dataesr/scientific-paragraphs-categorization huggingface dataset:
PCR was performed applying the following cycling conditions: 72 °C for 3 min, 98 °C for 30 s, then ten cycles of [98 °C for 10 s, 63 °C for 30 s, and 72 °C for 3 min], followed by a final 72 °C extension for 5 min. Final PCR products were purified using HighPre PCR Clean-up System (MagBio Genomics) and analyzed by Fragment analyzer (Agilent). Libraries were quantified using Qubit 4 Fluorometer (Life Technologies), pooled and sequenced on a NextSeq instrument (Illumina). Bioinformatics. NGS sequencing data were demultiplexed using bcl2fastq software, and individual FASTQ files were analyzed using a Perl implementation of the Matlab script described previously  34. For the quantification of indels or base editing frequencies, sequencing reads were scanned for matches to two 10 bp sequences that flank both sides of an intervening window, in which indels or base edits might occur.
Top three candidates were selected for each sgRNA for NGS analysis. Sequencing data were analyzed by CRISPResso2 (ref. 81 ). Cytidine base editing and DT treatment of mice humanized for hHBEGF expression. All mouse experiments were approved by the AstraZeneca internal committee for animal studies and the Gothenburg Ethics Committee for Experimental Animals (license number: 162-2015+) compliant with EU directives on the protection of animals used for scientific purposes. Experimental mice were generated as double heterozygotes by breeding Alb-Cre mice (016833, The Jackson Laboratory) to iDTR mice (Expression of transgene, human HBEGF, is blocked by loxP-flanked STOP sequence) on the C57BL/6NCrl genetic background. Mice were housed in negative pressure IVC caging, in a temperature controlled room (21 °C) with a 12:12 h light-dark cycle (dawn: 5:30 a.
An automated Droplet Generator (BioRAD) was used to generate droplets in a new semi-skirted 96-well PCR plate (30129504, Eppendorf). After droplet generation, the PCR plate was placed in a C1000 Touch™ Thermal Cycler (Bio-Rad, cat no. 185-1197) for PCR amplification, as detailed in Supplementary Table  6. The droplet reading was performed with the QX 100 Droplet reader (Bio-Rad, cat. no. 186-3001), using ddPCR™ Droplet Reader Oil (Bio-Rad, cat. no. 186-3004). Data acquisition and analysis was performed using the software QuantaSoft (Bio-Rad) and the "RED" program. The fluorescence amplitude threshold was set manually as the midpoint between the average fluorescence amplitude of the four droplet clusters (Translocation-positive, AP3B1positive, positive for both targets, and empty droplets).
qPCR was carried out in white 384-well plates (Thermo Fisher) in 10 μL reactions using the 2× TaqMan® Fast Advanced Master Mix (Thermo Fisher) and 12 ng of cDNA template, applying the following thermal protocol: 95 °C for 20 s: 40 cycles of: 95 °C for 1 s, 60 °C for 20 s (QuantStudio™ 7 Flex Real-Time PCR System, Thermo Fisher). Quantification cycles (Cq's) were determined via the threshold method (QuantStudio™ Real-Time PCR Software v1. 3, Thermo Fisher). Gene expression data were analyzed with the GenEx v7. 1 software (MultiD Analyses AB). Diptheria toxin, Philippe Soriano for providing the sequence of adenovirus splicing acceptor, and Guy Riddihough (Life Science Editors) for help with editing the manuscript. This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no.
The modeling is done, considering that the interjoists in the hollow body slab are filling elements and do not participate in the flexural behavior of the slab. The analytical simulation of the behavior of the rib in bending is examined by the method of the resistance of materials. The conduct of the numerical simulation was possible thanks to the use of a computer with "ABAQUS / CAE 6. 14-15" software. Numerical modeling is based on Finite Element analysis. It was an implicit calculation code whose calculation algorithm is based on iterative calculations. The mesh used is of the very tight type. The calculated rib is simply supported on its two ends and stressed in bending by a uniform load. -The angular deformations on this plate remain zero; -The deformation   is neglected; -The thickness of the table is small (t = 5 cm); consequently, the stresses in the thickness direction are assumed to be zero; -The stress state being plane, we have τyz = τxz = 0 The deformation of the slab is calculated by the expression: 𝑤(𝑥) = 𝑝𝑥 24𝐸𝐼 [𝑙 3 -2𝑙𝑥 2 -𝑥 3 ] (4) With,.
