dataesr/scientific-paragraphs-categorization huggingface dataset:
2% concordant (Figure  1C ; nuclei, 2,349; total cells, 2,260), validating technical cell segmentation and enumeration approaches. ACIs from corresponding organoid sections (PATXO118) were detected for α-SMA, CK-19, and annexin A5 (Figure  1D ). Likewise, detected nuclei and total enumerated cells analyzed in 11 organoids from PATO044 (Figure  1E ) yielded excellent concordance (97. 0%) (Figure  1F ; nuclei, 2,162; total cells, 2,098) and detected ACIs for α-SMA, CK-19, and annexin A5 (Figure  1G ). for average cytoplasmic intensities (ACIs). Representative PDXO imaging videos of thick 3D organoid sections are provided in Supplemental Video 1. 3D reconstructions generated from Z-stacks, including individual fluorophore wavelength channels and cytoplasmic algorithms, were then analyzed to quantify the expression of each protein marker using Imaris software.
Supplemental Video 3 shows antibody penetration and fluorescence signals throughout the PDO. Collectively, these data indicate that cellular elements of PDAC organoids can be enumerated and that cellular markers inclusive of CK-19, α-SMA, Ki-67, and annexin A5 can be reliably detected using cytoplasmic and nuclear flow that spanned fewer than 14 days to screen drug efficacy ex vivo. Organoids were immediately generated upon acquisition of tumor tissue (day 0), allowed to enter a growth phase (days 0-2), and treated with standard-of-care drug regimens (days 3-5) to ultimately measure drug efficacy (days 7-14) (Supplemental Figure  4 ). Serial IF imaging analysis was performed within 14 days on a caseby-case basis relative to untreated controls. We initially validated organoid viability following drug treatments using ex vivo ODSA colorimetric readout assays and further evaluated the matched equivalence of ODSA readouts with annexin A5 levels, as measured by 3D fluorescence microscopy.
Similarly, when analyzing PDOs treated with FFX, we noticed objective response inflections in proportions of responder/intermediate responder models when transitioning from 30% to 40% and from 10% to 20% thresholds (Supplemental Figure  6E, bottom ). As such, 40% and 10% thresholds in the FFX treatment matrix represented outliers, with skewed readouts toward resistant and responder models, respectively. We therefore interpreted 20% or 30% viability reduction thresholds in the FFX group, representing a distinct cluster of balanced responder/intermediate/ nonresponder models bracketed by obvious response inflections, to represent objective response thresholds. Because overlap of objective response thresholds between GEM/PAC and FFX data sets occurred only with a 30% viability reduction threshold, and PDO responses using a 30% viability reduction threshold universally correlated with patient tumor responses, this threshold was tentatively identified as optimal for the ODSA.
Antibodies for IF and Western blot are listed in Supplemental Table  6. All organoid culturing was maintained in OGM at 37°C in a 5% CO 2 environment unless stated otherwise, as instructed by the vendors. All imaging analysis and ODSA on PDOs in this study was completed in passage 2 unless specified otherwise. Protocols for 3D confocal microscopy and Imaris software analysis. Organoids grew directly in 8-well chamber slides (Ibidi; catalog 80826) for 3 days and were exposed to GEM at different doses, as shown in the Supplemental Table  3. Organoid IF staining in the chambers was performed after 4 days of treatment. Organoid 3D serial images were captured using an Andor Revolution XDi WD Spinning Disk Confocal microscope (Olympus America) in the MD Anderson Flow Cytometry and Cellular Imaging facility.
This microscope supports simultaneous multichannel imaging and a wide information bandwidth. Available excitation wavelengths used in the study included 405, 488, 594, and 647 nm. Acquired serial confocal Z-stacks of 5 μm were captured and reconstructed into 3D in multiple organoids each for control and treatment conditions and analyzed using the Imaris Bitplane 9. 2 software program (Oxford Instruments). Average nuclei or cytoplasmic intensities were calculated though all organoid nuclei or cell numbers. 3D organoids were chosen at depth ranges between 40 and 120 μm. 3D and IF image quantification algorithms. Imaris Arena provided sets of features for visualization and analysis of multiple channels of IF data sets for static 3D organoid images. Cytoplasmic and nuclear algorithms for image processing, segmentation, classification, and reporting were developed following the manufacturer's instructions.
