dataesr/scientific-paragraphs-categorization huggingface dataset:
Other Some of the unitigs were shared by closely related RTs. Unitigs for RT001 were able to identify the genetically closely related RT087, RT241, and RT012, which altogether form a clonal complex (CC) 141  [44]. One of the markers identified for RT017 showed no false-positives or false negatives. Other markers for RT017 initially generated a small number of false-positives, but 100% true-positives in the validation dataset. Markers for RT078 identified 78 out of 79 isolates of RT126 and all of the RT413 strains from the test dataset, likely due to the close genetic relatedness of these RTs (CC 1)  [44, 48, 49]. Unitig sequences for RT106 were also able to identify C. difficile RT500 along with RT106 from the test set. Phylogenetic grouping of C. difficile genomes  [44]  showed that C. difficile core genome multi locus sequence typing (cgMLST) of RT106 and RT500 (CC 22) generated completely indistinguishable groupings.
Marker's SEQ ID numbers and their sequences are given in Supplementary Table  S4. Some of the unitigs were shared by closely related RTs. Unitigs for RT001 were able to identify the genetically closely related RT087, RT241, and RT012, which altogether form a clonal complex (CC) 141  [44]. One of the markers identified for RT017 showed no false-positives or false negatives. Other markers for RT017 initially generated a small number of false-positives, but 100% true-positives in the validation dataset. Markers for RT078 identified 78 out of 79 isolates of RT126 and all of the RT413 strains from the test dataset, likely due to the close genetic relatedness of these RTs (CC 1)  [44, 48, 49]. Unitig sequences for RT106 were also able to identify C. difficile RT500 along with RT106 from the test set.
Acknowledgments: During this study, M. G. , L. H. , H. P. , M. J. , K. D. B. and A. v. B. were employees of bioMérieux, a company designing, developing, and marketing tests in the domain of infectious diseases. The company was not involved in the design of the current study and the opinions expressed are those of the authors and may be different from formal company opinions and policies. We thank our colleagues from bioMérieux and  Creighton University  who provided expertise and insight that greatly assisted the research.
514 keV) and Bi LIII (13. 409 keV) edges were performed at the BM08 (LISA) beamline of the European Synchrotron Radiation Facility (ESRF), Grenoble, France  [13]. All measurements were carried out using a dynamically sagittal-focusing Si (311) monochromator  [14] , and the spectra were recorded in the fluorescence yield mode using a 12 element hyper-pure Ge detector  [15]. Ag2S and Bi2S3 polycrystalline powders were measured in the transmission mode as references. The structure of Ag2S is that of the mineral acanthite (monoclinic lattice, space group 14, i. e. , P21/c) with two inequivalent Ag sites present, see Figure  2 , left panel. XAFS data analysis in the extended energy range (EXAFS) was performed using the DEMETER package  [16]  and theoretical signals based on FEFF  [17]. Simulations of the X-ray absorption nearedge structure (XANES) part of the XAFS spectra were performed using FDMNES  [18].
X-ray diffraction measurements were performed using a PANalytical X'Pert PRO MPD Alpha1 powder diffractometer with Cu Kα radiation (λ = 1. 5406 Å , 45 kV-40 mA) in the Scientific and Technological Centers of the University of Barcelona (CCiT-UB). The samples were prepared by drop-casting a concentrated dispersion of AgxBi1-xS2 NPs onto a clean glass slide. The XRD patterns were analyzed with the TOPAS software for structural refinement  [19] , version 7, using the functions    (13. 409 keV) edges were performed at the BM08 (LISA) beamline of the European Synchrotron Radiation Facility (ESRF), Grenoble, France  [13]. All measurements were carried out using a dynamically sagittal-focusing Si (311) monochromator  [14] , and the spectra were recorded in the fluorescence yield mode using a 12 element hyper-pure Ge detector  [15].
