dataesr/scientific-paragraphs-categorization huggingface dataset:
Digital pathology IHC WS were digitized with NanoZoomer 2. 0HT (Hamamatsu®) at ×40 magnification to generate a WS image (WSI) file in ndpi format. All WSI were imported in the open-source QuPath software version 0. 2. 3  [32]. Pathologist manually outlined the tumour areas considered to be the tumour centre (CT). An in-house script then automatically defined an invasive margin (IM) of 10 0 0 μm wide surrounding the tumour. Using QuPath detection scripting, positive cells were taken into account to evaluate the cell densities (cells/mm ²) of CD3 + , CD8 + and CD4 + in CT and IM (supplementary data, Fig. 2  ). A pathologist independently reviewed all analysed images for quality control.
Digital pathology IHC TMA were digitized with NanoZoomer 2. 0HT (Hamamat-su®) at ×40 magnification to generate a TMA image (TMAI) file in ndpi format. All TMAI were imported in the open-source QuPath software version 0. 2. 3  [32]  for tissue arraying and quantification. Using the QuPath detection script, positive cells were included in the CD3 + cell count. For each patient, the mean TMA values of the three TMA spots was used.
HER2 status in pMMR/MSS CRC and clinicopathological characteristics Since dMMR/MSI CRC are largely infiltrated by lymphocytes  [21] [22] [23]  , the immune infiltrate analysis was only performed on a population of 258 patients with no missing data regarding the MMR status. Only selected pMMR/MSS tumours were included in the description of immune infiltrate. Clinicopathological characteristics of patients with pMMR/MSS CRC, HER2 amplified, HER2 2 + , HER2 1 + and HER2 0 IHC scores are shown in Table  2. No differences in patient characteristics were observed between these four groups. Of note, all HER2 amplified CRC were pMMR/MSS in the complete dataset.
In addition, a paradigm shift is currently occurring with the emergence of the "HER2 low" status. This status corresponds to a low level of HER2 expression (IHC2 + /FISH-, IHC1 + ). In CRC, the clinical trial DESTINY-CRC01 (Clinicaltrails. gov; NCT03384940) evaluate the efficacy of a HER2-directed antibody (Trastuzumab) in combination with a DNA topoisomerase I inhibitor conjugate (Deruxtecan), in CRC expressing different levels of HER2 expression (IHC3 + , IHC2 + /FISH + , IHC2 + /FISH-, IHC1 + )  [38]. No patients showed an objective response in the HER2 low cohort, as compared to the 3 + cohort. However, in this setting, only one study is available and trials clinical are ongoing  [48]. Therefore, this particular immunohistochemical pattern still needs to be considered. Regarding breast and gastric carcinoma, this new association showed efficacy, even in tumours with low levels of HER2 expression (IHC2 + /FISH-, IHC1 + )  [39 , 40].
Methods: This multicenter, double-blind, controlled phase 2 study (NCT03840902) evaluated the safety and efficacy of BA with concurrent chemoradiotherapy (cCRT) followed by BA (BA group) versus placebo with cCRT followed by durvalumab (durvalumab group) in patients with unresectable stage III NSCLC. The primary end point was progression-free survival according to Response Evaluation Criteria in Solid Tumors version 1. 1 as assessed by the investigator. On the basis of the recommendation of an independent data monitoring committee, the study was discontinued before the maturity of overall survival data (secondary end point). Results: A total of 153 patients were randomized to either BA (n ¼ 75) or durvalumab groups (n ¼ 78). The median progression-free survival was 12. 8 months versus 14. 6 months (stratified hazard ratio ¼ 1.
6  Consistent with the PACIFIC analysis, results from the PACIFIC-R retrospective real-world study revealed numerically longer median real-world PFS with consolidation durvalumab after cCRT (23. 7 mo) versus sequential chemoradiotherapy (19. 3 mo) and in patients with PD-L1 expression of greater than or equal to 1% versus less than 1% (22. 4 mo versus 15. 6 mo, respectively). 7  However, many patients treated with the current SoC still experience disease progression or recurrence (5-y PFS, 33. 1%; 5-y OS, 42. 9%). 8  Other immune checkpoint inhibitors that have been explored concurrently with cCRT include nivolumab (NICOLAS; NCT02434081), 9 pembrolizumab (NCT02621398),  10  and atezolizumab (NCT02525757)  11  ; however, none has been studied as a maintenance agent after cCRT. Therefore, novel treatment options are needed to address the unmet medical needs of this difficult-to-treat population.