TMAs were scanned and quantified using Visiopharm software. reflective of underlying cancer biology. As such, we do not believe that null findings with respect to α-SMA/CK-19 ratios observed in FFPE PDAC tumors detract from the potential prognostic ability of α-SMA/CK-19 ratios in disaggregation assays/PDO formation. Cells that comprise PDOs may exhibit varying levels of CK-19 and/or α-SMA during ex vivo culturing or passaging (Supplemental Figure  3 ). So far no reports have explained this phenomenon, which could explain why stromal subtype analysis is needed to determine antitumor efficacy in an in vitro model  (35). This phenomenon suggests that optimization of PDO culture is crucial, especially for PDOs from patients who have received neoadjuvant therapy, to prevent PDOs from losing cells or stopping growth during passages.
All of these agents' stock solutions were prepared following the vendor's instructions. The values of each well readout were plotted using the Prism 9. 0 software program (GraphPad Software). Statistics. All quantified data were plotted and analyzed using Prism 9. 0. Significance was determined using the Student's unpaired 2 tailed t test, 1-way ANOVA, and exact McNemar's test. Data are representative of at least 3 independent experiments and reported as mean ± SEM, unless otherwise indicated. P values of less than 0. 05 were considered significant. Differences in patient survival based on α-SMA/CK-19 ratios were tested using the Kaplan-Meier log-rank test. Study approval. The PDAC PDX protocol was approved by the MD Anderson Institutional Review Board (no. LAB07-0854). Acquisition of PDAC specimens from FNA biopsies was approved by the MD Anderson Institutional Review Board (no.
Acknowledgments We thank  Don Norwood  and  Erica Goodoff  from the  Research Medical Library at The University of Texas MD Anderson Cancer Center  for editorial assistance. This work was supported by the  NIH/National Cancer Institute  ( K08CA218690  to MPK),  NIH  ( P01 CA117969  to RAD), and  NIH  ( R50 CA243707-01A1  to JKB). This work was supported in part by the  NIH  through the  University of Texas PDX Development and Trial Center Grant  ( U54CA224065 ). This work was also partially funded by the  Skip Viragh Foundation , the  Bettie Willerson Driver Cancer Research Fund , and various pancreatic cancer research donors. We acknowledge the  Cancer   Center Support Grant for the Flow Cytometry and Cellular Imaging Core Facility  ( P30CA16672 ). Address correspondence to: Ya'an Kang or Michael P.
However, a prospectively collected database was used, which allows for accurate capture of all pertinent data used for analysis. Second, this is a single-surgeon series and thus suffers from certain operator-dependent biases such diagnosis at the time of surgery. However, all radiological assessments were repeated using a validated tool and clearly identified cam and/or pincer to be present in all hips; hence, all cases satisfied diagnostic radiological criteria. Additionally, the type of procedure performed was similarly determined by the surgeon. Finally, comprehensive PROM data were only captured in 56% of patients, which may lead to selection biases of reported results. In conclusion, regardless of whether open or arthroscopic surgical technique is used, FAI surgery provides a lasting improvement in functional scores and an acceptable joint preservation rate of 90.
Similarly to approximation schemes, we argue that the actual challenge is to design an algorithm providing both performances and strong guarantees. However, conversely to most of these schemes, we also consider operational constraints to provide a practical, high-performance implementation. In this work, we leverage inherent limitations in the accuracy of delay measurements and account for the operational constraint added by SR to design a new algorithm, BEST2COP, providing guarantees and performance in all cases. Our proposal efficiently deals with the complexity of DCLC in SR domains (DCLC-SR) thanks to simple but efficient data structures and amortized procedures specifically tailored to deal with the three metrics (delay, IGP cost, and the number of segments). We show that BEST2COP outperforms a state-of-the-art algorithm on both random and real networks of up to 1000 nodes.
BEST2COP(E): Efficient Data Structures and Algorithms for Massive Scale Networks This section presents our contributions. We introduce and define preliminary notations and concepts used to design BEST2COP, before describing the data structures used by our algorithm. In section 3. 3, we describe our algorithm, BEST2COP, and show how we extend it for massive scale networks divided in several areas in section 3. 4. We have shown that SR seems indeed appropriate (as desired) for fine-grained delay-based TE. We thus aim to solve DCLC in the context of an ISP deploying SR, leading to the DCLC-SR problem that considers the IGP cost, the propagation delay, and the number of segments. For readability purposes, we denote: •  0 the metric referring to the number of segments, with the constraint  0 = MSD applied to it; •  1 the delay metric, with a constraint  1 ; •  2 the IGP metric being optimized.