Search Strategy The systematic review and meta-analysis were performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and the Cochrane Handbook for Systematic Reviews of Interventions  [7, 8]. Study protocol was established in priori and was registered with PROSPERO (CRD42021226369). Two review authors (L. N. and W. K. ) independently conducted a systematic search through three electronic databases: Medline, Embase, and Cochrane Library. The last search was performed on 12 November 2020. The following terms and/or keywords were used: ("adjuvant chemotherapy" OR "AC") AND ("neoadjuvant chemotherapy" OR "NAC") AND ("bladder cancer" OR "muscle-invasive bladder cancer" OR "MIBC") AND ("residual disease" OR "locally advanced disease").
Data Extraction After removal of duplicates, two review authors (W. K. and L. N. ) independently screened the titles and abstracts of the retrieved records using a standardized item form. All potentially eligible studies were evaluated as full text if available. Any disagreements were subsequently resolved by consultation with the other authors. The following data were initially extracted: first author, year of publication, journal, study region, study design, study duration, number of patients in AC and surveillance group, and median follow-up. Further, the following clinicopathological data were retrieved: age, gender, Charlson comorbidity index (CCI), NAC regimen, number of NAC cycles, pathological stage, positive nodal status, surgical margin status, AC regimen, and number of AC cycles.
Subsequently, we extracted the outcome measurements of OS and DSS, including the hazard ratios (HRs) and 95% confidence intervals (CIs). OS was primarily defined as time from the date of RC to death from any case or to the time of the last known follow-up. DSS was primarily defined as the time from RC to death from cancer/MIBC or to the time of the last known follow-up. For articles that lacked some data, the corresponding authors were contacted, requesting additional information from their research, but no additional data were received.
Statistical Analysis Statistical analysis was conducted using Review Manager 5. 3 (The Nordic Cochrane Center, The Cochrane Collaboration, Copenhagen, Denmark) and the R platform (R project, Vienna, Austria). If available, the reported HRs and 95% CIs were included in the metaanalysis. We preferred to collect multivariate-analysis data; otherwise (if not reported), data from univariate analyses were extracted. The statistical significance of the pooled HRs was evaluated by the Z test. The heterogeneity I 2 index was calculated in order to indicate the proportion of inconsistency between studies that could not be attributed to chance. Significant heterogeneity was indicated by a ratio >50% for I 2 or a p value ≤0. 10 in the Cochrane Q test, in which case a random-effect model was used. When no significant heterogeneity was observed among the studies, a fixed-effect model was used.
Study Identification and Quality Assessment Our search strategy initially identified 2124 articles (2098 from online databases and 26 from additional sources). A flow diagram of the study selection and subsequent exclusions (with reasons) is presented in Figure  1. Eventually, we identified six studies for qualitative and quantitative analyses  [10] [11] [12] [13] [14] [15]. Table  1  summarizes the baseline characteristics of the eligible studies. Of the 3096 total participants in the selected articles, 2355 (76. 1%) and 741 (23. 9%) were in the surveillance and AC groups, respectively. All included studies had a retrospective design. The reported median follow-up ranged from 30 to 50 months. The assessment of the quality scores for the selected trials based on NOS ranged from 5 to 7, which was considered adequate for the subsequent systematic review and meta-analysis.
Meta-Analysis Results For each outcome of interest (OS and DSS), we performed a main analysis that included data from all available publications. Subsequently, we conducted subgroup analysis of OS in the pN+ subpopulation. Subgroup analysis of DSS in the pN+ subpopulation could not be reliably performed due to insufficient data. OS data were reported in five articles  [10] [11] [12] [13] [14]. No significant heterogeneity was observed among the studies (I 2 = 0%; p = 0. 61). Therefore, a fixed-effect model was used. A forest plot of HR and 95% CI for OS is presented in Figure  2A. The use of AC was associated with significantly better OS (HR 0. 84, 95% CI 0. 75-0. 94; p = 0. 002) in the pooled analysis. The funnel plot revealed no publication bias (Figure  3A ). Data for DSS were reported in two studies  [10, 15].
