dataesr/scientific-paragraphs-categorization huggingface dataset:
Correlation of GC subtypes with clinical characteristics We then investigated the associations between our metabolism-associated classification and other classification systems of GC. The comparison was performed separately for TCGA and GSE15459 cohorts (Figures  4A, B  and Tables  S3, S4 ). and TCGA subtype (Chi-square, P < 0. 001), Lauren subtype (Chi-square, P < 0. 001), and WHO subtype (Chi-square, P < 0. 001). On the other hand, pStage, age, and sex were not correlated with subtype in either of the datasets. Notably, in the GSE15459 set, C2 and C3 highly mirrored the metabolic and invasive type by Lei's classification. Such overlap is intriguing because the transcriptomic dataset and the clustering method were different. This finding suggested that metabolic transcripts could be the most informative in classifying gastric tumors.
Correlation of HER2 and metabolismrelated genes HER2 overexpression is recognized as a frequent molecular abnormality drives gastric cancer, and it has been solidly correlated to poor disease outcomes  (39, 40). Therefore, we investigated the HER2 expression with metabolism. Possibly owning to racial homogeneity, the expression of HER2 in cohort GSE84426 and GSE84433 has MAD less than 0. 2, in order to be consistent with the data processing criteria, we performed the analyses of HER2 just with TCGA and GSE15459 datasets. First, we explored the expression of HER2 in the metaclusters. it was lower in C3 (Figure  7A ). Next, we explored the correlation between the expression of HER2 and the enrichment of the metabolism pathways. We did not observe a pathway with correlation of opposite directions with HER2 among subgroups, however, the level of correlation is different across metaclusters (Figure  7C ).
Second, based on an anti-PD-1 immunotherapy-treated metastatic gastric cancer cohort  (35) , we investigated the response of the patients of a subtype to PD-1 treatment. As a result, C4 manifested a high likelihood of responding to PD-1 therapy, whereas the other subtypes did not (Figure  8B ). In addition, we analyzed the sensitivity of subtypes of drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. Among them, 270 drugs exhibited significantly different responses between the subtypes, and the top 12 drugs according to the p value (Table  S7 ), indicating different responses, were plotted. C3 had the highest sensitivity of these drugs (Figure  8C ).
8 8 FIGURE 8 Drug sensitivity analysis. (A) Summarized result of TIDE analysis; the width of a cluster corresponds to the number of patients in that subtype; number represent the percentage of predicted responders/non-responders for each subtype. (B) SubMap analysis based on immunotherapy treated metastatic gastric cancer cohort, C4 shows high sensitivity. (C) Drug sensitivity based on Genomics of Drug Sensitivity in Cancer database, the top 12 drugs with the most significant p value were illustrated.
2. Input/output (I/ O) management, except images, is done through a multifunction data acquisition systems DAQ (USB 6008, The computer used for data acquisition and control is located next to the centrifuge. It is remotely operated with a keyboard, video and mouse (KVM) extender (Link X50, Adder, UK). The receiver of the KVM is located in the room situated next to the centrifuge. Rotation speed of the Sorvall centrifuge is set either by a potentiometer, encoding speed by adjusting resistance (model RC5B) or with 5-position encoders using Gray code to input speed set point value (model RC5C+). Our approach consists in bypassing these controls by providing a computer-controlled set point value to the Sorvall command internal bus. This is done by the digital I/O which controls a universal card 16 relays (K6714-16, Velleman, Belgium) providing either a variable resistance (model RC5B) or the actual Gray code required by the machine (model RC5C+).
Image acquisition and processing In order to read the air-water menisci positions computationally, a monochrome camera (Scout scA640-120gm, Basler, Germany) has been used and fitted with a Cmount lens (HF16 HA-1B, Fujinon, Japan) that enables to have an adjustable field of view of about 7 × 5 mm. This camera is interfaced with a Software Development Kit (Pylon 3. 0 SDK, Basler, Germany) that outputs a buffer of vectors of 8 bits integer corresponding to grey levels of a 659 × 494 pixel matrix. From this matrix, images are reconstructed for display or analysis. In order to avoid delay in image analysis and display, only the last image of the buffer is read when the processor is available; the rest of the buffer is discarded. This image is time-coded with an independent counter with millisecond precision to process meniscus velocity and can be saved to disk with very limited and controlled time delay.
