dataesr/scientific-paragraphs-categorization huggingface dataset:
Response Assessment To evaluate the potential of the biomarkers to assess treatment response, changes between the baseline and follow-up images were analyzed with each imaging biomarker (SUL peak , TLG, PBI and PLI). For this evaluation, baseline data was taken from the automatic segmentation done by the U-Net BL network and follow-up data was taken from the segmentations done by the U-Net FU network. The difference between baseline and follow-up images was measured as: ∆biomarker(%) = (biomarkerFU -biomarkerBL) biomarkerBL × 100 with biomarkerBL, a biomarker taken on the baseline acquisition, and biomarkerFU, the same biomarker taken on the follow-up acquisition. To determine the best biomarker to assess treatment response, a Receiver Operating Characteristic (ROC) curve and its Area Under the Curve (AUC) were computed.
87 respectively. The confusion matrix for each network can be found in the Supplementary Materials (see Supplementary Table  S1. On the follow-up acquisitions, the U-Net FU network trained specifically for followup acquisitions with baseline images as input showed better performances (see Table  1 ) than U-Net BL network trained only on baseline acquisitions. According to the Wilcoxon signed-rank test, the two mean Dice scores were statistically different (p-value ≤ 0. 001). To evaluate our network on unseen data, we had accessed to 10 patients (five from each site) with one baseline and one follow-up acquisition. The results are presented in Table  1. Examples of lesion segmentations performed on both baseline and follow-up acquisitions for several patients from the test dataset are shown in Figure  3.
With only 60 patients and an important lesion heterogeneity in terms of location, size and contrast, we obtained a mean Dice score of 0. 66 on the validation dataset. For comparison, Xu et al. [15]  used a V-Net with two input channels to segment bone specific lesions on whole-body PET/CT from patients with multiple myeloma attained a mean Dice score of 0. 69. In 2020, a challenge for automatic HEad and neCK TumOR (HECKTOR) segmentation in PET/CT was organized jointly with MICCAI, giving access to 201 patients for training and 53 for testing. Eighteen teams submitted their results to the challenge and the best method obtained a mean Dice score of 0. 76 with a U-Net architecture with residual layers supplemented with squeeze and excitation normalization  [17, 44]. Another team tested the original 3D nnU-Net for this challenge and reached a Dice score of 0.
72  [45]. Blanc-Durand et al. [46]  also adopted a nnU-Net-based method to segment diffuse large B-cell lymphoma lesions on PET/CT from 733 patients and computed the total metabolic tumour volume. They obtained a mean Dice score of 0. 73. Moreover, according to  [47] , in the context of head and neck tumor segmentation, the inter-observer Dice score in tumor segmentation only reach 0. 69 on PET/CT, highlighting the challenge of lesion segmentation in one specific location. For metastatic lesions, the segmentation task is even more challenging for experts as tumor lesions can be of various sizes and located in different areas of the body. Concerning the segmentation of follow-up images, our performances were less optimal with a mean Dice score of 0. 58 on the validation dataset. However, results showed the usefulness of longitudinal segmentation as the U-Net FU network with the baseline PET and baseline lesion segmentation as inputs had significantly better results than the U-Net BL network (0.
The evaluation of both networks with unseen data suggested that networks were not overfitted during training as we obtained dice scores between 0. 63 and 0. 77. However, limited data were used for testing as we had access to only 10 unseen patients with one baseline and one follow-up acquisition. Moreover, the test performances were computed using the ensemble model of each network, contrary to the validation performances computed with one model for each cross-validation fold and then averaged. This could explain the superior performances on the test dataset. Overall, this work demonstrates that deep-learning-based segmentation can reach promising performances for metastatic breast cancer on longitudinal PET/CT. Furthermore, with this kind of segmentation imaging biomarkers can be computed and used to assess patients' response to treatment.