Routers 11 The detailed complexity is given in section 3. 5. 1 within an area maintain a comprehensive topological database of their own area only. Stub-areas are centered around the backbone, or area 0. Area Border Routers, or ABRs, possess an interface in both the backbone area and a stub area. Being at the intersection of two areas, they are in charge of sending a summary of the topological database (the best distance to each node) of one area to the other. There are usually at least two ABRs between two areas. We here (and in the evaluation) consider two ABRs, but the computations performed can be easily extended to manage more ABRs. Summaries of a non-backbone area are sent through the backbone. Upon reception, ABRs inject the summary within their own area. In the end, all routers possess a detailed topological database of their own area and the best distances towards destinations outside of their own area.
Massive Scale Topology Generation To the best of our knowledge, there are no massive scale topologies made publicly available which exhibit IGP costs, delays, and area subdivision. For example, the graphs available in the topology zoo (or sndlib) datasets do not exceed 700 nodes in general. Moreover, the ones for which the two metrics can be extracted, or at least inferred, are limited to less than 100 nodes. Thus, at first glance, performing a practical massive-scale performance evaluation of BEST2COPE is highly challenging if not impossible. There exist a few topology generators  [60, 54]  able to generate networks of arbitrary size with realistic networking patterns, but specific requirements must be met to generate topologies onto which BEST2COPE can be evaluated, in particular the need for two metrics and the area decomposition.
Topology generation requirements. First, the experimental topologies must be large, typically between 10 000 and 100 000 nodes. Second, they must possess two valuation functions as realistic as possible, one for the IGP cost and the other modeling the delay. Third, since the specific patterns exhibited by real networks impact the complexity of TE-related problems, the generated topologies must possess realistic structures (e. g. , with respect to redundancy in the face of failures in particular). Finally, for our purposes, the topology must be composed of different areas centered around a core backbone, typically with two ABRs between each to avoid single point of failure. Since we do not know any generator addressing such requirements, we developed YARGG (Yet Another Realistic Graph Generator), a python tool(Code available online 14 ) which allows one to evaluate its algorithm on massive-scale realistic IP networks.
Conclusion While the overhead of MPLS-based solutions lead to a TE winter in the past decade, Segment Routing marked its rebirth. In particular, SR enables the deployment of a practical solution to the well-known DCLC problem. In this paper, we proposed a multi-metric SR construct onto which our algorithm, BEST2COP, iterates to natively and efficiently solve DCLC in SR domains. BEST2COP leverages both SR properties and the inherently limited accuracy of measured delays to efficiently handle all scenarios; either with exactitude for simple instances (i. e. , with Pareto fronts limited in size and/or weak delay trueness) or, at worst for difficult instances, with strong guarantees, e. g. , a bounded distance regarding the delay constraint. Indeed, BEST2COP not only handle SR as a third constraint almost for free, but also relies on the most simple and efficient data structures and concepts available in the DCLC literature.
Materials and methods To map above-and belowground forest biomass carbon stock in China during 2002-2021, we (1) calibrated a SAR-based high-resolution forest aboveground biomass map in China based on massive field measurements of AGBC during 2011-2015, (2) extended the AGBC time series to 2002-2021 by referring to the tree and short vegetation cover retrieved from optical remote sensing, (3) calibrated the AGBC time series in some specific areas using a long-term, integrated microwave-based VOD dataset and (4) mapped forestlands' BGBC through a random forest model developed based on the in situ records in the published literature. The basic procedure is shown in Fig. 1   2019 )'s AGB performed the best among all existing AGB datasets in China  (Chang et al. , 2021). Mean forest canopy heights and tree coverage are also good indicators of the spatial pattern of forest biomass.
, 2021) , leaving 2444 homogeneous forest plots remaining (see Fig. 2  for the spatial distribution of these forest plots and Fig. S1a-b  in the Supplement for the cumulative frequency curve and histogram of the AGBC records). The AGBC records in these forest plots were further multiplied by the mean fraction of forestland over 2011-2015 in the corresponding grid, which was computed from the annual 30 m resolution China land cover dataset (CLCD)  (Yang and Huang, 2021). By comparison, GlobBiomass 2010 AGB matches the best with the gridscale forest AGBC derived from plot measurements, with a correlation coefficient (CC) of 0. 50, followed by tree cover (CC = 0. 42), the product of canopy height and tree cover (CC = 0. 38) and, finally, the canopy height (0. 27) and  Huang et al. (2019) 's AGB (0.