Currently, trials with checkpoint immunotherapy are specifically targeting patients at a high risk of recurrence, including patients with significant residual disease after prior NAC (pT2-T4 and/or pN+; NCT02632409, NCT02450331). Comparison of these two treatment modalities (AC vs. checkpoint inhibitors) in patients receiving NAC could identify the best form of treatment in terms of oncological efficacy. The important question remains as to whether survival benefits surpass the possible significant negative effect of AC after NAC on patient-reported outcomes. According to a study by Zhang et al. with chemotherapy-naïve patients, self-reported anxiety and depression were increased, while quality of life (QoL) was not decreased in MIBC patients during AC  [19]. Data regarding the toxicity and impact on patient-reported outcomes of AC after NAC and RC were not presented in any of the included studies.
Leaf disks were collected 24 h later, and ROS assays were performed as described for Arabidopsis. Root skewing. Seeds were sown directly on MS agar square plates and stratified for 2 days at 4 °C. Plates were transferred to 22 °C under a 16-h photoperiod, in an upright position for 9 days. The root angle was measured by the ImageJ software, as performed previously  9. Protein expression and purification. Codon-optimized synthetic genes for expression in Spodoptera frugiperda (Invitrogen GeneArt), coding for Arabidopsis thaliana MIK2 (residues 1-709) and BAK1 (1-220) ectodomains were cloned into a modified pFastBac (Geneva Biotech) vector, providing a TEV (tobacco etch virus protease) cleavable C-terminal StrepII-9xHis tag. For protein expression, Trichoplusia ni Tnao38 cells  49  were infected with MIK2 or BAK1 virus with a multiplicity of infection (MOI) of 3 and incubated for 1 d at 28 °C and 2 d at 22 °C at 110 rpm.
MIK2 and BAK1 ectodomains were gel filtrated into pH 5 ITC buffer (20 mM sodium citrate pH 5. 0, 150 mM NaCl). SCOOP12 peptide powder was dissolved in the same buffer to obtain the desired concentration. A typical experiment consisted of injecting 1 μL of a 200 μM solution of the ligand into 20 μM MIK2 solution in the cell at 150 s intervals. BAK1 vs MIK2-SCOOP12 experiments were performed by titrating 200 µM BAK1 onto the MIK2-SCOOP12 complex in the cell, using the same injection pattern. ITC data were corrected for the heat of dilution by subtracting the mixing enthalpies for titrant solution injections into protein-free ITC buffer. Experiments were done in triplicates and data were analyzed using the MicroCal PEAQ-ITC Analysis Software provided by the manufacturer. All ITC runs used for data analysis had an N ranging between 0.
8 and 1. 3. The N values were fitted to 1 in the analysis. Similarity/identity matrices. SIAS was used to generate similarity and identity matrices (http://imed. med. ucm. es/Tools/sias. html). Species divergence estimations. The timing of fungal species divergence was estimated using TimeTree  [50] [51] [52] [53]. Statistical analysis. Statistical analysis was performed in GraphPad Prism 7. 0. (GraphPad Software, http://www. graphpad. com) unless stated otherwise. Dot plots were used to show individual data points wherever possible. P-values < 0. 05 were considered nonsignificant. Sample sizes, statistical tests used, and P-values are stated in the figure legends. Open Access This article is licensed under Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Acknowledgements We thank  Justin Lee ,  Jean-Pierre Renou ,  Jen Sheen ,  Wei-Cai Yang , and  Yuelin Zhang  for providing published materials, the  John Innes Centre Horticultural Services  for plant care, and all past and current members of the Zipfel and Santiago groups for technical help and fruitful discussions. Nicholas Talbot  is acknowledged for hosting J. R. for part of this study. This work was supported by the  European Research Council  under the Grant Agreements no. 773153  and no. 716358  (grant " IMMUNO-PEPTALK " to C. Z. and grant "WallWatchers" to J. S. , respectively), The  Gatsby Charitable Foundation  (to C. Z. ), the  University of Zürich  (to C. Z. ), the  Swiss National Science Foundation  grants no. 31003A_182625  (to C. Z. ) and no. 31003A_173101  (to J. S. ), and the  Fondation Philanthropique Famille Sandoz  (to J.
