consisted of the raw ECG recordings and corresponding medical report prepared following the Holter examinations. The reference rhythm annotations at the beat level were obtained for a subset of these datasets in order to create RBDB-test and SHDB-test. For that purpose, a total of 100 recordings were selected from the original databases while stratifying by age, sex and diagnosis for AF l. Specifically, 80 recordings from patients that had AF l based on the cardiology report were selected for each test set. For the RBDBtest and SHDB-test, the AF l diagnoses were obtained from the medical report prepared following the patient's examination. As no patient reports were available for UVAF, the diagnosis was inferred from the AF l events annotated in the recordings. For the CPSC-test the complete available open dataset was used as is.
Holters from the SHDB-test were recorded using Fukuda Holter monitor and digitized at 125 Hz and two leads were recorded, modified CC5 and NASA leads. The UVAF-test, RBDB-test and SHDB-test recordings were manually re-annotated (see Expert annotations of test sets). For the CPSC-test AF and AFL beat labels were created automatically by the Holter software and later reviewed by cardiologists to correct the onsets and offsets of AF l. The open access labels were used as provided. At last, all test sets were combined under Combined test set to assess global performance. Based on the clinical data at our disposal and Holter findings we estimate that most AFL cases were typical. Apart from geographic groups, generalizability performance was assessed with respect to sex and age groups. Performance across sex was assessed for the combined test for each sex group.
Simulating intended use scenario In addition to the RBDB-test recordings, an additional 1725 consecutive recordings retrospectively obtained from the clinic from the period August 2017 and November 2021, were obtained from the Rambam Hospital Holter clinic. We combined those additional recordings with RBDB-test and denoted the new dataset RBDB-test2. The free texts of the Holter examination reports were reviewed manually for the diagnosis of AF and AFL. If short AF or AFL events were explicitly reported, then the corresponding Holters were reviewed by a fellow in cardiology (AE) and if the event(s) were shorter than 30 s, then a non-AF l label was given to the recording. If at least one event was longer than 30 s, then an AF lpositive label was given to the recording  22. As a result, the RBDB-test2 set consisted of 1825 recordings with a binary label for the presence or absence of AF l rhythm.
Preprocessing All datasets were resampled to 200 Hz using an anti-aliasing filter. A zero phase second-order infinite impulse response bandpass filter with the passband [0. 67-100]Hz 25 was used to remove baseline wander and high frequency noise. Beat annotations were detected using epltd implementation of the Pan and Tompkins algorithm  26. The RR intervals, based on the beat annotations, were divided into non-overlapping windows of 60-beats (Fig. 1 ). For each window, the bSQI index  27  was computed with an agreement window of 50 ms. We used epltd as the reference peak detector and xqrs  20  as test peak detector. Regarding the UVAF, some reference beat labels were missing. To account for that, windows with over 10 s of missing beat labels were excluded. Patients presenting over 25% of 60-beat windows with missing beat labels based on this criterion were discarded.
ACKNOWLEDGEMENTS This research was supported for S. B. , S. G. , and J. A. B. by a grant ( 3-17550 ) from the  Ministry of Science & Technology, Israel & Ministry of Europe and Foreign Affairs (MEAE)  and the  Ministry of Higher Education, Research and Innovation (MESRI) of France  and  ERANET -3-16881  from the  Israeli Ministry of Health  and the  Technion-Rambam Initiative in Artificial Intelligence in Medicine (TERA). We thank  Iris Eisen  from the  Department of Cardiology at Rambam Health Care Campus  and  Aya Gosa  from the  Department of Cardiovascular Medicine, Faculty of Medicine, Saitama Medical University International Medical Center, Japan  for supporting the export of the Holter data.