We chose to classify patients only as responders vs. non-responders, as we did not have access to enough patients in each PERCIST category. Yet, further analyses on a larger dataset could lead to the development of an automated computation of PERCIST response. Contrary to Hatt et al. [49] , we did not find a statistical difference between ∆SUL peak and ∆TLG. This may be explained by the fact that we used the PERCIST criteria to assess response, which depends on SUL peak , whereas they used histopathologic response. ∆PBI and ∆PLI were never used to assess response to treatment or for prognostic purposes. Our study shows that ∆PBI has potential for treatment response assessment with an AUC of 0. 72 not statistically different from ∆SUL peak and ∆TLG, and a statistical difference between the responders and non-responders groups using the Mann-Whitney U test.
Therefore, PLI may have some prognostic value for OS and PFS prediction. This work has some limitations. Our study was performed on a relatively small dataset of 60 patients, and we did not have access to an external cohort for the validation of the networks. However, patients were recruited across two centers and over 2000 lesions were used to train each network, ensuring generalizable training. Results on the 10 unseen patients suggested that our networks were not overfitted during training. Moreover, all trainings were done using the same 5-fold cross-validation with data balanced among folds in terms of acquisition site and number of follow-up images. To avoid any bias and ensure valid comparison between networks, when validating the U-Net BL network with follow-up acquisitions, we used the model that was not trained with the corresponding baseline acquisition.
Since this prospective study is still ongoing, future included patients could be used to extend our test dataset and improve the validation of our networks. To overcome the lack of labelled data, pretraining with unlabeled data from our dataset but also from other PET/CT datasets could also be implemented as proposed by Alzubaidi et al. [52]. They were able to improve their results by about 10% in different classification scenarios and their work could be adapted to segmentation tasks. To do so, instead of assigning random labels as explored for classification, we could for instance try to pretrain the network to segment zones with SUV values superior to 2. 5 (commonly used threshold indicative of malignancy).
, 2015; Churakova et al. , 2016). In contrast to ring width, tree-ring δ 13 C or δ 18 O stable isotopes have already been incorporated in some global land surface models such as FOREST-BGC  (Panek and Waring, 1997) , ORCHIDEE  (Shi et al. , 2011; Churakova et al. , 2016; Risi et al. , 2016) , JULES  (Bodin et al. , 2013) , LPX-Bern  (Saurer et al. , 2014; Keel et al. , 2016; Keller et al. , 2017) , and CLM4. 5  (Raczka et al. , 2016; Keller et al. , 2017). An enormous benefit of explicitly modeling tree-ring width and stable isotopes in the global land surface models is the ability to directly compare readily available treering data to modeled data. Thus, the global models can be evaluated and improved with freely available worldwide treering data found, for example, in the International Tree-ring Data Bank (ITRDB) and tree-ring laboratories around the world.
Tree-ring data Six previously published tree-ring datasets in northern and western Europe with simultaneous measurements of ring width and δ 13 C and δ 18 O compositions were used to evaluate the skill of the model along a climate gradient (Table  1 ). The three northernmost sites are located in the temperaturelimited boreal region in Finland and have chronologies of ring width and δ 13 C and δ 18 O composition for Scotch pine (Kessi and Sivakkovaara;  Hilasvuori et al. , 2009)  and pedunculate oak (Bromarv;  Hilasvuori and Berninger, 2010). The remaining three sites are located in France and represent moisture-sensitive temperate forests of sessile oak (Fontainebleau;  Etien et al. , 2009)  and pedunculate oak (Rennes and Annecy;  Raffalli-Delerce et al. , 2004; Danis et al. , 2006). In all sites, except Annecy, the available ring width and δ 13 C and δ 18 O chronologies cover the common period 1960-2000.
Simulations The model was run at each tree-ring site over the period 1901-2000 using as meteorological forcing the nearest 0. 5 • grid of the 6-hourly CRU-NCEP dataset  (Viovy, 2014). This gridded forcing dataset is a statistical merging of the monthly CRU TS station-based dataset of the Climate Research Unit  (New et al. , 2000)  with the atmospheric reanalysis from the National Centers for Environmental Prediction (NCEP). The corresponding soil type and vegetation PFT were prescribed for the sites (Table  1 ). Monthly δ 18 O composition of precipitation (δ 18 O p ) and water vapor (δ 18 O v ) was obtained from the nearest grid of a global simulation of the isotope-enabled LMDz general circulation model nudged by an atmospheric reanalysis over the period 1890-2007  (Risi et al. , 2010). Biomass and soil carbon pools were initialized to steadystate equilibrium by a 5000-year spinup obtained by cycling over the meteorology for the period 1901-1910.