Marine datasets used for PT Unfortunately, the  Sarnthein et al. (2020)  review paper fails to show a single figure illustrating oceanic 14 C records with their plateaus compared and tuned with the proposed atmospheric calibration 14 C curve. It is thus necessary to dig into the literature to see the marine core records: Figs. 3 and 4 in  Sarnthein et al. (2007)  for cores SO17940, MD01-2416, ODP893A, and PS2644; Fig. 2  in  Sarnthein et al. (2011)  for core  MD01-2378; Figs. 3 to 13 in Sarnthein et al. (2015)  for cores GIK23074, PS2644, MD08-3180, ODP1002, MD01-2378, GIK17940, SO50-37,  MD01-2416 , MD02-2489 , ODP893A, and MD02-2503; Fig. S1a, b, c, d in Balmer et al. (2016)  for cores GeoB1711-4, GeoB3910-1,  KNR-159-5-36GGC, and MD07-3076; Fig. 4 in Sarnthein and Werner (2017)  for cores GIK23258, MSM5/5-712, and T88-2; Fig.
While simple, this simulation study illustrates some of the potential difficulties of identifying 14 C age plateaus in the presence of observational noise and a lack of sampling density. Were the simulated data to have much higher precision and be sampled much more densely, we would expect the 14 C age plateaus to align more consistently. Such a preliminary study with an exact and reproducible approach is needed to assess the robustness of the method. The fundamental questions of how to remove the potentially confounding effects of MRA changes, the lack of a timescale for the marine records on which to calculate the 14 C age gradient combined with significant sedimentation rate changes, and other potential geoscientific factors in identifying 14 C age plateaus will however still remain.
More likely, it represents that we do not yet know when and with what magnitude any such 14 C variations occur. Construction of the IntCal20 curve uses Bayesian splines  (Heaton et al. , 2020b). The outputs of this Bayesian approach are a large set of posterior curve realizations that aim to find a trade-off between passing near the observed 14 C determinations on which the curve is based, while not being so variable as to overfit the data and introduce spurious features that are simply artifacts of the analytical sampling errors within the 14 C observations. The pointwise summary is then based upon averaging over 2500 of these curve realizations. The IntCal approach does, in fact, assume there are similar levels of short-term atmospheric variability from 55-13. 9 cal kyr as in the tree-ring-based section from 13.
Further, low-resolution and noisy 14 C datasets, particularly hard to avoid in marine sediment cores, severely limit the ability to reconstruct the fine-scale structures needed to tune to atmospheric 14 C signal. Consequently, the identification and tuning of hypothesized 14 C age plateaus between sparse marine sediment and atmospheric records may lead to significant misalignment. The resultant calendar chronology for the marine sediment and inferred MRA changes may therefore be equally incorrect. Techniques do exist to provide calendar chronologies for deep-sea cores independently from 14 C (and which can then be used to infer MRA changes). These utilize alternative age links between proxies in combination with age-depth modeling. One approach is to use climate proxies with the sediment cores, for example these have been tied to the oxygen isotopic (δ 18 O) profile of Hulu Cave stalagmites, which have been accurately dated by U-Th  (Bard et al.
(2020) and Bronk Ramsey et al. (2020) , its varve-counted timescale needs adjustment using the Hulu Cave 14 C record. To accurately iden-   (Heaton et al. , 2020b). Superimposed thick green lines indicate the 15 atmospheric plateaus with their numbering as listed in Table  1  of  Sarnthein et al. (2020). tify the higher-frequency components of 14 C variation from 55-14 cal kyr BP we must await recovery of new archives, notably floating tree-ring sequences. IntCal20, compared to previous versions of the 14 C calibration curve, profits from a multitude of improvements in all 14 C archives beyond 14 cal kyr. These include new highresolution 14 C and U-Th data from Hulu Cave  (Cheng et al. , 2018) , reanalysis of the Lake Suigetsu timeline  (Bronk Ramsey et al. , 2020) , updates in the calendar scale and MRA modeling of the Cariaco basin 14 C dataset  (Hughen and Heaton, 2020) , floating tree-ring 14 C sequences  (Adolphi et al.
Acknowledgements. We thank the editor,  André Paul , the referees,  Paula Reimer  and an anonymous referee, and the commenters,  Michael Sarnthein ,  Pieter Grootes ,  Frank Lamy ,  Helge Arz ,  Elisabeth Michel ,  Giuseppe Siani , and  Bernhard Weninger , for their comments which helped us in revising and strengthening our paper. Timothy J. Heaton  was funded by a  Leverhulme Trust   Fellowship   RF-2019-140/9. Edouard Bard  is supported by  EQUIPEX ASTER-CEREGE  and  ANR CARBOTRYDH. Financial support. This research has been supported by the  Leverhulme Trust  (grant no. RF-2019-140/9 ),  EQUIPEX ASTER-CEREGE  and  ANR CARBOTRYDH. Review statement. This paper was edited by  André Paul  and reviewed by  Paula Reimer  and one anonymous referee.