Ag 2 S and Bi 2 S 3 polycrystalline powders were measured in the transmission mode as references. The structure of Ag 2 S is that of the mineral acanthite (monoclinic lattice, space group 14, i. e. , P2 1 /c) with two inequivalent Ag sites present, see Figure  2 , left panel. XAFS data analysis in the extended energy range (EXAFS) was performed using the DEMETER package  [16]  and theoretical signals based on FEFF  [17]. Simulations of the X-ray absorption near-edge structure (XANES) part of the XAFS spectra were performed using FDMNES  [18]. X-ray diffraction measurements were performed using a PANalytical X'Pert PRO MPD Alpha1 powder diffractometer with Cu K α radiation (λ = 1. 5406 Å, 45 kV-40 mA) in the Scientific and Technological Centers of the University of Barcelona (CCiT-UB). The samples were prepared by drop-casting a concentrated dispersion of Ag x Bi 1-x S 2 NPs onto a clean glass slide.
The XRD patterns were analyzed with the TOPAS software for structural refinement  [19] , version 7, using the functions recently developed for the analysis of nanocrystalline powders  [20]. Different structures were implemented in the refinement to assess the degree of matching to the experimental data. Structural information on standard phases was taken from the "materials project" web interface  [21]. In addition, to fit quality and structural information, data analysis provides the crystalline domain size, assuming spherical shape with a lognormal distribution of diameters, and inhomogeneous strain (also referred to as microstrain). Details on the procedure are reported elsewhere  [20, 22]. Tentative structures for the clusters were simulated using the DFT method, as implemented in the VASP code (version 5.
The structures from the last 2. 9 ps of the run were used for the simulation of EXAFS spectra at the Ag K-edge  [29]. In particular, from 290 single structures (1 taken every 10 fs), a single EXAFS spectrum was calculated with the FEFF 8. 4 code  [30] , each Ag atom in the cell contributing with its own spectrum. Single and multiple scattering paths were considered up to a maximum path length of 10 Å and a maximum scattering order of 4, and the data range in k space was 0-20 Å -1. The data were then averaged and the final residual (i. e. , the difference between the spectra taken with 289 and 290 frames) was about 3 × 10 -4. The EXAFS spectra so obtained were successively filtered in the interval R = [0. 4-6. 8] Å in order to eliminate signals from atomic replicas generated by the periodic boundary conditions.
In particular, from 290 single structures (1 taken every 10 fs), a single EXAFS spectrum was calculated with the FEFF 8. 4 code  [30] , each Ag atom in the cell contributing with its own spectrum. Single and multiple scattering paths were considered up to a maximum path length of 10 Å and a maximum scattering order of 4, and the data range in  space was 0-20 Å -1. The data were then averaged and the final residual (i. e. , the difference between the spectra taken with 289 and 290 frames) was about 3 × 10 -4. The EXAFS spectra so obtained were successively filtered in the interval R = [0. 4-6. 8] Å in order to eliminate signals from atomic replicas generated by the periodic boundary conditions.
Introduction: The National Comprehensive Cancer Network guidelines recommend re-challenge with the first-line treatment for relapsed small cell lung cancer (SCLC) with chemotherapy-free interval (CTFI)≥180 days. A phase II study (NCT02454972) showed remarkable antitumor activity in SCLC patients treated with lurbinectedin 3. 2 mg/m 2 1 -h intravenous infusion every 3 weeks as second-line therapy. We report results for the pre-planned subset of patients with CTFI ≥ 180 days.
Lurbinectedin (ZEPZELCA™, PharmaMar, Madrid, Spain) is a selective inhibitor of oncogenic transcription that binds preferentially to guanines located in the GC-rich regulatory areas of DNA gene promoters  [15, 16]. By preventing binding of transcription factors to their recognition sequences, the drug inhibits oncogenic transcription and leads to tumor cell apoptosis  [17]. Lurbinectedin also affects the tumor microenvironment landscape by inhibiting activated transcription in tumor-associated macrophages  [18]. Recently, in June 2020, the US FDA granted accelerated approval to lurbinectedin for adult patients with metastatic SCLC with disease progression on or after platinum-based chemotherapy based on results from a phase II study (study B-005; NCT02454972). This pivotal study showed remarkable antitumor activity in 60 SCLC patients with CTFI ≥ 90 days treated with lurbinectedin as second-line: ORR of 45.
