dataesr/scientific-paragraphs-categorization huggingface dataset:
LA strain analysis LAS analysis was obtained using an automated speckle tracking software (Auto-Strain QLAB 13. 0, Philips Medical systems, Andover, MA, USA) with a LAS dedicated mode. The LAS was defined as the strain value in three phases: reservoir strain in systole (LASr), conduit strain in early diastole (LASct) and contraction strain in late diastole (LAScd)  [10]. LASr was a positive value, while LASct and LACcd were negative values. LAS values, for each phase, were obtained from an optimized apical fourchamber view using an automated approach as recommended  [10]  (Fig. 1 ). The regions of interests (ROI) were generated automatically and LA endocardial border was manually adjusted when required. The QRS complex was used as initial zero-baseline strain electrocardiogram reference point as recommended  [10].
The calibration of the model was assessed by the Hosmer-Lemeshow goodness-of-fit statistic (good fit was defined as a p value of > 0. 05)  [22]. The C-statistic test was used to test the ability of the model to discriminate patients with and without AF  [23]. Data are presented as odds ratio (ORs) and 95% confidence intervals (CIs). Cumulative risk curves, as function of time, were generated using the Kaplan-Meier method, and compared by the log-rank test. A statistical test was significant when P value was under 0. 05. All P values are the results of 2-tailed tests. Statistical analyses were performed using SPSS software version 24 (IBM Corp, Armonk, NY).
Limitations The first limitation of our study is the limited sample size especially in the AF group. Second, LAS analysis was calculated only from a single four-chamber cardiac view. Computation of the biplane LAS (four-chamber and twochamber view) would have provided more data on LA function. However, the use of a single apical view is currently recommended for a greater feasibility  [10]. Third, we were faced with the known limitations factors of LAS analysis (far field, pulmonary veins and LA appendage orifice, LA thin walls)  [40]  leading to the exclusion of 6 patients from the study. In our study, LAS measurement was strongly reproducible probably due to the fact that LAS analysis was performed by an echocardiography expert (level III competence according to the EACVI definition  [21] ).
Methods Patients aged 40 years or older who had at least two episodes of unpredictable severe reflex syncope during the last year and a tilt-induced syncope with an asystolic pause >3 s were included. After pacemaker implantation, 63 patients randomized to pacing ON (DDD-CLS mode, 50 bpm basic rate and 120 bpm maximum pacing rate) experienced a 77% reduction in syncopal recurrence over 1 year as compared to 64 patients randomized to pacing OFF (ODO mode, only sensing functions and related pacemaker diagnostic data). The off-label use of the ODO mode allowed collection of long-term profiles of spontaneous heart rate (HR) for comparisons with the pacing ON group. In the present analysis, we utilized the available pacemaker diagnostic data in a total of 103 patients (52 pacing ON, 51 pacing OFF) to generate cumulative distribution charts for HR and percentage of pacing up to 12 months post-implantation.
Methods For model development, CT images of 100 patients who underwent a whole-body or torso 18 F-fluorodeoxyglucose PET-CT scan were retrospectively included. Two radiologists semi-automatically labeled the following seven body components in every CT image slice, providing 39,268 images for training the 3D U-Net: skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs with vessels, and central nervous system. The segmentation accuracy was assessed using reference masks from one internal and three external datasets: two domestic centers (n=20, each) and a French public dataset (n=24). The 3D U-Net-driven values were clinically validated using bioelectrical impedance analysis (BIA) and by assessing the model's diagnostic performance for sarcopenia in a communitybased elderly cohort (n=522).
Results The 3D U-Net achieved accurate body composition segmentation with an average dice similarity coefficient of 96. 5% to 98. 9% for all masks and 92. 3% to 99. 3% for muscle, abdominal visceral fat, and subcutaneous fat in the validation datasets. The 3D U-Net-derived torso volume of skeletal muscle and fat tissue and the average area of those tissues in the waist were correlated with BIA-derived appendicular lean mass (correlation coefficients: 0. 71 5 and 0. 72, each) and fat mass (correlation coefficients: 0. 95 and 0. 93, each). The 3D U-Netderived average areas of skeletal muscle and fat tissue in the waist were independently associated with sarcopenia (P<. 001, each) with adjustment for age and sex, providing an area under the curve of 0. 858 (95% CI, 0. 815 to 0. 901).