Therefore, if the increase in temperature is quite low and gradual (as it is usually the case for standard measurement), and air temperature can be used directly as a proxy of stem temperature, if the equilibration time is long enough. A rough estimate would be to allow 3 min for a change of 1 °C. If more drastic changes in temperature are observed, a first-order rate reaction model using a 5-min running mean air temperature as the input can be used; for detail, see  Wang et al. (2014b). Air temperature inside the centrifuge tank is monitored with a class A PT100 (D00538, Correge, France) connected to a converter (LKM 104/1, LKM electronic, Germany). Data is logged through the Multifunction DAQ analogue input (USB 6008, National Instruments, USA).
2014 ) and resulted in 2-bit volumic images with a 3. 02-μm 3 voxel resolution. The theoretical hydraulic conductance (k h ) used for the reference VC curve was calculated from a transverse cross-section of each micro-CT scans following the protocol described in  Choat (2016)  and  Lamarque (2020). In short, the individual area and diameter of both air-and water-filled vessels were measured using the ImageJ software  (Schneider et al. , 2012)  The maximum theoretical hydraulic conductance (k max , m-4 MPa -1 s -1 ) was calculated as the sum of the conductance of each water-filled vessels conductance. k h ¼ X πD 4 128η ð7Þ where D is the diameter of vessels (m), and η is the water viscosity (1. 002 10 -3 Pa s at 20 °C). The actual per cent loss of theoretical hydraulic conductance for each sample was then calculated as PLC = 100 × (1k h / k max ).
Vulnerability curves for both CAVI1000 and micro-CT measurements were fitted using the NLIN procedure in SAS 9. 4 (SAS, Cary, NC, USA) based on the following equation  (Pammenter and Willigen, 1998) : PLC ¼ 100= 1 þ exp S 25 P-P 50 ð Þ ð8Þ where P 50 (MPa) is the xylem pressure inducing 50% loss of hydraulic conductivity, and S (% MPa-1) is the slope of the vulnerability curve at the inflexion point. The model was fitted on each vulnerability curve to obtain a P 50 and slope value for each individual. Those values were then averaged for each method, and the curve reconstructed from these averaged values. Details of the sample size and number of replicates for each measurement can be found in Table  2.
Acknowledgements All measurements have been performed at the PHENOBOIS platform, except Micro-Computed Tomography scans that have been performed at the  PSIC HE beamline (SOLEIL Synchrotron facility , project  20150954 ). The authors would like to thank  Henri Inchauspe  for his contribution to the camera module of the software;  Anne-Isabelle Gravel ,  Cédric Lemaire  and  Melvin Tyree  for many fruitful discussions about the cavitron technique;  Stephane Ygorra  and  Luc Lasne  for their advices on image vision; and  Romain Souchal  for the help in setting the rotation speed control on Sorvall centrifuge. A. Techene  ( SAM precis ) and  G. Dumercq  ( DG Meca ) are also acknowledged for providing amazing rotors.
Methods We developed an open-cohort micro-simulation model of OPC natural history among contemporary and future birth cohorts of men, accounting for sexual behaviors, population growth, aging, and herd immunity. We used data from nationally representative databases, cancer registries from all 50 states, large clinical trials, and literature. We evaluated the status quo scenario (the current HPV vaccination uptake remained stable) and alternative scenarios of improvements in uptake rates in adolescents (aged 9-17 years) and young adults (aged 18-26 years) by 2025 to achieve and maintain the 80% goal. The primary outcome was to project OPC incidence and burden from 2009 to 2100. We also assessed the impact of disruption in HPV vaccine uptake during the COVID-19 pandemic. Findings OPC incidence is projected to rise until the mid-2030s, reaching the age-standardized incidence rate of 9¢8 (95% uncertainty interval [UI] 9¢5-10¢1) per 100 000 men, with the peak annual burden of 23 850 (UI, 23 200-24 500) cases.