75 • × 2. 75 • (dataset available at https://www. climate. unibe. ch, last access: 18 June 2021). This simulation was obtained by forcing LPX-Bern with monthly soil water δ 18 O (δ 18 O sw ), δ 18 O v , and relative humidity data from a simulation with the coupled atmosphere-land surface model ECHAM5-JSBACH driven by observed sea surface temperature  (Haese et al. , 2013; Keel et al. , 2016). LPX-Bern uses the Péclet modified Craig-Gordon model to calculate daily δ 18 O in leaf water together with the formulation of Roden-Lin-Ehleringer  (Roden et al. , 2000)  to determine δ 18 O in tree-ring cellulose.
Model-data comparison The ability of ORCHIDEE to simulate the interannual variability of the three tree-ring parameters over the course of the 20th century was first evaluated in the Fontainebleau forest (Table  1 ). Fontainebleau is a well-studied tree-ring site in France  (Michelot et al. , 2011 (Michelot et al. , , 2012;; Daux et al. , 2018)  and it has been used to evaluate the MAIDENiso tree-ring model  (Danis et al. , 2012). We compared our simulation of ring width, 13 C, and δ 18 O for this site with the simulation of MAIDENiso over the period 1953-2000. Then, the evaluation was conducted for the rest of the sites over the common period 1960-2000, except in Annecy where a shorter span of the observations limited the model-data comparison to the period 1971-2000. A simulated tree-ring width chronology was derived for each site by dividing the simulated tree-ring width series of the largest model tree by its mean.
, 2019). The implication of such a strong stomatal constraint for land-atmosphere interactions in ORCHIDEE r898 is an overestimation of energy partitioning towards sensible heat and an underestimation of transpiration and carbon uptake per unit of leaf area in the temperate region. In the boreal region (Kessi, Sivakkovaara, and Bromarv), where stomatal control is not the main driver of 13 C R  (Shestakova et al. , 2019) , ORCHIDEE simulated a level of correlation between stable isotopes (Fig. 4b ) and sensitivity to VPD (Fig. 4c ) in good agreement with the observations, indicating a better gas exchange parameterization for the boreal broadleaf summergreen and needleleaf evergreen PFTs (Table  1 ). The parameterization of the temperate broadleaf summergreen PFT of the model could be improved by assimilating treering data together with other ecosystem observations (e.
Conclusions We demonstrated the potential of a tree-ring triplet based on tree-ring width and 13 C and δ 18 O stable isotopes to constrain the representation of tree growth and physiology in the global land surface model ORCHIDEE (r898), bridging the long-standing gap between the tree-ring and land surface modeling communities. The comparisons with tree-ring data and the MAIDENiso and LPX-Bern models showed that the parameterization of leaf-level physiological responses (stomatal control) of ORCHIDEE can be mechanistically constrained using the simulated tree-ring isotopic variability, but the representation of tree growth requires improvement. The lack of carbon remobilization from storage in the model is currently a major limitation for a realistic simulation of the interannual coupling between tree physiology and growth and the enduring legacy effects of climate extremes on ecosystem dynamics.
Acknowledgements. This research is part of a post-doctoral grant to  Jonathan Barichivich  from the presidential program " Make Our Planet Great Again " (MOPGA) conducted through the  Centre National de la Recherche Scientifique (CNRS) of France. We thank  Joel Guiot  for sharing the simulations of MAIDENiso for Fontainebleau,  Fortunat Joos , and  Martin Werner  for kindly sharing the isotopic forcing used for their LPX-Bern simulations and  Manuel Gloor  and  Roel Brienen  for discussions on isotopic theory. We also thank  John Marshall  and the two other anonymous reviewers for their constructive comments that improved the paper. Financial support. This research has been supported by the  VER-IFY  grant from the  European Commission ,  Horizon 2020 framework program  (grant no. 776810 ).