Statistical analysis This exploratory assessment is based on a pre-planned analysis by CTFI. Descriptive statistics were used. Non-continuous variables are described in frequency tables using counts and percentages. Continuous variables are described by median, minimum and maximum. Binomial exact estimates and its 95 % confidence interval were calculated for the evaluation of the main endpoint (ORR). The Kaplan-Meier method was used to analyze DoR, PFS and OS. AEs were recorded and coded with the Medical Dictionary for Regulatory Activities (MedDRA), v. 21. 0. AEs and laboratory values were graded per the National Cancer Institute-Common Toxicity Criteria for Adverse Events (NCI-CTCAE), v. 4. 0. SAS software (v. 9. 4) was used to generate statistical outputs.
Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.
However, deep active learning (DAL), integrating active learning (AL) in DL architecture, is able to achieve text classification based on few labelled data which can minimize the work of human labelling  [20] [21] [22]. It would seem to be more appropriate to implement text classification based on a new bibliographic dataset for selecting relevant records, while the labelled data derived from active learning could be used as training data to train the DL architecture  [22]. In this context, focusing on landscape factors affecting dengue transmission and satellite EO data currently used for identifying landscape factors, this study proposes to build a semi-supervised classification framework of literature by integrating the review process and text classification algorithms and provides an overview of dengue landscape factors and satellite EO data.
Board Searches and Removal of Duplicates The searches were performed from inception to 31 December 2019 in four databases: Science Direct, Web of Science, PubMed and Scopus, by considering the titles and abstracts of English journal articles. The queries were formed by combining dengue-related terms (i. e. , dengue and Aedes) and the words related to "remote sensing", "landscape" and "weather" (i. e. , remote sensing, satellite, earth observation, landscape, land cover, land use, household, dwelling, habitation, precipitation and temperature) using the Boolean operator "AND" (see more details in Table  A1 ). All search records were combined together and the duplicate records were removed using the MySQL database. The remaining records were organized in alphabetical order for further analysis.
Text Scoring To efficiently eliminate the definitely-irrelevant records, we used text weighting and text scoring for ranking all the records. First, we pre-set some terms KEY i (i = 1,. , m) and their priority levels (i. e. , high, medium and low) (Table  1 ) according to the criteria in Section 2. 1. Each of them was randomly assigned a weight value WEIGHT i (i = 1,. , m) from the interval of weights that was set according to its priority level. The higher the priority level of a term, the greater its weight value. We then extracted the key terms K j (j = 1,. , n) and their corresponding weight values W j (j = 1, 2,. , n) from the title and abstract using the Natural Language Toolkit (NLTK) in Python. If K j contains pre-set terms in KEY i , we calculated the score of a record as Score = WEIGHT i * W j (i = 1,.
BiLSTM-Based Active Learning To efficiently and accurately select relevant records in the absence of sufficient labelled samples, we performed a BiLSTM-based active learning based on the titles and abstracts of the records derived from text scoring (Figure  1 ). Prior to training the BiLSTM model (see more details in Appendix C)  [23] , we created an initial training dataset by selecting 15 relevant samples and 30 irrelevant samples from the results of text scoring based on the criteria in Section 2. 1. The initial training dataset was used to train the BiLSTM model. Based on the word embedding derived from the unlabelled data using the Word2Vec CBOW model  [24]  (see more details in Appendix B), the BiLSTM model was used to identify the "potential" records from unlabelled data, which were then manually labelled as either relevant or irrelevant based on the four criteria in Section 2.
