dataesr/scientific-paragraphs-categorization huggingface dataset:
The present work is structured as follows. Section 2 contains a literature review of similar work in other areas and focuses on warehouse systems. In addition, some literature gaps are described, and this paper's primary purpose is presented. Section 3 is dedicated to the applied KPI discovery, dataset creation, and metric selection procedure. Section 4 presents the indicators found with their definition and a ranking of the indicators according to the metric presented. Finally, in Section 5, there are the conclusions with a focus on the future perspectives and limitations of this paper.
Search on Scopus TITLE (warehouse) AND NOT TITLE-ABS-KEY ("data warehouse") Quality sampling of Science and Scopus stand out as the most valuable databases, with a focus on Life Sciences, Physical Sciences, and Technology. Web of Science is known for its selectivity, whereas Scopus offers a more comprehensive coverage. It is worth noting that nearly all the journals in Web of Science are also indexed in Scopus. In fact, Scopus boasts 66. 07% more unique journals compared to Web of Science  [22]. Nevertheless, it is important to emphasize that Scopus primarily includes journals considered of high value. Therefore, our decision to opt for Scopus reflects a balance between selectivity and the preservation of valuable contributions. To extract the most comprehensive number of documents without including off-topic articles, we define the following query: "TITLE (autom* AND warehouse)" on Scopus (https:// www.
KPI relevance validation The second objective of this work is to extract a sufficiently big enough database about the use of warehouse KPIs in order to create an evaluation and ranking system. To extract a comprehensive sample of articles focused on warehouse systems, we define the following query: TITLE (warehouse) AND NOT TITLE-ABS-KEY ("data warehouse") on Scopus (https:// www. scopus. com/). This query allowed us to find papers with a focus on warehouses. Searching the same keyword (warehouse) inside the whole abstract would extract too many documents without a clear direction. Therefore, we limited the query to the title. We also decided to avoid all these articles containing the keyword "data warehouse" in their title since it was evident from different analyses, we conducted that the central part of the out of our scope articles was in the computer science domain.
A not discussed output of the analysis described in this paper is creating a collection of scientific manuscripts in PDF format where the KPIs identified by human processing analysis are highlighted. This database is a resource that habilitates the use of supervised machine learning methods to obtain a KPI detection model in a document. Therefore, a comprehensive future improvement is using both supervised and not supervised methods to automatically identify KPIs in a text-based document. Finally, an interesting qualitative future work is the introduction of the industry 5. 0 concepts in the KPI classification. This work includes a classification based on different sustainability criteria, and this is a fundamental step in evaluating and classifying KPIs in terms of sustainability, human-centrality, and resilience of the system where this KPIs set is applied.
PURPOSE Metastatic castration-resistant prostate cancer (mCRPC) remains a lethal disease with current standard-of-care therapies. Homologous recombination repair (HRR) gene alterations, including BRCA1/2 alterations, can sensitize cancer cells to poly (ADP-ribose) polymerase inhibition, which may improve outcomes in treatment-naïve mCRPC when combined with androgen receptor signaling inhibition. METHODS MAGNITUDE (ClinicalTrials. gov identifier: NCT03748641) is a phase III, randomized, double-blinded study that evaluates niraparib and abiraterone acetate plus prednisone (niraparib 1 AAP) in patients with (HRR1, n 5 423) or without (HRR2, n 5 247) HRR-associated gene alterations, as prospectively determined by tissue/plasma-based assays. Patients were assigned 1:1 to receive niraparib 1 AAP or placebo 1 AAP.
INTRODUCTION 5] [6] [7] [8]  Deleterious gene alterations, particularly in BRCA1/2, can sensitize prostate cancer cells to inhibition of poly (ADP-ribose) polymerase (PARP) through a mechanism of synthetic lethality. 4, 9 Inhibition of androgen receptor signaling can also result in downregulated expression of DNA repair genes and sensitize prostate cancer cells to PARP inhibition. 10, 11 Therefore, we hypothesized that targeting both oncogenic pathways concurrently may improve outcomes in patients with and without HRR alterations. 10, 12 4] [15] [16] [17]  In the phase II GALAHAD trial (ClinicalTrials. gov identifier: NCT02854436), an objective response rate (ORR) of 34. 2% with niraparib monotherapy as third-line or higher therapy was achieved in patients with mCRPC, measurable metastases, and BRCA1/2 pathogenic alterations.