The model was parameterized using data from the National Health and Nutrition Examination Survey (NHANES), HPV infection in Men Study (HIM study), and other published studies. The unobservable progression probabilities were calibrated to reproduce the observed squamous cell OPC incidence trends and the annual number of cases estimated using the National Program of Cancer Registries (NPCR) and Surveillance Epidemiology and End Results (SEER) data (data providing 100% population coverage for all 50 states and the District of Columbia [DC]). We used data from the census bureau of the USA to assign population age distribution, new births, life expectancies, and obtain a projected population of men through 2100. 18, 19 6] [7]  The model also considered HPV vaccination coverage and herd protection through female vaccination.
HPV vaccine efficacy and coverage For base case analysis, we assumed that HPV vaccination results in a 90% reduction in the acquisition of vaccine-type oral HPV infections among vaccinated men (Appendix 5). 5  We modeled herd effects, assuming a linear inverse relationship between HPV acquisition and female vaccine coverage. We considered age-mixing patterns to calculate age-specific herd protection through female vaccination (Appendix 6). 23 We reproduce the current national HPV vaccination coverage for adolescents (9-17 years) and young adults (18-26 years) estimated using the NHANES data and validated against the National Immunization Survey-Teen (NIS-Teen) and National Health Information Survey (NHIS) data (Appendix Figures  11 and12). Simulation scenarios: status quo, healthy people goal of 80% coverage achieved by 2025, optimistic scenario, impact of the COVID-19 pandemic The status quo scenario assumed that the uptake rates in 2019 would continue.
unforeseen demographic changes may affect future OPC patterns. Second, our model captures herd immunity benefits using a systematic approach that considers female vaccination coverage. We validated our findings by comparing them with other transmission-based simulation models and oral HPV infection prevalence trends among unvaccinated men in the USA. However, future modeling advancements can consider HPV transmission dynamics. 6] [7]  Further evidence from an ongoing randomized controlled trial (NCT04199689) is needed. Fourth, due to data unavailability, we could not evaluate the impact of the COVID-19 pandemic on changes in sexual behaviors among the sexually active young adult population. Therefore, we limited the COVID-19 scenario evaluation to only specific birth cohorts. For the COVID-19 scenario, we assumed that the HPV vaccination uptake is unlikely among adolescents who transition to the young adult age groups given the low historic uptake among young adults.
Wildfires are considered as one of the most disturbing factors in forest areas and high-density vegetation regions. The mapping of wildfires is particularly important for fire prediction and burned biomass estimation. Therefore, accurate and timely mapping of burned areas is of great importance and has a key role in disaster management. Estimation of burned areas from multi-spectral datasets is challenging, because of the complexity of the background and the different reflections of wildfires on the targets. To this end, this study presents a novel burned-area mapping framework based on fusion of multi-temporal Sentinel-1 coherence imagery and post-event Sentinel-2 imagery. The proposed framework uses hybrid quadratic morphological (QM) operations and convolution layers for deep feature extraction.
The proposed architecture is known as the QMDNN-Net, where the overall framework of QMDNN-Net is based on a deep Siamese network. QMDNN-Net has double streams for extracting deep features from multi-temporal coherence data and from Sentinel-2 imagery. The streams are similar to each other, and have the same number of group-dilated convolution blocks and QM layers. QMDNN-Net is defined based on quadratic dilation and erosion, and then it takes the average of these as output. The results of wildfire mapping with QMDNN-Net are evaluated here with two real-world wildfire datasets based on Sentinel-1 and Sentinel-2 imagery. The results show that QMDNN-Net achieves an overall accuracy and Kappa coefficient of 95. 5% and 0. 9, respectively, and outperforms other state-of-the-art methods.
