dataesr/scientific-paragraphs-categorization huggingface dataset:
(D) The TaqMan assay was also performed as previously described in primary HKs previously retrovirally transduced with the hTERT gene and expressing E6 and/or E7 from HPV38 (n = 3). Results were normalized to GAPDH. ���� , p<0. 0001. (E) Proteins extracts from hTERT pLXSN or hTERT HPV38 E6/E7 cells were analyzed by IB with the indicated antibodies. Images shown are representative examples of 2 different experiments. (F) hTERT pLXSN or hTERT HPV38 E6/E7 cells were plated on coverslips and after 24 h were probed for ITGA1 using anti-ITGA1 antibody followed by secondary Alexa Fluor 488-conjugated antibody. Nuclei were stained with DAPI (pseudocoloured red), and cells were analyzed under a microscope. Images were merged using ImageJ software. https://doi. org/10. 1371/journal. ppat. 1008792. g001.
Error bars represent standard deviations of 3 independent experiments performed in triplicate. �� , p<0. 01. (C) HKs or 38HK were cross-linked and chromatin was processed for ChIP using p53 or IgG antibodies. Results were analyzed by qPCR using primers spanning for p53 REs of the ITGA1 promoter and normalized to IgG enrichment (negative control). Error bars represent the standard deviation of 2 independent experiments performed in 2 different HKs donors. � , p<0. 05, ns, not significant. To corroborate these data that indicate cross-talk between p53 and PKR in 38HK, we performed reciprocal IP using PKR or p53 antibodies to assess the possible interaction between the two cellular proteins. The PKR/p53 complex was immunoprecipitated by both antibodies (Fig  5A ). Importantly, the S392 p53 form was found to be associated with PKR (Fig  5B ).
Inhibition of ITGA1 expression results in activation of EGFR signaling and cellular proliferation. Paradoxically, ectopic levels of WT p53 in 38HK further repressed ITGA1 expression and increased cellular proliferation. Thus, in HPV38-transformed HKs, WT p53 acquires pro-proliferation properties. This conclusion is further corroborated by the fact that treated with PKR inhibitor, 2AP, or PBS:glacial acetic acid (200:1) as a mock-treated control (-) were obtained for PKR IP as previously described (top). The input was run in a different gel to improve image quality. (C) 38HK were cross-linked and chromatin was extracted for a ChIP-reChIP assay by p53 IP followed by p446PKR IP. Data from 3 independent experiments performed in duplicate were analyzed by qPCR. p53RE2 and the intergenic region of chromosome 22 (nc) primers were used.
Fluorescence microscopy hTERT pLXSN or hTERT HPV38 E6/E7 cells were plated onto coverslips and cultured for 24 h. Thereafter, the cells were fixed with 4% formaldehyde for 30 min at room temperature and permeabilized with 0. 1% Triton X. Cells were incubated with blocking solution for 1 h, washed with PBS, and incubated with ITGA1 (ab243033; Abcam) antibody overnight at 4˚C. The cells were then incubated with Alexa Fluor 488-conjugated secondary antibody for 2 h at room temperature and mounted on slides using Vectashield Antifade Mounting Medium with DAPI. The slides were visualized using a Nikon Eclipse Ti wide-field inverted fluorescence video microscope. The images thus captured were analyzed by NIS-Element software from Nikon and processed using ImageJ software.
Methods We collected papers from 2019 by a standard search in PubMed and Web of Science ®. Searches were performed in October and December 2019 to identify peer-reviewed journal articles published in 2019, in the English language, related to HFOI research in biomedical informatics. In addition to the search of electronic databases, manual searches of key themes were performed in major biomedical journals (e. g. , Journal of the American Medical Informatics Association, Methods of Information in Medicine, Journal of Medical Internet Research, etc. ). We used free-text and coded keywords. Free-text keywords were listed in with respect to the questions addressed by the section. Relevant MeSH terms were also identified, and PubMed test queries were made iteratively. Two final queries were built, one based on MeSH terms for major topics and one on free-text keywords searched in titles or abstracts via PubMed and Web of science ®.
They used log data collected during treatment delivery, integrating text and visual analytics in a novel approach aiding analysis of a rich complex dataset. They found four clusters of different types of users based on app usage patterns, and compared them in terms of engagement and associated factors, highlighting mechanisms of change and usability perceptions. The paper has both practical and scientific value: it contributes to richer methods for understanding user engagement more holistically and it contains a detailed description of a log analysis work which may be helpful. Giordanengo et al. ,  [9]  evaluated the design of a diabetes dashboard displaying patient-collected data to clinicians, using iterative participatory design with 14 clinicians. The study permits the assessment of the reliability of self-collected data, calculations, and highlighted conditions requiring investigation.