Code and data availability. The latest ORCHIDEE model code is archived at https://doi. org/10. 14768/20200228001. 1  (Luyssaert, 2019). All the tree-ring data and ORCHIDEE tree-ring simulations used in this study are available at https://doi. org/10. 5281/zenodo. 4992981 (Barichivich, 2021).
The Data Quality Segment Database (DQSEGDB) software is a database service, backend API, frontend graphical web interface, and client package used by the Laser Interferometer Gravitational-Wave Observatory (LIGO), Virgo, GEO600 and the Kamioka Gravitational wave detector for storing and accessing metadata describing the status of their detectors. The DQSEGDB has been used in the analysis of all published detections of gravitational waves in the advanced detector era. The DQSEGDB currently stores roughly 600 million metadata entries and responds to roughly 600,000 queries per day with an average response time of 0. 223 ms.
The Data Quality Segment Database (DQSEGDB) occupies one critical space in this infrastructure. The Data Analysis (DA) algorithms require information about the state of the IFOs to analyze the observatory data. This requires the definition and distribution of metadata about the observatory data, which we call Data Quality (DQ) flags. A DQ flag is the name given to a set of metadata that describes some portion of the global status of the detector, operation of the instrument, or quality of the data that may impact its analysis. These flags are critical to the data analyses because a category of them mark the times when the IFOs are operating in an optimal state, thereby indicating which observatory data should be analyzed. Additional flags indicate data that should explicitly not be analyzed, such as when hardware injections are ongoing or when electronics faults cause noise in the GW detection channel.
These DQ flags are also called DQ vetoes because they can be used to exclude data from being analyzed  [10]. The set of data associated with each flag name is the list of times when the state of that flag was known and the list of times when that state was active or inactive, which are compliments within the set of known times. The time periods are contained in a data product known as "segments", where a segment is a continuous range of time expressed as a half-open GPS time interval [tstart,tend). Within the GW community, the terms DQ segments and DQ flags are often used interchangeably because of this tight relationship. Each flag has a unique name. The flag names are associated with their IFO identifiers, and are combined in the format [IFO]:[FLAG-NAME].
Initial Detector Databases The DQSEGDB service and client software were built to replace the aging predecessor services that served the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo collaborations separately during their initial observing runs. These previous services were each able to store hundreds of flags and approximately 2 million individual DQ segment and metadata entries by the end of the final science runs of initial LIGO and Virgo. The LIGO service had became very slow, and would often take 10 to 30 minutes to respond to queries made by GW data analyses. This severely restricted the usability of the service and indicated a strong need for a replacement for advanced GW detectors, where the number of flags and number of segments the databases would need to store would grow by factors of hundreds.
LVC Data Landscape and Terminology Each IFO produces one primary data channel, which contains the measurement of the GW strain, and approximately 200,000 channels of auxiliary data that are used to monitor the status of all the hardware and software components used to produce the primary data. This data set constitutes approximately 2 TB per day per IFO. Customized scripts are used to reduce this huge amount of auxiliary data into approximately 1000 DQ flags per IFO. At the location of the IFOs, a set of real-time processes automatically generate segments for a portion of the total DQ flags. These processes encode the metadata in XML files that each contain information about the status of these flags for 16 seconds of data. Each of these XML files is about 78 kB in size, which translates to roughly 420 MB of metadata generated per day per IFO.
Software Design and Architecture In addition to the requirements that the DQSEGDB service be able to respond rapidly while storing a large amount of metadata, several other design requirements and elements of design philosophy were also met when the new software was written. The database was required to contain both the DQ segments and enough additional metadata to allow the tracking of their provenance. The service was required to allow remote clients to connect via command line or web GUI, and was to provide the metadata within 15 minutes of its generation. The API was chosen to provide a RESTful set of URIs with a Resource-Oriented Architecture, compatible with multiple programming languages, and restrictive such that data could not be removed from the database. A JSON format was chosen for the returned data, which included an option for all provenance metadata.