1. Meanwhile, we improved the training dataset by combining the selected relevant records and previous relevant samples, and randomly selected irrelevant records from the results of text scoring in order to keep the ratio of relevant and irrelevant samples at 1:2. Finally, the BiLSTM model was re-trained using the new training dataset to identify the potential citations from the remaining unlabelled data. The parameters of the BiLSTM architecture were updated by training the results from the previous round. BiLSTM learning and active learning were alternately implemented until we could not find any relevant records.
Inclusion, Perfomance and Rationality Because all the algorithms were implemented based on the titles and abstracts, we evaluated the full-texts of the records derived from BiLSTM-based active learning for final inclusion of the articles that met the criteria in Section 2. 1. In fact, bibliographic databases might misclassify some records as English journal articles and store their English titles and abstracts. To verify the performance of the algorithms of text scoring and BiLSTM-active learning, we randomly selected 10% of unlabelled records derived from BiLSTM-based active learning and manually interpreted them as either relevant or irrelevant. This step was iterated three times. Moreover, to verify the rationality of text scoring and BiLSTM-based active learning, we computed the number of relevant records per score rank interval.
Throughout the process of semi-supervised text classification, we manually evaluated 1056 titles/abstracts (Table  3 ). Moreover, the accurate and rational identification of relevant records can be indicated by the following two facts. First, no relevant records were found by manually evaluating the records selected randomly from the unlabelled dataset (i. e. , 925 records after BiLSTM-based active learning). This indicated a good performance of the semi-supervised text classification. Second, although each record probably received different scores in 20 text scoring experiments, the number of relevant records per score rank interval showed a consistent decreasing trend (Figure  2 ). This indicated the rationality of text scoring using the preset terms and priority levels, that is, the more relevant a record is to the topic question, the greater the possibility it will receive a high score.
selected randomly from the unlabelled dataset (i. e. , 925 records after BiLSTM-based active learning). This indicated a good performance of the semi-supervised text classification. Second, although each record probably received different scores in 20 text scoring experiments, the number of relevant records per score rank interval showed a consistent decreasing trend (Figure  2 ). This indicated the rationality of text scoring using the preset terms and priority levels, that is, the more relevant a record is to the topic question, the greater the possibility it will receive a high score. The accurate and rational identification of relevant records can be explained by the facts: (1) A clear topic was defined. In fact, modelling or correlating dengue epidemiological or entomological variables with landscape factors in different geographic contexts often includes the identification of landscape factors, landscape characterization and spatio-temporal analysis of dengue cases or vectors.
This interdisciplinary topic provides evident features that meet the definition of appropriate inclusion criteria. These criteria then help to define terms and priority levels for text scoring and active learning. (2)  The union of the results of 20 text scoring experiments enable the inclusion of potential records as much as possible, and greatly exclude the irrelevant records. (3) BiLSTM has proved to be especially useful in understanding the context of words  [23] , and active learning based on clear and appropriate inclusion criteria allows for the accurate selection of relevant records and for the control of the balance of positive and negative samples in training datasets for each cycle in BiLSTM learning. Moreover, it should be noted that other models are possible, such as BiLSTM with attention mechanism (AC-BiLSTM)  [16]  or a combination of CNN and LSTM (C-LSTM)  [25] , which might generate a high accuracy of text classification.
Satellite Earth Observation Data Among the 101 included articles, only 64 studies used satellite EO data. Table  4  presents the satellite EO sensors, derived products and spatio-temporal resolutions used for identifying dengue landscape factors in selected studies. Evidently, for LU/LC mapping, most studies used very fine (i. e. , pixel size < 10 m) and fine (i. e. , 10 m ≤ pixel size < 100 m) spatial resolution data, including multi-spectral bands derived from Landsat 4 Thematic Mapper (TM), Landsat 5 TM, Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), Indian Remote-Sensing Satellite-P6 (IRS-P6), Satellite Pour l'Observation de la Terre 4 (SPOT-4), Sentinel-2, GaoFen-1, SPOT-5, Advanced Land Observing Satellite (ALOS), IKONOS and Quickbird. For topographic factors, two global scale and freely available digital elevation models (DEMs) at resolutions of 30 m and 90 m from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) mission were used to extract topographic features.