The study found that nurses are still often not included in selection, modification, and other aspects of EHR deployment, with high rates of dissatisfaction, and that advanced functionality of EHRs is needed to achieve desired effects, with satisfaction increasing with the EHR level of sophistication. Relying on the two conceptual frameworks and the National Coordinating Council for Medication Errors Reporting and Prevention (NCC MERP) taxonomy, a retrospective analysis of 9-year safety reports from the US FDA (Food and Drug Administration) MAUDE (Manufacturer and User Facility Device Experience) database (45,624 MAUDE reports were extracted; 3,521 reports were identified as HIT-related reports) was conducted. A hundred and fifty-two unique reports met the inclusion criteria. Results show a variety of contributing factors to risks which are mostly associated with clinical content followed by human-computer interface, and people.
While Organizational and system-related contributing factors are critical underlying factors, they were not directly highlighted. Results also provide an overview of how likely certain effect may be brought on one step and how it affects the other adjacent steps (e. g. , receiving wrong information causes making a wrong decision with a probability of 0. 62, and the wrong informa-tion may lead to a wrong (probability: 0. 38) or delayed (probability: 0. 56) care process, with a high chance of resulting in patient harm (probability: 0. 94)). The application of the two conceptual frameworks to the MAUDE database may be a general approach to analyze any event reports in the context of HIT. It may enhance the understanding of HIT-induced problems, along with their causes and the identification of the most significant risks to patient safety.
An evaluation was conducted as a web-based user study, in which 18 clinicians analyzed two simulated patient scenarios using a randomly assigned workflow. Data analysis compared the two workflows with respect to accuracy and efficiency in diagnostic interpretation, efficacy in collecting detailed phenotypic information, and user satisfaction. Results demonstrated that gene interpretation could be accelerated using the prototype-based workflow by facilitating prototypical thinking. It was likely that participants employed some level of proto-typical thinking in both workflows, while the reasoning process was more efficiently facilitated by the prototype-based workflow. Patterson ES, Su G, Sarkar U Reducing delays to diagnosis in ambulatory care settings: A macrocognition perspective Appl Ergon 2020 Jan;82:102965 In this paper, the authors want to characterize contributors to diagnostic delays by physicians that can be mitigated by work system redesign.
Methods Patients and dataset constitution. This study was approved by the Institutional Review Board at Rothschild foundation and followed the tenets of the Declaration of Helsinki. Informed consent was obtained from all participants. A total of 2890 electronic medical records of patients (6397 eyes) evaluated for refractive surgery at the Laser Vision Institute Noémie de Rothschild (Foundation Adolphe de Rothschild Hospital, Paris) were retrieved and consenting patients data was analyzed. We excluded patients with strabismus and any other ocular abnormalities except ametropia. After data cleaning, eyes with subjective refraction and a valid wavefront aberrometry examination were randomly split into a 350 eyes test set and a training set, with no cross over of.
Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models which encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.
Introduction Graph-to-text generation refers to the task of generating natural language text from input graph structures, which can be semantic representations  (Konstas et al. , 2017)  or knowledge graphs (KG)  (Gardent et al. , 2017; Koncel-Kedziorski et al. , 2019). While most recent work  (Song et al. , 2018; Ribeiro et al. , 2019; Guo et al. , 2019)  focuses on generating sentences, a more challenging and interesting scenario emerges when the goal is to generate multi-sentence texts. In this context, in addition to sentence generation, document planning needs to be handled: the input needs to be mapped into several sentences; sentences need to be ordered and connected using appropriate discourse markers; and inter-sentential anaphora and 1 Code is available at https://github. com/UKPLab/kg2text ellipsis may need to be generated to avoid repetition.
KG-to-Text Generation. In this work, we focus on generating text from KGs. In comparison to AMRs, which are rooted and connected graphs, KGs do not have a defined topology, which may vary widely among different datasets, making the generation process more demanding. KGs are sparse structures that potentially contain a large number of relations. Moreover, we are typically interested in generating multi-sentence texts from KGs, which involves solving document planning issues  (Konstas and Lapata, 2013). Recent neural approaches for KG-to-text generation simply linearise the KG triples thereby loosing graph structure information. For instance,  Colin and Gardent (2018) ,  Moryossef et al. (2019)  and Adapt  (Gardent et al. , 2017)  employ LSTM/GRU to encode WebNLG graphs. Castro Ferreira et al.