Study Design and Participants This global, multicenter, randomized, double-blind, controlled phase 2 study (NCT03840902) evaluated BA use in patients with unresectable stage III NSCLC according to the International Association for the Study of Lung Cancer Staging Manual in Thoracic Oncology version 8. As per the final protocol (additional details in Supplementary Methods) the study comprised a safety run-in phase (n ¼ 42) that stratified patients according to ethnicity (12 Japanese and 30 non-Japanese patients) and an expansion phase (n ¼ 118) that stratified patients according to chemotherapy regimen and PD-L1 expression. Randomized (1:1) patients received either 1200 mg BA every 2 weeks with cCRT followed by BA every 2 weeks or placebo with cCRT followed by durvalumab (10 mg/kg every 2 wk).
7%) and bleeding events (grade !3, 16. 3%) as AESIs (NCT04066491; D. Y. Oh, unpublished and submitted data). Bleeding events and anemia were considered potential risks at the time of study initiation, given the importance of TGF-b in angiogenesis and vasculogenesis. 24  Ongoing safety surveillance during the study confirmed these risks, and all clinical study documents were updated to include these events as important identified risks, and appropriate risk mitigation measures were added to the study protocol. This ensured appropriate, timely identification, and management of anemia and bleeding events throughout the study. Anemia AEs were observed in 51. 4% of patients treated with BA, which could be related to the high incidence of bleeding. Any-grade bleeding events that occurred in the BA group were epistaxis (17.
The simulated sea ice evaluated in this section is the last 50 years from 100-year "present-day" control runs forced by greenhouse gases and aerosols from the year 2000 (see  Williams et al. , 2017). Simulated sea ice from GC5 is compared against the GC3. 1 CMIP6 configuration  (Williams et al. , 2017; Ridley et al. , 2018) , along with reference datasets including observations of sea ice concentration from the Hadley Centre Sea Ice and Sea Surface Temperature dataset version 2 (HadISST. 2. 2. 0. 0;  Titchner and Rayner, 2014) , observations of sea surface temperature from the ESA CCI SST L4 dataset of  Good et al. (2019) , and sea ice thickness from the Pan Arctic Ice Ocean Modeling and Assimilation System (PIOMAS;  Schweiger et al. , 2011)  reanalysis, which assimilates observations of sea ice concentration but not sea ice thickness.
1 at all times of the year, as illustrated for September and March respectively in Figure  4b,d  and 4c ,e. These are now much closer to the HadISST. 2 dataset. This is because of a considerable improvement in GC5 of Southern Ocean surface temperatures (see Figure 4f,g and  Storkey et al. , in prep) , where previously a warm ocean bias led to low sea ice area  (Ridley et al. , 2018; Rae et al. , 2015). Despite the general increase in Antarctic sea ice cover, as illustrated on Figure  4b ,d there is a minor reduction in the Weddell Sea compared to GC3. 1. This is associated with the emergence of multi-year sensible heat polynyas, a feature common to many climate models  (Heuzé et al. , 2015)  including Met Office configurations  (Megann et al. , 2014; Ridley et al. , 2022).
Setting, participants and study design Data from the Clinical Records Interactive Search (CRIS) system, from the South London and Maudsley NHS Foundation Trust, UK, were used for this retrospective cohort and case-control study. CRIS allows researchers to access anonymized health-care records  (Fernandes et al. , 2013; Stewart et al. , 2009) , registered or imported since 1999, for patients being referred to specialist mental health services in four South London boroughs (Lambeth, Southwark, Lewisham and Croydon) and in national and specialist services also run by the Trust. The South London and Maudsley NHS Foundation Trust provides mental health services for a total local population of 1. 3 million people, offering inpatient as well as community care  (Fernandes et al. , 2013; Rogers et al.
, 2021). CRIS currently contains data on electronic records for over 500,000 subjects  (Rogers et al. , 2021). A cohort of patients suffering from catatonia was identified retrospectively through CRIS as described previously  (Dawkins et al. , 2022; Jeyaventhan et al. , 2022; Rogers et al. , 2021; Yeoh et al. , 2022). In brief, a natural language processing service was used to identify catatonic episodes. Then, records obtained with the natural language algorithm were analyzed manually, to ensure that identified episodes satisfied the inclusion criteria. The following inclusion criteria were established for this study: 1) a diagnosis of catatonia made by a clinician; 2) evidence of at least two features of catatonia according to the Bush-Francis Catatonia Screening Instrument (BFCSI)  (Bush et al.