Materials and methods The checklist of the tenuipalpid mites reported from the Bahia state territory includes all the species records published in periodical journals, annals, and/or books, and newly identified specimens collected in the present study. The acquisition of published records was based on internet searches, the catalog of  Mesa et al. (2009) , and the database of  Castro et al. (2022a). All the records were confirmed through direct consultation with original publications, voucher specimens and the type image reference at the Flat Mites of the World. Mite specimens collected between March 2007 and November 2021 from fifteen municipalities in the Bahia state territory, northeastern Brazil were identified for the present study. Samples consisted of leaves and branches of twenty-nine plant species/varieties in seventeen families: Anarcadiaceae -(  1   27 ) Alpinia purpurata var.
Acknowledgements We thank the " Coordenação de Aperfeiçoamento de Pessoal de Nível Superior" (CAPES)  for the fellowships to RSN (Finance Code  001 ), and  RSM (PNPD/Agronomy Proc. 88882. 305808/2018-1 ), the  "Fundação de Amparo à Pesquisa do Estado de São Paulo" (FAPESP)  for the research grant to ADT (Proc. 2018/12252-8  and  2019/25078-9 ), and  UESC, ESALQ ,  UNESP  and  USDA  for the support. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  USDA; USDA  is an equal opportunity provider and employer.
At selected time points (0, 2, 5, 10, 30, 60, 120 and 180 min) after syringe preparation, ten microliters (in triplicate) were carefully withdrawn (using a calibrated pipette) from the syringe and deposited in a 5 mL tube. Additionally, after the last sampling at 180 min, we also investigated the impact of homogenising the solution by 10 inversions of the syringe. The radioactivity in the tubes was determined by a gamma counter as described above. During the waiting period, the syringes were stored at room temperature, protected from light. For data analysis, 0 min was set as reference. At each time point (T), the percentage of radiopharmaceutical that does not undergo sorption-type interactions with the syringe was determined according to the following equation: Contact angle measurements.
Data and statistical analyses. Values were expressed as mean and standard error of the mean (SEM). The normality was analyzed using a Shapiro-Wilk test. To compare the sorption kinetics between 2 and 3-part syringes of each radiopharmaceutical, random-effects models were performed to take into account longitudinal repeated data. The following fixed effects were evaluated: group (2 and 3-part syringes), time and their interaction. The normality of residuals was analyzed as aforementioned. When appropriate, a logarithmic transformation of sorption was applied. Statistical analyses were conducted using Stata software (version 15, StataCorp, College Station, US) with a two-sided type I error at 5% corrected due to multiple comparisons. More precisely, a Sidak's type I error correction was applied.
SPECT/CT imaging allowed us to precisely pinpoint the location of the interaction as being caused by the plunger stopper, as shown in Fig. 2. To our knowledge this is Table  4. Contact angle measurements results, expressed in degree (°) as mean ± standard deviation. Eight measurements were performed for each sample. the first published study using this sort of analysis to help identify the causal material of a sorption phenomenon and opens up interesting perspectives for future studies for example of other devices. This finding is also coherent with the Supplementary data provided in some recently published work showing a loss of diazepam ranging from 25% to more than 50% when left in contact with syringe plungers  11. Additionally, it seems to indicate that polyisoprene is not inert, and caution should be applied when using this material in contact with medications.
2021). This is mostly due to the lack of wide-field X-ray surveys with the sensitivity to detect these mostly extragalactic sources. eROSITA was launched in 2019 and the data from its all sky surveys will have the potential to change this. Most ULXs known today have been identified serendipitously in pointed imaging X-ray observations by XMM-Newton,  Chandra, and Swift (e. g. , Liu & Bregman 2005; Liu & Mirabel 2005; Winter et al. 2006; Swartz et al. 2011; Walton et al. 2011; Earnshaw et al. 2019b; Kovlakas et al. 2020) , with the latest catalog of ULX candidates containing 1843 sources  (Walton et al. 2022 ). However, the relative rates of persistent and transient sources is unknown. A few detailed studies of transient ULXs discovered serendipitously have been presented in the literature (e.