Data and Preprocessing We attest the effectiveness of our models on two datasets: AGENDA  (Koncel-Kedziorski et al. , 2019)  and WebNLG  (Gardent et al. , 2017). Table  1  shows the statistics for both datasets. AGENDA. In this dataset, KGs are paired with scientific abstracts extracted from proceedings of 12 top AI conferences. Each instance consists of the paper title, a KG and the paper abstract. Entities correspond to scientific terms which are often multi-word expressions (co-referential entities are merged). We treat each token in the title as a node, creating a unique graph with title and KG tokens as nodes. As shown in Table  1 , the average output length is considerably large, as the target outputs are multi-sentence abstracts. WebNLG. In this dataset, each instance contains a KG extracted from DBPedia.
The target text consists of sentences that verbalise the graph. We evaluate the models on the test set with seen categories. Note that this dataset has a considerable number of edge relations (see Table  1 ). In order to avoid parameter explosion, we use regularization based on the basis function decomposition to define the model relation weights  (Schlichtkrull et al. , 2018). Also, as an alternative, we employ the Levi Transformation to create nodes from relational edges between entities  (Beck et al. , 2018).
, 2002) , METEOR  (Denkowski and Lavie, 2014)  and CHRF++  (Popović, 2015)  automatic metrics and also perform a human evaluation (Section 5. 6). For layer-wise models, the number of encoder layers are chosen from {2, 4, 6}, and for PGE and CGE, the global and local layers are chosen from and {2, 4, 6} and {1, 2, 3}, respectively. The hidden encoder dimensions are chosen from {256, 384, 448} (see Figure  3 ). Hyperparameters are tuned on the development set of both datasets. We report the test results when the BLEU score on dev set is optimal.
Results on AGENDA Table  2  shows the results, where we report the number of layers and attention heads employed. We train models with only global or local encoders as baselines. Each model has the respective parameter size that gives the best results on the dev set. First, the local encoder, which requires fewer encoder layers and parameters, has a better performance compared to the global encoder. This shows that explicitly encoding the graph structure is important to improve the node representations. Second, our approaches substantially outperform both baselines. CGE-LW outperforms Koncel-Kedziorski et al. (  2019 ), a transformer model that focuses on the relations between adjacent nodes, by a large margin, achieving the new state-of-the-art BLEU score of 18. 01, 25. 9% higher. We also note that KGs are highly incomplete in this dataset, with an average number of connected components of 19.
Results on WebNLG We compare the performance of our more effective models (CGE, CGE-LW) with six state-of-theart results reported on this dataset. Three systems are the best competitors in the WebNLG challenge for seen categories: UPF-FORGe, Melbourne and Adapt. UPF-FORGe follows a rule-based approach, whereas the others use neural encoderdecoder models with linearized triple sets as input. Table  3  presents the results. CGE achieves a BLEU score of 62. 30, 8. 9% better than the best model of Castro  Ferreira et al. (2019) , who employ an end-to-end architecture based on GRUs. CGE using Levi graphs outperforms Trisedya et al. (  2018 ), an approach that encodes both intratriple and inter-triple relationships, by 4. 5 BLEU points. Interestingly, their intra-triple and intertriple mechanisms are closely related with the local and global encodings.
Ablation Study In Table  4 , we report an ablation study on the impact of each module used in CGE model on the dev set of AGENDA. We also report the number of parameters used in each configuration. Global Graph Encoder. We start by an ablation on the global encoder. After removing the global attention coefficients, the performance of the model drops by 1. 79 BLEU and 1. 97 CHRF++ scores. Results also show that using FFN in the global COMBINE(. ) function is important to the model but less effective than the global attention. However, when we remove FNN, the number of parameters drops considerably (around 18%) from 61. 5 to 50. 4 million. Finally, without the entire global encoder, the result drops substantially by 2. 21 BLEU points. This indicates that enriching node embeddings with a global context allows learning more expressive graph representations.
Impact of the Graph Structure and Output Length The overall performance on both datasets suggests the strength of combining global and local node representations. However, we are also interested in estimating the models' performance concerning different data properties. Graph Size. Figure  4a  shows the effect of the graph size, measured in number of nodes, on the performance, measured using CHRF++ scores, 6  for the AGENDA. We evaluate global and local graph encoders, PGE-LW and CGE-LW. We find that the score increases as the graph size increases. Interesting, the gap between the local and global encoders increases when the graph size increases. between nodes based on the graph structure is more beneficial than allowing direct communication between nodes, overlooking the graph structure. Also note that the the cascaded model (CGE-LW) is consistently better than the parallel model (PGE-LW) over all graph sizes.