Study population For the training dataset, we retrospectively collected 100 unenhanced whole-body CT scans of PET/CT examination from 100 patients, preferentially for the purpose of an initial staging workup of early lung cancer or systematic screening at a single tertiary center (Figure  1 ). We used three datasets from other institutions for external validation to evaluate segmentation performance between the reference masks and the network-derived masks (Table  1 ): the first dataset contained 20 patients with stage I lung cancer, the second dataset contained 20 participants in a community-based older adults cohort with 20 CT scans, and the third dataset contained 24 CT scans of 18 patients with head and neck cancer. For clinical validation, we used the KURE cohort as a fourth external dataset  (17).
Preparation of training CT data The whole-body CT images were processed in a commercially available software program (MEDIP PRO v2. 0. 0. 0, MEDICALIP Co. Ltd. , Seoul, Korea) for semi-automatic segmentation of the following seven components of body composition: skin, bone, muscle, abdominal visceral fat (AVF), subcutaneous fat (SF), internal organs and vessels (IO), and central nervous system (CNS). The bone mask extended from the skull to the phalanges and contained the hyoid bone, cricoid, arytenoid, thyroid cartilages, and rib cartilages. The muscle mask included skeletal muscles, including the proximal portion of the thin diaphragm and crus as much as recognizable, resulting in the exclusion of the central tendon of the diaphragm. The AVF mask included fatty tissues of peritoneal and retroperitoneal spaces  (18)  that were distributed below the xiphoid process and diaphragmatic hiatus to the perirectal fat.
Development and materialization of the deep neural network The 100 CT scans were randomly assigned into one of the three following data sets: 80 cases for the training set, 10 cases for the tuning set, and 10 cases for the internal validation set. Most of the CT scans consisted of 2-mm-section CT images that covered the head to the lower arm and mid-thigh. In total, 39,286 slices of axial data were available in the dataset. The training data of CT images were normalized using the soft tissue window setting. The two-dimensional (2D) U-Net received an input size of 512×512×1 and consisted of initial convolutions, four encoders, four decoders, and a final convolution. Except for the final convolution (a 1×1 convolution), every convolutional layer consisted of a 3×3 convolution followed by batch normalization  (19)  and the rectified linear unit activation function  (20).
The 2D and 3D U-Net models were packaged as a standalone software (DeepCatch v1. 0. 0. 0; http://www. medicalip. com/Download) by a functional modification of the FDAapproved MEDIP PRO v2. 0. 0. 0. The specifications required for running the U-Net models are as follows: operating system, Microsoft Windows 7 (64 bit; IBM Corporation, San Jose, CA) or higher; central processing unit, Intel i5 (Intel Corporation, Santa Clara, CA) or higher; random-access memory, 8 GB or higher; and graphics processing unit, GeForce 1000 series or higher (NVIDIA Corporation, Santa Clara, CA) with the minimum memory of 2 GB or larger for 2D U-Net and 8 GB or larger for 3D-UNet supporting the Compute Unified Device Architecture with the latest drivers. The segmentation was accomplished in around 45 seconds for the 2D U-Net model and in around 75 seconds for the 3D U-Net model with the recommended specifications.
Clinical validation cohort: KURE The KURE cohort is a government-funded cohort of community-based Korean older adults, in whom cardiovascular, musculoskeletal, and age-related diseases were studied  (17). A total of 3517 subjects participated in the study between 2012 and 2015, and a follow-up study was completed between 2016 and 2019. CT images of the torso area (from neck to midthigh level) were obtained in the follow-up study through a comprehensive assessment of musculoskeletal function, physical performance, and radiological and biochemical measurements. Because CT images were only available at follow-up, a subset of the data of the KURE cohort collected in 2016 was analyzed as the clinical validation set. Details of the KURE cohort can be obtained in the cohort profile  (22). The KURE population was included in prior studies on study protocol  (17, 22) , vitamin D  (23) (24) (25) , dysmotility  (26) , and metabolic syndrome  (27, 28).