Statistical Analysis Statistical analysis was performed with SPSS Statistics version 21 (IBM Corp, Armonk, NY, USA). One-way ANOVA was carried out followed by Tukey as a post-hoc test to determine statistically significant differences between fatty acid and isotopic means for different provinces (p < 0. 05). Furthermore, linear discriminant analysis (LDA) was applied for building predictive models by the combination of isotopic and fatty acid composition that maximize the discrimination of the predefined regions. The difference between means was normalized by a measure of the within-class variability. In addition, to determine the chemical compounds responsible for determining the geographical origin according to discriminant functions, the statistical significance of each discriminant function was evaluated by Wilk's lambda.
Acknowledgments: The authors are very grateful to the  GIZ and ANDZOA  for their financial support and assistance in the choice of sampling sites. Thanks are due to  Women's Cooperatives Tighanimine Filahia (Agadir) , for Argan fruit processing. The authors are grateful to the staff of the  Agro-Food Technology and Quality Laboratory (INRA-Marrakech) , and staff of the  Structural and Isotopic Analysis Laboratory (CNESTEN-Rabat ). The authors would like to express their gratitude to the editors, reviewers and the editorial office.
ACKNOWLEDGMENTS We thank all the patients involved in the study as well as their caregivers, care team, investigators, and research staff at participating institutions. We also thank  Maria Dzialo  and  Sandy Adjemian  of  Data Investigation Company Europe (Brussels, Belgium ) for providing medical writing support and editorial support, which was sponsored by  Ipsen (Merelbeke, Belgium)  in accordance with Good Publication Practice guidelines. V. De Ruyter  and  K. Geboes  designed the study,  L. Duck ,  G. Demolin ,  L. D'Hondt ,  C. Dopchie ,  K. Hendrickx ,  B. Lannove ,  F. Bastin ,  D. Lossignol ,  O. Hamdan ,  W. Lybaert , and  V. Vandenhaute  contributed to data acquisition. B. Regnault contributed to statistical analysis. V. De Ruyter  and  K. Geboes  contributed to data interpretation.
INTRODUCTION The segmentation of 3D point cloud into different classes is an important task to support various workflows, for example the generation of 3D GIS and BIM  (Bassier et al. , 2017). This has been done using various approaches which can generally be divided into algorithmic or machine learning-based methods  (Murtiyoso et al. , 2020). Algorithmic approaches have shown to generate good results  (Maalek et al. , 2019)  while machinelearning (ML) based classification methods have also showed promising results  (Malinverni et al. , 2019). ML techniques are more robust against noise and occlusions, but require training data which may not always be available. In the field of machine learning, deep learning (DL) as a subset has gained much attention in recent years aided by the recent developments in computing power and availability of large training datasets.
A previous research attempted to use fuzzy logic in performing image classification  (Neusch and Grussenmeyer, 2003). With a similar purpose,  Grilli et al. (2018)  performed supervised segmentation on orthophotos and UV textures of photogrammetric 3D models, but using an MLbased approach. In this paper, we present a DL-based transfer learning approach for performing a similar task. The developed approach deploys a pre-trained ResNet-18 network  (He et al. , 2016)  to create a DeepLabv3+ network  (Chen et al. , 2018)  by training it on a dataset of several hundreds of labelled rectified images of building façades. The trained network was then applied to semantically segment an orthoimage (thus 2. 5D) of a case-study building. From this image, a function was employed to back-project the pixel coordinates into the 3D point cloud, thus extracting the point cloud of each class.
RELATED WORK Semantic analysis firstly of 2D and more recently of 3D scenes has become an increasingly studied topic in various applications, such as photogrammetry, remote sensing, computer vision and robotics  (Heipke and Rottensteiner, 2020). Input data might be images, point clouds or textured meshes. Several approaches have been developed to automate this task with promising results, such as algorithmic  (Maalek et al. , 2019)  and machine learning  (Matrone et al. , 2020)  approaches. At the same time, deep learning and neural networks are becoming more popular due to increasing computational capacity and growing number of available databases. Neural networks are generally used for several tasks: classification, semantic segmentation, instance segmentation and more recently, panoptic segmentation.