Many studies have included WFBM, such as for greenhouse-gas estimation from wildfires, burned-biomass estimation, wildfire damage assessment and active wildfire detection  [17]. With the increasing availability of remote-sensing imagery, WFBM has become a hot research topic, with many studies carried out to date (e. g. ,  [18] ). Burned-area mapping based on remote-sensing imagery can be applied to three main scenarios here: (1) bi-temporal post/pre-event datasets; (2) post-event datasets; and (3) fusion of heterogeneous datasets  [19] [20] [21]. WFBM based on bi-temporal datasets uses the change detection framework, with the similarities of pixels in the first and second datasets measured, and the generation of the WFBM based on a similarity value  [16]. WFBM based on only post-events focuses on the difference in the spectral signatures of the pixels in the scene to detect 'burned' pixels.
The fusion scenario combines different types of remote-sensing imagery to detect burned pixels, which can then be applied based on combining previous scenarios  [22]. wildfire datasets. For instance, Hardtke, et al. [23]  designed a semi-automatic burned-area mapping framework in semiarid ecosystem regions using time-series moderate resolution imaging spectroradiometer (MODIS) imagery. This framework was applied in three main steps: (1) generation of the time-series normalised burned ratio (NBR); (2) detection of potentially burned pixels using change detection by image differencing between time (n) th and (n-1) th ; and (3) segmentation in a region-growing manner, then visualisation and vectorisation of the detected wildfire areas. Hawbaker, et al. [24]  proposed burned-area detection to produce the 'Landsat burned-area essential climate variable product' using dense time-series data.
They applied the algorithm using a gradient-boosted regression model to estimate the probability of burned areas in scenes from Landsat data. Next, the maximum burned probability value was generated by compositing the burned probabilities with the time-series dataset. The final binary burned map was generated using the segmentation process and thresholding. Jakimow, et al. [25]  presented a fire-detection and tillage-events framework based on dense time-series Landsat-7 and Landsat-8 datasets. This procedure was applied in four main steps: (1) extraction of three clear Landsat observation sequences (at the acquisition date, and before and after the acquisition date); (2) model parameterisation and classification based on a random forest algorithm; (3) aggregation of random forest class probabilities for each year, to estimate the class score; and (4) derived annual layers summarised as single annual maps that show the burned and tilled areas.
Giglio, et al. [26]  proposed a burned-area mapping framework based on a daily time series MODIS dataset. This method was applied in a series of steps:  (1)  extraction of daily surface reflectance and active fire masks; (2) generation of change/fire-related composites; (3) extraction of temporal texture, which uses the local standard deviation; (4) selection of training sample based on a cumulative active-fire composite;  (5)  extraction of conditional probability densities for burned and unburned sample data using a Gaussian kernel estimator;  (6)  separability test applied for burned and unburned conditional probability distributions, to ensure they differ considerably;  (7)  prior probabilities that establish spatially explicit prior burned probabilities;  (8)  posterior burned probability used to give the observed change in a vegetation index; (9) initial classification to generate the experimental classification of burned or unburned; and (10) final classification based on additional contextual information and a majority filter.
Pinto, et al. [29]  proposed a deep-learning-based burned-area detection method based on the visible infrared imaging radiometer suite (VIIRS) dataset. The proposed framework was based on U-Net architecture that used the three-dimensional convolution layers and long short-term memory in the encoder parts. Furthermore, the transpose convolution and temporal convolution layers were used in the decoder part. Liu, et al. [30]  presented a burned-area detection model based on time series Landsat data and harmonic model fitting. This method was initially applied to the 'Breaks for the Additive Season and Trend' monitor for segmentation of stable periods. Then, the harmonic model was fitted for a stable period using the time series burned-area index. Burned-area detection was achieved by comparing the predicted value by the model and the observed value based on a threshold.
Seydi, et al. [16]  presented a burned-area mapping framework based on bitemporal Sentinel-2 imagery using Google Earth engine cloud computing. This framework was applied in three stages: (1) extraction of spectral and spatial features for the bi-temporal dataset; (2) change detection based on image differencing; and (3) burned-area detection based on binary classification using a random forest algorithm. Llorens, et al. [31]  proposed a burned-area detection method using Sentinel-2 imagery based on detection of changes by NBR indexing and thresholding. The threshold values were defined by the European Forest Fire Information Service algorithm. Furthermore, this method can generate the burned severity level according to the separability index.