Segmentation performance in the development set The mean age and sex distribution of the subjects in the development set (internal set) and the three external validation sets for segmentation accuracy were 62. 3±13. 3 years (women, 60%), 68. 0±10. 5 years (women, 45%), 74. 0±3. 7 years (women, 55%), and 58. 5±9. 3 years (women, 17%), respectively (Table  1 ). Both the 2D and 3D U-Net achieved accurate body composition segmentation (Table  2  and Figure  2 ), and the 3D U-Net performed significantly better than the 2D U-Net for all seven masks (Table  S1 ). The average DSCs between the U-Net results and the reference for all masks were as follows: the internal dataset, 95. 3±3. 0% and 96. 9±2. 4%, each; the first external dataset, 93. 3±1. 0% and 96. 5±1. 1%, each; and the second external dataset, 95.
6±1. 1% and 98. 9±0. 4%, each. When confined to muscle, AVF, and SF masks, the average DSCs between the 2D and 3D U-Net results and the reference masks were as follows: the internal dataset, 94. 6±5. 6% and 96. 8±3. 5%, each; the first external dataset, 94. 4±2. 7% and 97. 0±0. 9%, each; the second validation dataset, 96. 2±2. 5% and 99. 3±0. 9%, each; the third external dataset, 90. 3±3. 6% and 92. 3±2. 2%, each. The segmentation performance of the 3D U-Net was generally not affected by age, sex, BMI, or scan range (Table  S2 ).
Discussion The 2D and 3D U-Nets developed in this study were validated in several datasets, including the three of the four major clinical CT vendors  (38). The correlations observed between the U-Nets and the reference values in the internal validation dataset were relatively well reproduced in the external validation datasets. The 3D U-Net performed better than the 2D U-Net for all masks, and the performance of the 3D U-Net was mostly maintained regardless of age, sex, BMI, and scan range. The magnitude of the correlations was slightly lower in the French public dataset than in the other two external validation datasets. This partly originated from differences in the individual standards for muscle (i. e. , whether they contained axillary vessels) and AVF (i. e. , whether some feces and outer boundaries outside the pelvic floor muscle were contained) in the French public dataset.
Our study has several limitations. First, the amount of validation data for assessing the DSC was relatively small, which was somewhat inevitable as the required time and resources for preparing the reference dataset were substantial. Second, our datasets preferentially included the elderly population. Third, the muscle mask inevitably contained distal peripheral vessels, and intrathoracic visceral fat and intramuscular fat were included in the SF mask. Fourth, we only validated the U-Net-driven muscle and fat volume in the abdominal area in the elderly cohort, although the network was applicable to other body parts (e. g. , thoracic area). Fifth, we only presented the results of the U-Net models for noncontrast CT scan due to difficulty in preparing a sufficient number of reference data, while the 3D U-Net for contrast CT scan was also developed.
The 3D U-Net showed a similar DSC of 95. 4% for all masks (muscle, 96. 7%, AVF, 95. 1%; SF, 96. 2%) in the preliminary analysis of the internal dataset. Sixth, the training dataset lacked any patients with ascites, major anatomical variations, or diffuse subcutaneous edema; therefore, it is uncertain whether this algorithm will also work well in such cases.
Conclusion A deep neural network enabled the automatic volumetric segmentation of body composition on whole-body CT images, and the 3D U-Net-derived average areas of skeletal muscle and fatty tissue in the waist were independently associated with sarcopenia and dysmobility syndrome in the elderly. The network-driven automatic volumetric segmentation of CT images of PET-CT scan will enable body composition analysis as adjunctive output of PET-CT scan and volumetric analysis of metabolism in whole-body fat and muscle. Abbreviation: CT, computed tomography; kVp, peak kilovoltage; Ref mAs, Reference milliampere-second. Data in the parenthesis indicates the range of body mass index. All CT scans were obtained without contrast enhancement. Statistical comparisons were performed in the pooled data of the internal dataset, the first, and the second external datasets.
technician and one radiology resident (Y. S. L. with 3 years of body CT interpretation) initially segmented the CT images by using thresholds of Hounsfield units (HU), providing rough masks of muscle, fat, and bone as follows: -29 to 150 HU for skeletal muscle, -190 to -30 HU for adipose tissue, >250 HU for bone. Then, one body radiologist (S. H. Y. , with 15 years of body CT interpretation) adjusted the masks in every axial CT image slice for the training dataset of 100 CT scans by manual correction, morphological closing, subtraction, and merging. Two body radiologists (J. H. H. and J. H. P. with 6 and 5 years of body CT interpretation, each) prepared the reference masks for the first and second external validation data of the 20 CT scans in a similar way, respectively. Reference masks in the third external validation data were prepared by the French nuclear medicine physician (P.