, 1996a (Bush et al. , , 1996b)) ; 3) available date for the diagnosis of the catatonic episode; and 4) a time interval not longer than 30 days between date of onset of catatonia and the date of inclusion in the health-care records. While the initial data extractions were performed between 2018 and 2021  (Rogers et al. , 2021) , two complementary data extractions were performed on 06/06/2022 and 09/06/2022, not modifying the number of patients included, but adding additional variables for seasonality analyses, such as number of total admissions by month, for all psychiatric disorders in the South London and Maudsley NHS Foundation Trust, from 2007 to 2016, and country of origin for patients included in the study. A control group obtained from CRIS was used in this study including all patients admitted as psychiatric inpatients to the hospitals of the South London and Maudsley NHS Foundation Trust between 01/01/ 2007 and 31/12/2016.
Data on mean temperatures by month in London, from 2007 to 2016, were obtained from London City Airport weather station. The presentation of catatonia across seasons was explored using a cohort study design. The effect of month of birth on the subsequent development of catatonia was explored using both a cohort and a casecontrol study design. All procedures used in this project comply with the ethical standards of National and Institutional Committees on Human Experimentation and follow the Declaration of Helsinki -Ethical principles for medical research involving humans. The use of CRIS system has been approved by the Oxfordshire C Research Ethics Committee (ref: 18/SC/0372) and this project was approved by the CRIS Oversight Committee (ref: 17-102)  (Rogers et al. , 2021).
Data availability Data are owned by a third party, Maudsley Biomedical Research Centre (BRC) Clinical Records Interactive Search (CRIS) tool, which provides access to anonymised data derived from South London and Maudsley electronic medical records. These data can only be accessed by permitted individuals from within a secure firewall (i. e. , the data cannot be sent elsewhere), in the same manner as the authors. For more information please contact: cris. administrator@slam. nhs. uk.
Of the 500,000 UKBB participants, 100,000 are undergoing MRI of the brain, heart and whole body, as well as dual-energy X-ray absorptiometry to measure BMD. As of August 2022, approximately 53,000 participants have been scanned. Efficient measurement of BMFF from these MRI datasets will require development of new automated analysis. methods. Several groups have developed machine learning for automated segmentation of other anatomical regions from the UKBB MRI data  (15) (16) (17). Machine learning has also recently been used to segment the knee or vertebral BM from Dixon images in smaller cohorts outwith the UKBB  (18) (19) (20) ; however, machine learning has not yet been developed for automated segmentation of the BM from other skeletal sites, and never using MR data from the UKBB. These were the goals of the present study.
Manual segmentation of MRI data A training dataset of 75 subjects (Fig. 1A ) was extracted from the test dataset to be used for the training and validation of the deep learning algorithms. Each of these 75 datasets was segmented by a single observer for consistency, generating manual segmentations. For each subject, the fat images were used to define four distinct volumes of interest (VOIs) corresponding to BM regions of pathophysiological relevance: the spine, the femoral head, the total hip, and the femoral diaphysis. The spine consisted of all the vertebral marrow in the principle abdominal volume, which contained 6-7 vertebrae ranging from T8 to L3. The reason for this range of vertebrae is that the multiple abdominal acquisitions have a fixed volume and are continuous across the patient's body; hence, the range of vertebrae within each abdominal volume depends on the patient's height.
0 software (AnalyzeDirect, Overland Park, KS, USA) following an overall inspection of each volume to determine the extent of each region excluding partial volume, defined as a drop in signal intensity > 50% compared to the centre of the region. Of the 75 manually segmented datasets (Fig. 1A ), 64 were used to train the deep learning model for the spine; 61 were used for the femoral head and diaphysis; and 62 were used for the total hip (Fig. 1B ). To do so, the fat images and their corresponding manual segmentations were used iteratively to build a separate model to segment each region individually and generate a deep learning segmentation (Fig. 1D ). The remaining datasets (Fig. 1C ) were not used in training the models but instead were used as unseen validation data to test the models: 12 datasets were used for testing the spine, 11 for the femoral head, and 10 each for the total hip and diaphysis models.