Conclusions Satellite EO has been increasingly used in dengue research over the past years, especially for the identification of dengue landscape factors. During that time, various types of landscape factors were considered while the study areas and research objectives have become more complex, and the variety and volume of satellite EO data have been growing over these years. There is an increasing need to know what dengue landscape factors have been studied and what dengue landscape factors have been derived from satellite EO data during the past years. In this study, by integrating the review process, AL mechanism, text scoring and BiLSTM model, we propose a semi-supervised text classification framework that enables the efficient evaluation of bibliographic records derived from bibliographic databases and accurately selects the articles relevant to the research objective.
In this study, 101 relevant articles were efficiently selected from bibliographic databases using the proposed approach. Among them, 64 articles used satellite EO data. Valuable information on dengue landscape factors and current satellite EO data was reported. A catalogue of essential dengue landscape factors were identified that were divided into four categories: LU, LC, topography and continuous land surface features. These factors were considered as the direct or indirect proxies of Aedes breeding and resting sites, human-Aedes encounters, human mobility and virus replication in dengue transmission. Moreover, future research directions on how to integrate satellite EO data in dengue research were proposed in terms of landscape patterns, satellite sensors and deep learning. This study is an important step towards an efficient method for research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.
12  Other immuno-oncology and chemotherapy combinations, such as the IMpower 150 regimen, which has been approved in the EU  13  and USA 14 and includes atezolizumab and chemotherapy in combination with bevacizumab, have also demonstrated efficacy in patients with metastatic non-squamous NSCLC. 15 16 iven the changing treatment landscape and the emergence of immune-checkpoint inhibitors and chemotherapy combinations in the first-line setting, docetaxel in combination with an antiangiogenic agent may be an appropriate second-line treatment option following progression on a platinum containing regimen. 17 The randomised phase 3 REVEL trial (NCT01168973),  18  which included patients with non-squamous and squamous stage IV NSCLC, demonstrated that the addition of ramucirumab, an antibody targeted to the vascular endothelial growth factor receptor 2, to docetaxel improved survival compared with docetaxel alone (increase of 1.
| Offspring growth For offspring growth, we measured total leaf area (cm 2 ), shoot length (cm), leaf biomass (g), shoot biomass (g), and root biomass (g). However, we were not successful with the whole root sample collection from the field; hence, below-ground biomass were not analysed for the field experiment. The field samplings for offspring total leaf area and biomass were conducted by the end of the 16th and 25th week since sowing, whereas the greenhouse samplings were conducted by the end of the 14th and 23rd week since sowing. The total leaf area was measured by using the software WinFolia image analyser (Regent, Quebec, Canada). For offspring dry biomass, offspring samples were dried in an oven for at least 3 days at a constant temperature of 60 C.
| Data analysis 2. 5. 1 | Seed weight The weight of an individual seed was modelled as a function of mother tree treatments (three levels: irrigated, irrigation stop, and control). Because the weight of an individual seed varies across trees and the tree that produced higher number of seeds usually has a higher number of offspring in the forest, we also converted the tree-level individual seed weight to weighted tree-level individual seed weight. For this, the average seed weight of an individual tree was weighted by the total number of seeds produced by that tree.
Effects of predictor variables on seed germination and offspring mortality (i. e. , percentage data) were assessed by generalized linear mixed effect models using the function glmer in the lme4 package in R with negative binomial distribution  (Bates, Maechler, Bolker, & Walker, 2017) , whereas the effects of predictor variables on seed weight, number of seeds produced by a tree, and offspring growth (leaf area, biomass, and root to shoot ratio) were assessed by linear mixed effect models using the function lme in the nlme package in R  (Pinheiro, Bates, DebRoy, & Sarkar, 2014). The post hoc Tukey multiple comparison test was performed to detect the statistical differences  (Hothorn, Bretz, & Westfall, 2008). We visually verified the assumptions of normality and variance homogeneity of the residuals.