A technician and one radiology resident (Y. S. L. with 3 years of body CT interpretation) initially segmented the CT images by using thresholds of Hounsfield units (HU), providing rough masks of muscle, fat, and bone as follows: -29 to 150 HU for skeletal muscle, -190 to -30 HU for adipose tissue, >250 HU for bone. Then, one body radiologist (S. H. Y. , with 15 years of body CT interpretation) adjusted the masks in every axial CT image slice for the training dataset of 100 CT scans by manual correction, morphological closing, subtraction, and merging. Two body radiologists (J. H. H. and J. H. P. with 6 and 5 years of body CT interpretation, each) prepared the reference masks for the first and second external validation data of the 20 CT scans in a similar way, respectively. Reference masks in the third external validation data were prepared by the French nuclear medicine physician (P.
Annual Mean PM 2. 5 Concentrations The data on PM 2. 5 used in this study were selected from the PM 2. 5 grid estimation dataset of China (V4. CH. 02) simulated by the Atmospheric Composition Analysis Group with a spatial resolution of 0. 01 • × 0. 01 • (URL: http://fizz. phys. dal. ca/~atmos/martin/ ?page_id=140#V4. CH. 02 accessed 22 September 2020). Considering that the response of PM 2. 5 concentrations to natural and socioeconomic factors requires a certain amount of time to develop spatial pattern changes on a large spatial scale, the data period 2000-2015 was chosen to start the study by operating in a 5-year time step  (2000, 2005, 2010, 2015). This dataset uses the geophysical relationship between aerosol optical depth (AOD) and PM 2. 5 from NASA MODIS, MISR, SeaWIFS and other satellite instruments to estimate the concentration of PM 2.
5 through a chemical transport model (GEOS-Chem), and uses a GWR for calibration, with an accuracy of up to 81%  [35]. Detailed information on the dataset is given in van Donkelaar et al. [35, 36]. These data have been widely used in PM 2. 5 research  [37, 38]. To verify the reliability of this dataset in across China, this study uses PM 2. 5 concentration data monitored by 1497 ground-based monitoring stations in 2015 released by the Chinese Ministry of Ecology and Environment to compare and validate the PM 2. 5 raster dataset. The monitored annual average PM 2. 5 concentrations at the stations were spatially matched with the estimated PM 2. 5 concentration values through the "extract by points" tool in ArcGIS 10. 2, and the correlation coefficient and root mean square error (RMSE) were calculated as 0.
852 and 10. 57, respectively. Han et al. [37]  used this dataset to assess the accuracy of remotely sensed annual mean PM 2. 5 concentrations at the provincial spatial scale and, in validating the results, showed that the maximum relative error was 4. 9% in 2015. Therefore, minimal information is lost in using the modelled dataset compared with the actual values of regional PM 2. 5 concentrations in China, and it can therefore be reliably used in the spatial analysis of regional PM 2. 5 concentrations in China. Regional PM 2. 5 pollution in China is classified by categorizing PM 2. 5 concentrations into five levels that relate to the annual limit of PM 2. 5 concentration in China's Ambient Air Quality Standard  [39]  and the air quality guideline (AQG) by the World Health Organization  [40] ; specific classification information is shown in Figure  1.
Influencing Factors This study focuses on the spatial heterogeneity in response to natural and anthropogenic factors of PM 2. 5 concentration; therefore, based on the principle of spatial raster data accessibility, factors are selected in terms of natural factors and socioeconomic factors. Different factors are quantified using different datasets. Table  1  presents the factor categories established in this research and their corresponding application datasets. Through statistical analysis of the direction and intensity of the relationship between various factors and PM 2. 5 concentration, the difference in the effects of natural conditions and human activities on PM 2. 5 can be identified  [29] [30] [31]. Topographic, meteorological, vegetation, and natural source factors were selected to express the influence of natural conditions.