25). Therefore, to obtain an improved benchmark map of forest AGBC in China for the period of 2011-2015, we chose the GlobBiomass 2010 dataset as our basis and calibrated it against the in situ observationbased, grid-scale forest AGBC. To build an equation for the calibration, we divided the grid-scale AGBC values into 16 equidistant subranges (0-15, 15-30,. , 225-240 tC ha -1 ), calculated the median of grid-scale AGBC values that are within each subrange and then the median of GlobBiomass AGB values in the corresponding grids. According to previous studies, an exponential function would be suitable for calibrating the GlobBiomass map in a region such as China  (Mialon et al. , 2020). After the calibration, we averaged the benchmark AGBC map from 1/1200 to 1/120 • (approximately 1 km) to further reduce the uncertainties.
Temporally continuous forest AGBC mapping during 2002-2021 Because the benchmark AGBC was mapped based on SAR data, the spatial pattern accuracy is guaranteed, but the temporal coverage is limited to just a few years. Hence, to create a forest AGBC time series over the past 20 years, we integrated the benchmark AGBC with long-term continuous optical and passive microwave remote sensing data. The spatial resolution of optical remote sensing is higher and is thus preferred in this study. By adopting the MODIS vegetation continuous field (VCF) data (MOD44B v061), which includes three ground cover components, we obtain the percent tree cover, percent non-tree vegetation (i. e. short vegetation) cover and percent non-vegetated area  (DiMiceli et al. , 2022). We first calculated the mean tree cover (hereinafter TC mean ) and short vegetation cover (hereinafter SVC mean ) during 2011-2015 and resampled them from 250 m to 1/120 • , which is the same resolution as the benchmark AGBC map for 2011-2015.
We also aggregated the benchmark AGBC map and the VCF data (TC mean and SVC mean ) to 0. 25 • resolution. After each round of searching, we applied the shape language modelling algorithm (D'Errico, 2022), to fit the nonlinear but monotonous relationship between AGBC and VOD values within the searching window, and then fitted the bivariate linear regression between AGBC and VCF. If the nonlinear regression between AGBC and VOD is valid, and the R 2 is superior to the regression between AGBC and VCF data, then the LPDR VOD data are expected to outperform VCF in predicting the interannual AGBC changes in the corresponding 0. 25 • grid. Therefore, in these areas, we calibrated the VCFderived high-resolution (1/120 • ) annual AGBC by incorporating the ratio between the VOD-derived 0. 25 • AGBC and the aggregated VCF-derived AGBC in that year.
Forest belowground biomass carbon (BGBC) mapping during 2002-2021 This study mapped belowground forest biomass carbon (BGBC) following the random forest (RF) model approach  (Huang et al. , 2021). To reveal the forest above-and belowground biomass allocation rules in China, this study collated both AGB and BGB records at 8729 forest plots throughout China, which were obtained using allometric equations or clear-cutting methods from published papers, including  Luo (1996) ,  Luo et al. (2014) ,  Guo and Ren (2014)  and  Wang et al. (2014). Because forest stand age and tree species (forest type) information are also available at 8182 plots, while the climatic backgrounds are available from the WorldClim v2. 1 dataset  (Fick and Hijmans, 2017) , the forest plot AGB, forest type (hereinafter FOR_T), stand age, mean annual temperature (MAT), temperature seasonality (standard deviation of monthly temperature × 100; abbreviated as Tsea), mean annual precipitation (MAP) and precipitation seasonality (coefficient of variation in monthly precipitation; Psea) were ap- plied as predictors of the forest plot BGB.
For simplicity, we distinguished all forests into the following five types: evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), evergreen needleleaf forest (ENF), deciduous needleleaf forest (DNF), and mixed forest (MF). Using the data records at these 8182 plots (see Fig. 2  for the locations of these forest plots and Fig. S1c -f for the cumulative frequency curves and histograms of the AGB and BGB data), we trained 10-fold RF models using MATLAB R2021a. The number of regression trees was set to 500. Because the 1/120 • resolution grids in which forest AGBC data were available are often mixed with forestland and some other land cover types, e. g. waterbodies, bare ground and croplands, we converted the annual grid-average AGBC into the AGBC per area forestland by incorporating the annual fraction of forestland computed from the CLCD at 30 m resolution.