Software Functionalities These design requirements led to the current DQSEGDB software design. The service as a whole is split into three major components. The first is the primary database server, which is generally labelled the DQSEGDB. The second is the client software package, which contains both command line tools and a Python package that can be used to query the database. This set of tools provides many functions as requested by LVC scientists, while also satisfying the design requirements listed. The final component is a graphical web interface to the database, which provides a GUI interface that allows collaboration scientists to rapidly access the metadata without needing to write any code. The DQSEGDB server consists of an Apache layer that calls a custom Python application via the Apache WSGI module.
The Python application uses ODBC to communicate with a MariaDB database instance that hosts a dedicated, normalised database schema that has been designed to optimally host the DQ flags, their associated segments, the associated metadata about those segments, and some overall metadata about the data. The API provides access to all data associated with a given DQ flag through RESTful URIs, formatted as /dq/IFO/FLAG/VERSION. Within this URI, the data can be downselected based on the information and time interval of interest, using options such as /dq/IFO/FLAG/VERSION/active?s=t1&e=t2. This URI will return all active segments for the given FLAG in the GPS interval [t1, t2). The DQSEGDB database service with this design is much faster and more stable than its predecessors. The database currently holds over 600 million DQ segment entries stably.
Impact The new DQSEGDB system of servers has been very successful in meeting the needs of the GW community for storing and distributing IFO metadata since 2014. Thanks to the high performance of the DQSEGDB service, nearly all LVC GW searches are using this centralized source of data quality information. It is, thus, also providing a system for careful control and synchronization of the detector status information used by the LVC searches. The DQSEGDB is also used by many automated IFO monitoring processes and many LVC scientists investigating the performance of the IFOs. In particular, the data analyses that concluded in the detection of all GWs thus far have relied on the DQSEGDB infrastructure  [11, 4, 12, 13, 14]. The impact of the DQ information hosted in the DQSEGDB on GW searches is significant, as illustrated in Figure  1   [10].
The information is used to mitigate systematic noise issues, and thanks to the speed of the DQSEGDB service, the speed of testing of the different choices of DQ flags has been drastically improved. One example of the types of DQ flags used to remove a significant amount of noise was the "RF45 flag". This flag indicated times when issues with the electronics that controlled the radio frequency (RF) sidebands used to sense and control LIGOs optical cavities would contaminate the main detection channel with noise that resulted in a significant number of false triggers in DA pipelines  [10]. The latency from the time data is collected at the IFO sites to the moment the metadata may be queried by rapid analyses has also been reduced to less than 5 minutes. This functionality is being used by several "medium latency" analyses that are automatically started in response to external events such as observations of gamma-ray bursts.
With the DQ applied, the background is reduced to below an SNR of 12. 5. The upper limit of this background is closely tied to the limit at which GW events may confidently be detected. The SNR of the GW event GW151226 is indicated on the figure to demonstrate that this detection would have been missed without the use of the DQ data. Image License https://creativecommons. org/licenses/by/3. 0/, image was cropped from original. The new DQSEGDB service has allowed new collaboration tools that automatically, and very frequently, query the DQ flag metadata to be developed. These services provide many different benefits to the large, 1000+ person LVC. In particular, the Summary Page web infrastructure  [15]  makes heavy use of this ability. One open-access example of these pages is available at https://www.
gw-openscience. org/detector_status/day/20200207/. Many of the plots indicate the state of the interferometers, and all of this metadata is being retrieved from the DQSEGDB. These plots are updated on a rolling basis, requiring very frequent queries to the DQSEGDB service that the old service would not have been able to handle. These pages are used by IFO commissioners, data quality investigators, data analysts and the wider astronomical community to easily assess the state of the interferometers and rapidly investigate systematic issues. They have proven invaluable to data and event validation efforts in addition to daily IFO and collaboration operations. Finally, due to the speed and reliability of the service, additional GW detectors have also begun using this single instance of the DQSEGDB.