Study Assessments and End Points The primary end point was radiographic PFS (rPFS), defined as the time from random assignment to the first occurrence of radiographic progression assessed by blinded independent central review or death due to any cause. rPFS was determined by first observation of progression by bone scan (per Prostate Cancer Working Group 3) or progression of soft-tissue lesions by CT or MRI (per RECIST 1. 1), with imaging done every 8 weeks for the first 6 months and every 12 weeks after. 23, 24 Secondary end points were time to initiation of cytotoxic chemotherapy (TCC), time to symptomatic progression (TSP), and OS (Data Supplement [online only]). Other end points included time to PSA progression, ORR, and patient-reported outcomes (Data Supplement). Treatment-emergent adverse events (AEs) were defined as AEs occurring after the first dose of study drug to 30 days after the last dose and summarized by category and preferred term.
In the HRR1 cohort, niraparib 1 AAP delayed TCC (HR, 0. 59; 95% CI, 0. 39 to 0. 89;   4D ). At this preplanned interim analysis, the significance boundary of. 0001 (O'Brien-Fleming method) was not met by either end point. OS data are immature at this first interim analysis, with 46. 3% of the required events for final analysis (Fig  4E ; Data Supplement). The prespecified multivariate analysis of OS, adjusting for baseline variables, favored the niraparib 1 AAP arm in the HRR1 cohort (0. 77; 95% CI, 0. 53 to 1. 12; Data Supplement). Additionally, niraparib 1 AAP prolonged time to PSA progression and led to higher ORR in the HRR1 and BRCA1/2 groups (Fig  5 ). Time to PSA progression and rPFS were strongly correlated, with an overall r 5 0. 67 (95% CI, 0. 56 to 0. 75). In the HRR1 cohort, patientreported quality of life (QoL) changes over time between treatment arms were similar as determined by FACT-P total score (Data Supplement).
HRR2 cohort. In the futility analysis, 233 HRR2 patients (niraparib 1 AAP, n 5 117; placebo 1 AAP, n 5 116) were evaluated for the composite end point of time to PSA progression and/or rPFS (HR, 1. 09; 95% CI, 0. 75 to 1. 57; P 5. 66; Data Supplement) and the individual end points of rPFS and time to PSA progression (Data Supplement). On the basis of the prespecified criteria, futility was declared for the HRR2 cohort in August 2020, which was closed to further enrollment on the basis of Independent Data Monitoring Committee recommendations. All patients in the HRR2 cohort were unblinded, and patients randomly assigned to niraparib 1 AAP were allowed to continue niraparib 1 AAP or AAP alone per the investigator's discretion. Additional efficacy assessments were not performed once patients entered a safety data collection phase.
OS data were immature at this first interim analysis, although results from the OS multivariate analysis favor the niraparib 1 AAP group. As noted, the conservative boundary for statistical significance at this first interim analysis for all secondary end points was not crossed. Follow-up of patients with HRR1 mCRPC enrolled in MAGNITUDE is ongoing. Other limitations of the MAG-NITUDE study include the limited diversity in patient race and demographics. Furthermore, although the nine-gene panel selection was based on previous clinical data, comprehensive assessment of other genes implicated in HRR was not undertaken. The incidence of grade 3/4 AEs was higher with niraparib 1 AAP, with approximately a 4-fold increase of grade $ 3 anemia with niraparib 1 AAP compared with 7% with placebo 1 AAP, similar to previously reported data for AAP.