Considering the potential uncertainties in the forestland fraction and the inclusion of shrub or herbaceous plant AGB in the SAR-derived AGB, we only calculated the annual AGBC per area forestland in grids that were dominated by forestland (forestland fractions were consistently over 50 %). In these forestland grids, we simulated the forest BGBC per area forestland during 2002-2021 by inputting the estimated annual AGB (approximately 2 times the AGBC) per forestland, annual forest type map derived from the ESA Climate Change Initiative's land cover classification dataset  (Li et al. , 2018) , forest stand age  (Besnard et al. , 2021)  and climatic background variables into the RF model. Afterwards, we multiplied the simulation results in every forestland grid with the annual forestland fractions to obtain the forest BGB and BGBC (0.
Evaluation and assessment We compared the interannual trend of forest biomass carbon calculated in this study against that of existing global/regional long-term woody biomass datasets, including the well-received global long-term terrestrial biomass data between 1993-2012, which were developed mainly based on a long-term integrated VOD dataset  (Liu et al. , 2015) , and an updated woody biomass dataset covering 2001-2019, whose long time series was derived from optical remote sensing data (i. e. MODIS VCF dataset;  Xu et al. , 2021). To justify the random forest models for BGBC predictions, we drew partial dependence plots (PDPs) in MATLAB R2021a to show the marginal effect that one predictor has on the training target (e. g. BGB at forest plots;  Hastie et al. , 2009). Here, for each predictor, we excluded the extreme values (the lowest 1 % and the highest 1 %) before calculating the corresponding PDP to avoid roughly extending the PDP lines to data-scarce areas.
Comparison of the estimated forest biomass carbon pool change in this study against the existing datasets Although there is potential overestimation, the interannual variation in forest AGBC in China, according to  Liu et al. (2015) , and that of total biomass carbon, according to  Xu et al. (2021) , are both highly correlated with our results (R 2 = 0. 65 and 0. 88). Liu et al. (2015)  predicted a forest AGBC increase rate of 102. 2 ± 35. 8 Tg yr -1 (p < 0. 01), which is slightly higher than our estimate of 80. 8 ± 25. 1 Tg yr -1 during 2002-2012, while  Xu et al. (2021)  indicated a biomass carbon stock trend of 99. 4 ± 23. 2 Tg yr -1 (p < 0. 01) from 2002 to 2019, which is slightly lower than the rate of 115. 6 ± 20. 2 Tg yr -1 in this study (Fig. 7a-b ). The spatial maps of the forest biomass carbon trends estimated by  Xu et al.
(2021)  and  Liu et al. (2015)  were slightly patchy (Fig. 7c-d ). Compared to this study, the two existing datasets (i. e. the datasets of  Liu et al. , 2015, and Xu et al. , 2021)  predicted higher biomass carbon stock trends in the Qinling mountains and the mature deciduous conifer forests in northeastern China. Meanwhile, they predicted lower carbon sinks in southern China (Fig. 7c-f ), where reforestation and forest-management-induced shortterm extensive carbon uptake  (Tong et al. , 2020)  have been confirmed by atmospheric inversions  (Wang et al. , 2020; Yang et al. , 2021). Finally, by comparing Fig. 7e  and f, we could also notice that the hot spot of forest biomass carbon gains has moved from the Loess Plateau over the first decade of our study  period (2002-2012)  to southern China (e.
Some uncertainties in the forest biomass carbon dataset and future prospects During benchmark AGBC mapping, we converted the in situ AGBC data at forest plots into the grid-scale average AGBC by multiplying by the fraction of forestland during the time period of field investigation. Considering the overall high quality of the China's land use/cover datasets developed via the human-computer interactive interpretation of Landsat images  (Liu et al. , 2014; Yang and Huang, 2021) , and that the producer's accuracy (PA) and user's accuracy (UA) for forestland classification in the CLCD dataset used in this study were 73 % and 85 %, respectively, the errors within the benchmark AGBC mapping induced by the scale conversion based on the forestland area fraction were generally limited. The variation in climatic conditions in the short term may have subtle influences on that in the BGB, but explicit knowledge on this effect is lacking.