For these validation datasets, comparison of the deep learning segmentations with the manual segmentations (Fig. 1E ) allowed dice coefficients to be calculated for the four different algorithms (Table  2 ). All the deep learning segmentations for the training and validation datasets were manually checked. This identified several data issues and segmentation failures that. required the development of specific error-checking rules. These rules were based on determining if the VOIs generated were physiologically appropriate: VOIs could not consist only of single voxels, nor were gaps allowed within the VOIs. Therefore, the initial error-checking steps automatically removed any single-voxel VOIs and joined together any discontinuous VOIs. Additional error checking was used to identify those segmentations that were outliers within the distribution of regions generated.
U-Net development and training We first used MRI data from 61-64 subjects for manual segmentation of four VOIs: the spine, consisting of lumbar and thoracic vertebrae; the femoral head; total hip; and femoral diaphysis. We then trained separate U-Net models for each VOI and tested their performance on 10-12 subjects in a validation dataset (Fig. 1 ). Figure  2  shows the architecture of our new U-Net, while Table  2  shows the comparison Dice index results between the conventional U-Net and our new U-Net models for each site. Visual comparison of manual vs deep learning segmentations further confirmed the accuracy of the outputs from each of our deep learning models (Fig. 3 ).
Conclusions Our new deep learning models allow accurate segmentation of small VOIs from large volumetric MRI data. While we have used these models to analyse small BM regions, they could also be applied for precise, automated, large-scale analysis of other small anatomical structures of interest. The development and validation of our models using UKBB MRI data is hugely significant because, unlike most other MRI datasets, the UKBB also provides extensive genetic and phenotypic data for each subject, including. whole-genome sequencing and health records. This linked data allows comprehensive association studies to identify the genetic and pathophysiological factors associated with FF and other MRI-derived measurements. Indeed, Liu et al recently demonstrated the power of this approach using deep learning for segmentation of abdominal organs from UKBB MRI data  (16).
Finally, FF maps were generated from each MRI dataset (F) and the deep learning segmentations applied to these to obtain the BMFF for each VOI (G). Figure2-Architecture of our CBAM Attention ROI U-Net for segmenting small structures from large 3D data. Each convolutional block in the U-Net encoding subnetwork (or contracting path) includes one or two CBAM (convolutional block attention module) layers. A fixed-size single channel spatial attention map is generated by each CBAM layer through 1X1X1 convolutions and trilinear interpolation. These attention maps are then combined to produce a probability map of object location with which a ROI is defined. The encoded features of all resolution-levels are then cropped to the ROI and input into the decoder which produces the segmentation results within the detected ROI.
ACKNOWLEDGEMENTS This work was supported by a grant from the  Medical Research Council  ( MR/S010505/1  to W. P. C. ). W. P. C. was further supported by a  Chancellor's Fellowship  from the  University of Edinburgh. C. W. was further supported by the  British Heart Foundation  ( RG/16/10/32375 ). C. G. and T. M. were supported by the  Edinburgh Clinical Research Facility  and  NHS Lothian R&D. We are grateful to  Dominic Job  ( Edinburgh Imaging, University of Edinburgh ) for support with IT infrastructure, and  Jimmy Bell ,  Louise Thomas  and  Brandon Whitcher  ( University of Westminster ) for helpful discussions and advice regarding working with UKBB MRI data.
, 1987) , with P concentration of 2. 5 μM (low-P) and 250 μM (high-P) under continuous aeration. The nutrient solution was changed every three days and the pH was maintained at pH 5. 65. The containers were kept in a growth chamber at 27/20 °C day/night temperatures, light intensity of 330 μmol photons m -2 s -1 and 12-h photoperiod. After 13 days, root images were captured with a digital photography setup and analyzed using RootReader2D (USDA, 2020b) and WinRHIZO software (Regent Instruments Inc. , 2020). The imaging system consists of a Nikon D300s digital SLR Camera with 60mm macro lens that was affixed to a  Arredondo et al. , 2014) , especially in tropical weathered soils  (Novais and Smyth, 1999). P use eficiency in plants can be achieved by mechanisms underlying both P acquisition and P internal utilization efficiency  (Parentoni and Souza-Junior, 2008).