Moreover, Brand and Manandhar  [35]  used a semantic segmentation-based method for burned-area mapping with mono-temporal Sentinel-2 imagery. The U-Net-based segmentation deep learning framework has also been used for burned-area mapping. The performance of the proposed method was tested in different areas, and the accuracy assessment showed that the proposed method had high generalization despite highly diverse objects. In addition, Sertel and Alganci  [36]  evaluated the performance of a pixel-based and object-based burned-area detection method using the SPOT-6 dataset. They used the spectral bands and NDVI for classification of burned areas. A rule-based classifier was used to classify the segments, and a maximum likelihood classifier was used for pixel-based classification. The results of this classification showed that object-based classification has high-efficiency compared to pixel-level classification.
Ngadze, et al. [37]  evaluated the contribution of spectral bands on the performance of burned-area mapping using a random forest classifier. They used post-fire Sentinel-2 and Landsat-8 satellite imagery to map burned areas. The outcome of this burned-area mapping was illustrated using four spectral bands (i. e. , blue, red, red-edge, near-infrared) and had a high contribution in the of mapping burned areas. Moreover, Dragozi, et al. [38]  investigated the impact of spectral/spatial features on burned-area mapping using a very high resolution IKONOS dataset. They conducted the experiments in two steps: pixel-level classification, and object-level classification. They used the 'fuzzy complementary criterion' to select the features, and a support vector machine algorithm as a classifier. The results of this mapping showed that object-level classification has greater accuracy than pixel-level classification.
C. Fusion-Based Wildfire Burned Mapping Fusion-based WFBM focuses on multimodal datasets, and takes advantage of the different datasets to provide high efficiency. For example, Zhang, et al. [42]  proposed a deeplearning-based framework for mapping burned areas using the synergy of the Sentinel-1 and Sentinel-2 imagery. The proposed deep-learning-based framework was based on Siamese self-attention architecture that uses a self-attention module and a convolution layer. For mapping burned areas, this method uses bi-temporal Sentinel-2 imagery, time-series coherence and backscatter (γ0), and bi-temporal NDVI. Additionally, Boschetti, et al. [22]  fused MODIS and Landsat ETM+ to map burned areas on a large scale, with a spatial resolution of 30 m. This method was applied in three main steps: (1) detection of high potentially burned pixels based on decision-tree-based semantic change detection, by examination of a spectral category time series using Landsat imagery; (2) generation of candidate burned objects based on segmentation of detected high potential pixels using temporal and spatial proximity criteria; and (3) making a decision on candidate burned objects by examination of the contemporaneous 1 km MODIS active fire detection.
Moreover, Alonso-Canas and Chuvieco  [43]  developed a two-step global burned-area mapping method using time series of the 'medium resolution imaging spectrometer' data and thermal information derived from the MODIS hotspot product. This method used cumulative distribution functions to separate burned pixels. In the next step, a contextual criterion was used to enhance the spatial detection of the burned patches from the seed pixels. Furthermore, Roy, et al. [44]  designed a burned-area mapping algorithm based on combining the Landsat-8 and Sentinel-2 datasets. The fusion process was applied by random forest change regression that was trained with synthetic data built from laboratory and field spectra, and used a spectral model of fire effects on reflectance. The random forest algorithm was applied to each pixel on a temporal rolling basis considering 3 months of sensor data, to map the central month.
A temporal consistency check was used to minimise commission errors derived from non-target changes. Furthermore, a regiongrowing technique was used to handle omission errors. In addition, Belenguer-Plomer, et al. [45]  designed a detectionbased framework to map burned areas using Sentinel-1 and hotspots originating from MODIS and VIIRS sensors. This framework used the Reed-Xiaoli detector algorithm to detect anomalous changes in the backscatter coefficient dataset. These changes were overlaid with hotspots, which helped to enhance the burned-area mapping. Furthermore, a random forest classifier was used for some areas where ancillary data was not available. Also, a land-cover map was used to improve the performance of the Reed-Xiaoli algorithm. Crowley, et al. [46]  presented a wildfire progression system based on multi-sensor datasets (i.