1 × 10 -14 erg cm -2 s -1 in the 0. 2-12 keV band listed in XSA. A 150 ks XMM-Newton observation of NGC 4945 took place on 2022 July 5, 284 days after Swift J130511. 5-492933 was detected. The XMM data were reduced using a pipeline that utilizes v 19. 1. 0 of the Science Analysis Software (SAS). The cifbuild command was used to create a current calibration file corresponding to the observations, and the odfingest command was used to produce a SAS summary file. The data were reduced and MOS and pn event files were created using the emproc and epproc commands, respectively. We first identify periods of high background by creating a lightcurve of the events in the 10-12 keV band, creating good time intervals where the rate was <0. 4 counts s -1 in this band in the pn detector, leaving 99 ks for the pn and 101 ks for the MOS.
2SXPS J235825. 7-323609, an X-Ray Transient in the Field of NGC 7793 This X-ray source was first detected in a Swift/XRT observation taken on 2018 April 28 (obsID: 00094097003) and not found in our real-time search, rather a search through archival data. The target of the Swift observation was NGC 7793 P13, a ULX hosted by NGC 7793  (Read & Pietsch 1999)  known to be a ULX pulsar  (Fürst et al. 2016; Israel et al. 2017b). The enhanced XRT position given by the online tool was R. A. = 359°. 60774, decl. = -32°. 60254 (=23 h 58 m 25 86, -32°36′09 2) with an error radius of 2 5 (90% confidence; see Figure  6 ). The source is listed in the 2SXPS catalog as 2SXPS J235825. 7-323609 with a mean count rate of 5. 37 ± 0. 76 × 10 -4 counts s -1 , detected in a stack of data over the date range 2010 August 16-2018 July 28.
9-323610 at R. A. = 23 h 58 m 25 98 and decl. = -32°36′10 5 with a positional error of 1 0, which is an improvement on the XRT position. Fortuitously, HST has observed the region of 2SXPS J235825. 7-323609 as part of the GHOSTS survey (Radburn-Smith et al. 2011) with the ACS and F606W and F814W filters. However, no source is listed in the Hubble Source Catalog (HSC, v3) within the XMM-Newton positional error and the closest source lies 2 1 away. We ran UVOTSOURCE on the mostly U-band UVOT data but did not detect the source in any observation to a limiting magnitude of U ∼ 21. 5.
9-323526, which occurred on 2022 October 7 (obsID: 90801526002), with an exposure time of 53 ks. We used HEASOFT v 6. 28, NUSTARDAS v 2. 0. 0, and CALDB v 20211115 to analyze the data. We produced cleaned and calibrated event files using NUPIPELINE with the default settings on mode 1 data only. We used NUPRODUCTS to produce spectral data, including source and background spectra, and response files. A circular region with a radius of 40″ was used to extract the source spectra and a radius of 80″ was used to extract the background spectra, taking care to extract the background from the same chip as the source. The source is detected with a count rate of 5 × 10 -3 counts s -1 in the 3-10 keV band in each focal plane module (FPM), above which the background dominates the source. We used the absorbed power-law model described above to determine the flux plotted in Figure  10.
We also obtained a Chandra DDT observation of the source, which took place on 2022 October 27 (obsID: 27481), with ACIS-S at the aimpoint in VFAINT mode. The source was well detected, with a count rate of 4. 09 × 10 -2 counts s -1 in the 10 ks exposure. We extracted the Chandra spectrum with SPECEXTRACT from a circular region of radius 2 0 for the source and an annulus radii of 3 1 and 6 2 for the background. The spectra were grouped with a minimum of one count per bin with the tool GRPPHA. Again we used the absorbed power-law model from the Swift data to determine the flux plotted in Figure  10. We then fitted the Swift, NuSTAR, and Chandra data simultaneously initially with an absorbed power-law model in XSPEC, with a constant to account for cross-calibration uncertainties and the flux variability of the source.