, 2014; Wen et al. , 2019; Bernardino et al. , 2020 Bernardino et al. , , 2021)). Thus, this study aimed to identify and characterize hybrids regarding root traits related to P acquisition efficiency. Figure  1. Digital imaging and quantitative analysis of root system morphology in paper pouch system. Seeds were surface-sterilized and placed in moistened paper rolls. After four days, uniform seedlings were transferred to moistened blotting papers and placed into paper pouches with nutrient solution for 13 days. The captured images were batch converted to an 8-bit grayscale format using a custom written ImageJ plugin (NIH, 2020). The images were then batch thresholded using the RootReader2D software. The thresholded images were then imported into the WinRHIZO software for root trait analysis and quantification.
The images were analyzed using a calibration grid as a reference scale and changing the input settings to pale roots on a black background. copy stand and calibrated to have a fixed focal plane scale of 140 pixels cm -1. The root systems of each plant were spread out in a clear, water filled tray that was illuminated from below and individually imaged. The captured images were batch converted to an 8-bit grayscale format using a custom written ImageJ (NIH, 2020) plugin. The images were then batch thresholded using the RootReader2D software. During thresholding, the pixel intensities were inverted and the dark roots were separated from the illuminated background using a double adaptive thresholding filter. The settings used for the double adaptive filter were 15 and 65 for the minimum and maximum thresholding values, respectively, and kernel sizes of 21x21 pixels with thresholding criteria of 0.
09 percent or more above the local pixel intensity average for both filter passes. The thresholded images were then imported into the WinRHIZO software for root trait analysis and quantification. The images were analyzed using a calibration grid as a reference scale and changing the input settings to pale roots on a black background  (Sousa et al. , 2012). The analyzed traits were root length (RL) (cm), root diameter (RD) (mm), total root surface area (SA) (cm 2 ), root volume (RV) (cm 3 ), the surface area of roots with diameter between 0 and 1mm (SA1), 1 and 2 mm (SA2) and, >2 (SA3) (cm 2 ). The roots and shoots were dried separately at 65 °C to a constant weight to obtain root and shoot dry weight (RDW and SDW, respectively) (g) and root/shoot ratio (RSR).
Statistical analysis Traits obtained under low and high P conditions were subject to individual and joint analysis of variance. The multiple comparison procedure for all genotypes in both P levels were performed according to the  Scott and Knott (1974)  test at 5% probability. Data analysis was performed using the software Genes  (Cruz, 2013). Biplots of the principal component analysis PCA were plotted separately for each P level with the traits evaluated. The mean of each trait was standardized to avoid effects due to scaling differences. Graphical plotting was done with the ggplot2  (Wickham, 2016)  package of the R software version 3. 2. 5 (R Core  Team, 2016). Euclidean distance mean matrix was calculated and used as input data for cluster analysis based on the Unweighted Pair-Group Method of Arithmetic Averages (UPGMA) under each P level.
Prior CH 4 emissions The different methane sources and sinks used as prior information are based on a set of different emission inventories and landsurface models. Natural methane sources include hereby emissions from high-northern latitude wetlands, geological fluxes, CH 4 emissions from the Arctic Ocean, and wildfire events. The CH 4 emissions related to anthropogenic activities include the exploitation and distribution of natural gas and mineral oil, agricultural activities as well as waste management and biofuel burning. Since anthropogenic activities are generally limited in the Arctic and Sub-Arctic, the corresponding datasets have been combined for simplification. As mentioned before, atmospheric CH 4 sinks from free radicals are not taken into account. However, soil oxidation due to microbial activities is included in the form of negative CH 4 emissions.
Acknowledgements. We would like to thank all PIs and supporting staff for deploying, maintaining and making available data from observation sites around the Arctic. In particular, we thank our colleagues from the  V. E. Zuev Institute of Atmospheric Optics (Tomsk). We thank the data infrastructure of ICOS (Integrated Carbon Observing System;. icos-cp. eu), the WDCGG (World Data Center for Greenhouse Gases; https://gaw. kishou. go. jp/) and ObsPack (gml. noaa. gov/ccgg/obspack/) for distributing observations. Sophie Wittig  has been supported by  CEA NUMERICS , funded by the  European Union 's  Horizon 2020 program  under  Marie Sklodowska-Curie Grant  Agreement No  800945.