ACKNOWLEDGMENT We thank the patients who participated in this study, along with their families and carers, and the study teams who were involved at each participating institution. We also thank  Namphuong Tran, MD ,  Xin Zhao, MD, PhD , and  Nishi Kothari, MD , for overall assistance and intellectual guidance, including assistance with trial design;  Kavitha Nagarajan , BC, and  Susan Li, PhD , for assistance with data analysis;  Angela Trinh, BS ,  Mardy Eckhardt, PhD ,  Marcin Zmarzlik, MS ,  Danuta Gawryluk, PhD ,  Lauren Boswell , BS,  Susan Laabs , BA, and  Eileen Kan, MS, MBA , for assistance with study conduct and data analysis; and  Natalie Hutnick, PhD , and  Karen Urtishak, PhD , for assistance with biomarker development and analyses. Medical writing and editorial assistance were provided by  Danyang Zhou, PharmD ,  Lauren Fink, PhD ,  Michelle Kwon, PhD , and  Kristen Evaul, PhD , of  Lumanity Communications Inc (Yardley, PA, USA ), which was provided in accordance with Good Publication Practice (GPP3) guidelines and funded by  Janssen Research & Development.
Here, we tackle this question by systematic reanalysis of data from 63 fossil plant assemblages representing almost all continents, varied paleolatitudes, and nonanalog ecosystems (dataset of  Currano et al. , 2021). We explore the effect of resampling at different leaf sampling intensities (above and below 300 leaves) to determine the robustness of network metrics, assess paleoecologically relevant network-level and network class-level properties, and compare it with richness-based metrics that are often used in these systems (see  Currano et al. , 2021). We find that functional bipartite network representations not only perform better than many richness-based metrics, but also, with limited data, network metrics capture certain aspects of the complex structure of plant-DT interactions that are otherwise impossible to ascertain from aggregate richness metrics.
Data collection The compiled dataset analyzed here was developed for a previous meta-analysis study  (Currano et al. , 2021). In that study, fossil insect damage census data published before 2021 were collected using Web of Science and Google Scholar using the keywords "insect herbivory," "plant-insect interactions," and "fossil. " Censuses selected for analysis were younger than 70 million years (Late Cretaceous) and dominated by dicotyledonous angiosperms. The sites were included in the meta-analysis only if they were collected in an unbiased manner; that is, only if all identifiable leaves were scored for herbivory and amounted to at least 300 dicot leaves. Each fossil assemblage dataset consisted of information about individual fossil leaves (e. g. , taxonomic information) in the assemblage and a list of the number of different DTs present on each leaf.
Metrics and comparisons For each resampling of a given assemblage, we constructed a bipartite network using the occurrence data of DTs on individual leaves, such that plants and DTs are the two node classes in the network with edges or links connecting them. If one considers the adjacency matrix representation of this network, where rows are plant taxa and columns are DTs, we collated the number of DTs of a particular type on the leaves of a particular plant taxon in a single matrix element corresponding to the row and column represented by the plant and DT respectively. The reconstructed network was then used to calculate a set of metrics that have appeared in previous analyses of bipartite networks and may be ecologically meaningful metrics for plant-DT associations. These metrics have been used in previous works for different ecological systems  (Bascompte & Jordano, 2013; Dormann et al.
RESULTS From standard resampling procedures, it is expected that the resampled dataset would yield a lower richness than the full dataset, and this result is recovered (see Figure  1A, B ). For both plants and DTs, a 75% recovery rate (i. e. , the fraction of true value observed in a subsample) is observed for richness on average at the standard 300 sample size, and this continues to increase, as expected, with increased sampling. This relationship is the backbone of most of the comparisons using resampling in fossil plant-DT association studies  (Currano et al. , 2021). Given this result, one would expect that network metrics, which heavily depend upon the complex interactions among species, would perform even worse in resampling calculations as they depend highly upon the sampling and its accuracy.
These metrics include the species richness of plants at (A); damage types (DTs) at (B); their respective functional complementarity at (C) and (D); connectance at (E); and interaction evenness at (F). The values for each assemblage, before being averaged, were normalized with respect to the metric value of the complete assemblage, thereby using all the available data for the assemblage, which would have a value of 1. The red dotted horizontal line denotes 1, and the blue vertical dotted line denotes the 300-sample size of leaves used in previous works (see  Currano et al. , 2021). Please note that the sample size axis is logarithmic. associated with each link. Such metrics require weighted edges/links in bipartite networks, rather than just presences or absences that involve unweighted edges/links in bipartite networks.