The source was well detected, with a count rate of 3. 89 × 10 -2 counts s -1 in the 10 ks exposure. We extracted the Chandra spectrum with SPECEX- TRACT from a circular region of radius 2 0 for the source and an annulus radii of 2 5 and 12″ for the background. The spectra were grouped with a minimum of one count per bin with the tool GRPPHA. We fitted the spectrum with an absorbed power-law model as done for the Swift/XRT data, which yielded W = 27. 66 with 35 DoFs, where N H < 2. 8 × 10 22 cm -2 and G = -+ 4. 6 2. 6 3. 5 , consistent with Swift/XRT, albeit with large uncertainties. If we fit the stacked Swift/XRT data and the Chandra data together, we find W = 109. 49 with 128 DoFs, where N H < 6. 8 × 10 21 cm -2 and G = -+ 2. 6 1. 1 1. 6. We show these spectra in Figure  14. A diskbb model in place of the powerlaw one produced a worsened fit with ΔC = 10, where N H < 5 × 10 20 cm -2 and = -+ T 1.
The sources were detected at positions of 13 h 05 m 05 5, -49°31″ 39″ and 13 h 05 m 17 0, -49°30′ 15″, respectively. Suzaku J1305-4931 was active in 2006 January and Suzaku J1305-4930 was active in 2010 July, and lasted less than 6 months. Both Suzaku sources were close to our Swift/XRT sources (∼1′ from either), but closer to the plane of the galaxy. In addition to 2SXPS J235825. 7-323609 and Swift J235749. 9-323526 in NGC 7793,  Quintin et al. (2021)  reported the discovery of another transient ULX in that galaxy found while looking for long-term variability of XMM-Newton sources in the 4XMM Data Release 9 catalog. The source, which they name NGC 7793 ULX-4, was active for 8 months from 2012 May-November. The ULX had a position of 23 h 57 m 47 9-32°34′ 57″, which is close to the center of the galaxy, ∼8′ from 2SXPS J235825.
In other words, the number of systems that exhibit ULX luminosities in each of these galaxies is dominated by transient rather than persistent sources. Since we have only considered galaxies where a transient ULX has been identified in our searches, we cannot extend this conclusion to all galaxies. The rates are also biased by the Swift targeting and our incomplete search of observations. A more systematic search using eROSITA data could reveal the true rate. However, we note that the 6 months scanning pattern of eROSITA means some of the sources we identified can be missed. While the duration of the transient sources studied here is well determined, the duration of the persistent sources is not well known. However, evidence points to their far longer duration. For example, the collisionally ionized bubbles surrounding Holmberg IX X-1, NGC 1313 X-1 and X-2, NGC 7793 S26, and NGC 5585 ULX have estimated dynamical ages of ∼10 5 yr  (Pakull & Mirioni 2002; Pakull et al.
Acknowledgments We wish to thank the  Swift PI, Brad Cenko , for approving the target of opportunity requests we made to observe these transient sources, as well as the rest of the Swift team for carrying them out. We also acknowledge the use of public data from the Swift data archive. This work made use of data supplied by the  UK Swift Science Data Centre at the University of Leicester. We also wish to thank  Patrick Slane , Director of the  Chandra X-ray Center , for approving the DDT requests to observe Swift J130456. 1-493158, Swift J095520. 7+690401 , and Swift  J235749. 9-323526 , and the Chandra team for carrying out the observations. In addition we wish to thank the  NuSTAR PI ,  Fiona Harrison , for approving the DDT request we made to observe Swift J235749. 9-323526 as well as the  NuSTAR SOC  for carrying out the observation.
12 and 1. 71, respectively  (Nash et al. 2015; Tamburello et al. 2015). Nash et al. (2015)  highlighted an important potential confounding factor: most allometric studies derived scaling coefficients using animal activity space data from multiple studies that used a range of techniques (i. e. , visual tracking, mark-recapture, telemetry, mixed methods), potentially affecting scaling coefficients. They also highlighted that over time, the principal method used to define activity space shifted from minimum convex polygons up to the early 1990s to kernel utilization distributions (KUDs). This change in approach could readily add a methodological source of uncertainty, as opposed to a biological source, in describing the relationship between activity space and body size. Tamburello et al. (2015)  recognized that there was likely to be significant variability among the source data and attempted to account for the method by which activity space was measured by including it as a random effect in the model.