Construction and industrial activities resulting from the expansion of urban construction land have become major sources of PM 2. 5  [50] , while the rapid expansion of urban areas has amplified the intensity of the heat-island effect, thereby affecting the transmission and diffusion of PM 2. 5  [51]. In considering all of the variables used in this study, spatial resolution was standardized to 1 km × 1 km so that the data on various influencing factors match well with the PM 2. 5 data, and spatial sampling errors are minimized. The spatial location of all datasets was mediated by grid cells. Specifically, we established 23,614 grids of 20 km × 20 km size at the regional scale across China and PM 2. 5 data, DEM data, NDVI, meteorological data, GDP, POP, and NLI data were resampled to the spatial resolution of the grid cells to extract the corresponding resampled values of the grid cells and to calculate the proportional areas of arable land, forest land, grassland, construction land, and unused land within each grid cell.
MGWR is essentially a multiple linear regression, and the multivariate complexity and correlation of PM 2. 5 concentration influencing factors, so the multiple covariance problem needs to be solved before running MGWR; the test of variance inflation factor (VIF) is the most concise variable screening method  [64]. This study solves the multiple covariance problem by screening out the variables with VIF > 10. The original data were also normalized in order to make the coefficients of the modeling results statistically comparable at different locations and for different variables. During the establishment of the MGWR model, the bandwidth was calibrated using a back-fitting algorithm  [34]. In this paper, all calibration of the model was carried out using MGWR 2. 2 software  [63, 65] , which is suitable for modeling large datasets.
The software is available at https://sgsup. asu. edu/sparc/multiscale-gwr accessed 22 September 2020. In order to verify the practical validity of the MGWR model, a total of 658 matched samples were obtained using the spatial connection between ground-based monitoring stations and the 20 km × 20 km grids. The results of the linear fit between the predicted values of the MGWR model and the monitored PM 2. 5 concentration values at the stations showed that the regression coefficient R 2 of the fit reached 0. 715, which means that the MGWR model implemented in this study has a reliable practical value. From 2000 to 2015, the PM 2. 5 concentrations exhibit substantial spatial variations; there is a clear spatial division in PM 2. 5 distribution across the so-called Hu Huanyong line  [66]  from the southeast inland to the northwest (Figure  2a-d ) revealing clear spatial agglomeration.
Acknowledgments: The authors would like to thank Atmospheric Composition Analysis Group [52] (http://fizz. phys. dal. ca/accessed 22 September 2020), National Earth System Science Data Center, National Science & Technology Infrastructure of China [54] (http://www. geodata. cn/accessed on 30 May 2021), the  Resource and Environmental Science and Data Center of the Chinese Academy of Sciences  [53] (http://www. resdc. cn/accessed 22 September 2020) for providing data support. The national boundary data of China used in this study were provided by  National Geomatics Center of China  (http://www. ngcc. cn/ngccaccessed on 22 September 2020). The authors are grateful to the comments provided by the Editor and three anonymous reviewers, which were used to improve the quality of this work.
The curves in Figure  3a  display the WAXD data on top of the SAXS background, while the curves in Figure  3b  are displayed after subtraction of the SAXS contribution. The shape and position of the (002)-peak was evaluated in detail with the following procedure. Since the peaks are not symmetric, a simple Gaussian fit could not be applied. Therefore, a combination of the Lorentzian and Gaussian functions was used for an asymmetric fit to determine peak position (2θ) and full width at half maximum (FWHM) of the (002)-peak. Details about the function can be found in the Supplementary Information. Figure  3b  displays the fit curves of the organosolv-based samples without the SAXS background. The curves were shifted along the Y-axis to enhance visibility. It is visible that the right flank of the peaks towards higher diffraction angles only slightly changes with increasing temperature, while the peak position shifts and the left flank of the peaks changes, narrowing the peak.
For temperatures higher than applied during thermostabilization, the background at small diffraction angles also increases. This is due to the fact of the increasing small-angle X-ray scattering (SAXS) signal, i. e. , the WAXD profiles are recorded on top of SAXS intensity  [21]. In general, SAXS intensity at high scattering angles follows a power law  [25, 29] , which is also found in porous carbons  [21]. Thus, the background contribution was approximated by the following formula: I(2θ) ~(2θ) -n and subtracted before further data evaluation. The curves in Figure  3a  display the WAXD data on top of the SAXS background, while the curves in Figure  3b  are displayed after subtraction of the SAXS contribution. The shape and position of the (002)-peak was evaluated in detail with the following procedure.