These metrics include the C-Score for plants at (A) and damage types (DTs) at (B); nestedness, or the nestedness metric based on overlap and decreasing fill (NODF) at (C); H2, specialization at the level of the entire network, at (D); resilience slope (for DTs) at (E); and robustness/resilience (for DTs) at (F). The values for each assemblage, before being averaged, were normalized with respect to the metric value of the complete assemblage, using all the available data for the assemblage, which would have a value of 1. The red dotted horizontal line denotes 1, and the blue vertical dotted line denotes the 300 sample leaf level used in previous works  (Currano et al. , 2021). Please note that the sample size axis is logarithmic. F I G U R E 4 Legend on next page. from the VFD. Richness values for subsampled assemblages show a general expected trend of worse performance for larger sample size assemblages for a given subsampling size, with more than 50% underestimation for groups with 2000-4000 and >4000 leaves (Figure  4N, O ).
DISCUSSION Using functional bipartite network approaches, in contrast with taxonomic bipartite networks (Table  1 ), we sought to examine the robustness of network metrics to sampling intensity, and thereby assess the general usefulness of functional bipartite network approaches for the analysis of plant-insect associational data. Here, we show that many network metrics are reliable at estimating various interactions between plant and insect herbivores, using damage types (DTs;  Labandeira et al. , 2007)  within paleontological datasets. Utilizing the functional network approach allows us to depart from taxonomic classifications, focusing on conserved functional traits/groupings, which are comparable across large swaths of geological time as well as geographical space. Traditionally, studies of plant-DT associations have focused on quantifying the frequency and richness of DTs and functional feeding groups (FFGs) from plant assemblages  (Currano et al.
Expanding from simple rarefaction to incorporate such effects in subsampling routines may provide a better ecological signal, one that accounts for collection and various forms of preservational bias. Bipartite networks for modern and fossil plant-insect associations often differ in sampling intensity, and modern Linnaean taxonomic networks use datasets derived from plants and insect herbivores instead of plants and DTs. Nevertheless, the plant-DT association data we considered is similar to modern data in two ways. First, interactions, as damage, are represented on leaves and other plant organs. Such types of damage are therefore proxy units for observation of insect herbivory, and more importantly, these DTs are similar across space and time, irrespective of species which lodged them. For bulk census data of modern floral assemblages, documentation of an interaction typically is independent of the existence of the two interacting species of a plant host and an interactive herbivore  (Novotny et al.
ACKNOWLEDGMENTS This is contribution 402 of the Evolution of  Terrestrial Ecosystems consortium at the National Museum of Natural History , in Washington, DC. Anshuman Swain is funded by a  James S. McDonnell Foundation (JSMF) postdoctoral Fellowship. S. Augusta Maccracken  is funded by  NSF  PRFB award No. 2010800. Ellen D. Currano  is funded by  NSF   EAR 145031.
Measurements of endosperm turgor by nanoindentation Siliques were opened and seeds were placed individually on adhesive tape on a microscope slide and covered with water. The slides were then placed on the extended stage of the nanoindenter (TI950 Triboindenter, Hysitron). A truncated conical tip with a flat end of ~100 µm diameter (nominal value = 96. 96 µm) was used for indentation. The "displacement-controlled" mode was used to allow imposition of a maximum indentation of 30 µm with a load rate of 6 µm/s (5 s extend, 5 s retract). High-resolution force-displacement curves were recorded with a data acquisition rate of 200 points/s. After indentation, the water was removed and replaced with a drop of clearing solution to allow subsequent embryo staging (see clearing section). Endosperm pressure was calculated using the following formula as described in Beauzamy et al.
Quantification of pELA1::3X-VENUS-N7 expression in ad-oi nuclei The samples were prepared as described in the previous sections. Z-stacks of Col-0 and iku2 seeds expressing pELA1:3X-VENUS-N7 were acquired using a Leica SP8 upright confocal microscope equipped with a 40x water immersion objective (HCX APO L UV 40×/0. 8 W). After imaging, the water was removed and replaced with a drop of clearing solution to allow subsequent embryo staging (see clearing section). Nuclear Fluorescence intensities were measured using a custom-made macro script developed in ImageJ where the nuclei were segmented using on z-stack projections (Sum-slices) using a marker-based watershed (https://imagej. net/Marker-controlled_Watershed).
Seed clearing, size measurements and embryo staging At different days after anthesis, the siliques were opened with a needle, and the seeds were removed with forceps and put in a drop of clearing solution (1 vol glycerol/7 vol chloral hydrate liquid solution, VWR Chemicals) between a slide and a coverslip. The samples were incubated for at least 24 h at 4 °C before being imaged with a Zeiss Axioimager 2 equipped with a 20× DIC dry objective. Seed area was measured by outlining the seed manually using the polygon selection in ImageJ. To compare experimental data with simulations, the radius of the seed was calculated from area measurements by formalizing the seed as a circle (Area = π (radius) 2 ). Seeds were manually classified based on the developmental stage of their embryo. When the embryo was not visible, the seeds were labeled as unclassified.
The relative seed growth rate at day (n) was calculated using the following formula: Relative growth rate = (Area (Day n ) -Area (Day n-1 )) / Area (Day n-1 ). For measurements of seed size at maturity, the seeds were imaged using a Leica stereomicroscope. The resulting images were analyzed using the ImageJ software. The seed outlines were segmented by setting up min and max intensity to 110 and 170, performing a Gaussian Blur of 2px, and using the Make Binary and Analyze Particles functions. The seeds that were too close to each other to be segmented separately were manually removed from the analysis after the segmentation. In vitro culture of fruits and sorbitol treatments Fruits at 3 DPA were cut at the base of the pedicel using a sharp scalpel and immediately placed within a 2 mL Eppendorf tube containing 1.
25 mg. mL -1 and mounted with VECTASHIELD (Eurobio). The sections were imaged using a Zeiss Axioimager 2 equipped with a 40× dry objective. Quantification of the immunofluorescence signal was performed with a custom macro script developed on the ImageJ software. The two channels were split; the first channel was labeled as the "control" (Calcofluor), and the second channel as the "signal" (antibody) (Supplementary Fig. 7 ). Testa cells were segmented from the control channel using a stationary wavelet transformation and a marker-based watershed (https://imagej. net/Marker-controlled_Watershed). Each testa cell was then manually assigned to its layer. Enlarged region of interest (ROI) for a given cell (layer n) and for its neighboring cells (layer n-1) were added onto a new image and used to define cell wall junctions as being the common region between the two (Image-Calculator (And…) Command).
Quantitative gene expression analysis Total RNA from siliques at 2 and 5 DPA was extracted using the Spectrum Plant Total RNA Kit (Sigma). Total RNAs were digested with Turbo DNA-free DNase I (Ambion) according to the manufacturer's instructions. The mRNAs were reverse transcribed using the Super-Script VILO cDNA Synthesis Kit (Invitrogen) according to the manufacturer's protocol. PCR reactions were performed in an optical 384-well plate in the QuantStudio™ 6 Flex Real-Time PCR System (ThermoFisher Scientific), using FastStart Universal SYBR Green Master (Rox) (Roche), in a final volume of 10 µL, according to the manufacturer's instructions. The following standard thermal profile was used for all PCR reactions: 95 °C for 10 min, 40 cycles of 95 °C for 10 s, and 60 °C for 30 s. The data were analyzed using the QuantStudio Real-Time PCR Software v1.
3 (Applied Biosystems). As a reference, a geometric mean between two house-keeping genes, EIF4A1 and AP2M, was used to normalize ELA1 expression. For each couple of primers, PCR efficiency (E) was estimated from the data obtained from standard curve amplification using the equation E = 10 -1/slope. Expression levels are presented as E -ΔCt , where ΔCt = Ct GOI -Ct REF. The sequence of the primers used for qPCR can be found in Supplementary Table  7.
Statistical analysis No sample size calculation was performed prior to the experiments. The number of seeds (biological replicate) analyzed in each experiment was chosen given technical constraints (in time and material) specific to each experiment. At least, three seeds harvested from three different siliques were used per independent experiment. Each experiment was carried out independently at least twice (i. e. , from independent batches of plants growing at different times). Data from independent experiments were pooled except in the following cases: (1) when the growth conditions of the independent experiments were different (long days or continuous light, see Plant material and growth conditions section), (2) when the settings used for the acquisition of fluorescent images were not the same, and (3) where sample sizes were very different between independent experiments.