Similarly to the graph size, the score increases as the diameter increases. As the global encoder is not aware of the graph structure, this module has the worst scores, even though it en- ables direct node communication over long distance. In contrast, the local encoder can propagate precise node information throughout the graph structure for k-hop distances, making the relative performance better. Table  5  shows the models' performances with respect to the graph diameter for WebNLG. Similarly to the graph size, the score decreases as the diameter increases. Output Length. One interesting phenomenon to analyze is the length distribution (in number of words) of the generated outputs. We expect that our models generate texts with similar output lengths as the reference texts. As shown in Fig-  ure 4c , the references usually are bigger than the texts generated by all models for AGENDA.
Human Evaluation To further assess the quality of the generated text, we conduct a human evaluation on the WebNLG dataset. 9 Following previous works  (Gardent et al. , 2017; Castro Ferreira et al. , 2019) , we assess two quality criteria: (i) Fluency (i. e. , does the text flow in a natural, easy to read manner?) and (ii) Adequacy (i. e. , does the text clearly express the data?). We divide the datapoints into seven different sets by the number of triples. For each set, we randomly select 20 texts generated by Adapt, CGE with Levi graphs and their corresponding human reference (420 texts in total). Since the number of datapoints for each set is not balanced (see the different triple sets. Moreover, having human references may serve as an indicator of the sanity of the human evaluation experiment.
Additional Experiments Impact of the Vocabulary Sharing and Length Penalty. During the ablation studies, we note that the vocabulary sharing and length penalty are beneficial for the performance. To better estimate their impact, we evaluate CGE-LW model with its variations without employing vocabulary sharing, length penalty and without both mechanisms, on the test set of both datasets. Table  6  shows the results. We observe that sharing vocabulary is more important to WebNLG than AGENDA. This suggests that sharing vocabulary is beneficial when the training data is small, as in WebNLG. On the other hand, length penalty is more effective for AGENDA, as it has longer texts than WebNLG 10  , improving the BLEU score by 0. 71 points. Attention Distance Figure  7 : The average distance between nodes for the maximum attention for each head.
Case Study. Figure  6  shows examples of generated texts when the WebNLG graph is complex (7 triples). While CGE generates a factually correct text (it correctly verbalises all triples), the Adapt's output is repetitive. The example also illustrates how the text generated by CGE closely follows the graph structure whereby the first sentence verbalises the right-most subgraph, the sec- 11 The distance between two nodes is defined as the number of edges in a shortest path connecting them. ond the left-most one and the linking node Turkey makes the transition (using hyperonymy and a definite description, i. e. , The country). The text created by CGE is also more coherent than the reference. As noted above, the input graph includes two subgraphs linked by Turkey. In natural language, such a meaning representation corresponds to a topic shift with the first part of the text describing an entity from one subgraph, the second part an entity from the other subgraph, and the linking entity (Turkey) marking the topic shift.
Conclusion In this work, we introduced a unified graph attention network structure for investigating graph-totext models that combine global and local graph encoders in order to improve text generation. An extensive evaluation of our models demonstrated that the global and local contexts are empirically complementary, and a combination can achieve state-of-the-art results on two datasets. In addition, cascaded architectures give better results compared to parallel ones. We point out some directions for future work. First, it is interesting to study different fusion strategies to assemble the global and local encodings; Second, a promising direction is incorporating pre-trained contextualized word embeddings in graphs; Third, as discussed in Section 5. 5, it is worth studying ways to diminish the gap between the reference and the generated text lengths.
Acknowledgments We would like to thank  Pedro Savarese ,  Markus Zopf ,  Mohsen Mesgar ,  Prasetya Ajie Utama ,  Ji-Ung Lee  and  Kevin Stowe  for their feedback on this work, as well as the anonymous reviewers for detailed comments that improved this paper. This work has been supported by the  German Research Foundation  as part of the  Research Training Group Adaptive Preparation of Information from Heterogeneous Sources (AIPHES)  under grant No. GRK 1994/1.
A. Datasets We test our algorithm on two synthetic datasets. The first dataset corresponds to the Holliday junction complex (HJC) density map, while the second corresponds to the Human patched 1 (PTCH1) protein. For each dataset, we generate the synthetic ground truth V from the submitted density map  [40] ,  [41] , along with the associated atomic model in the Protein Data Bank using Chimera  [42]. The sizes of the HJC and PTCH1 volumes used in our simulations are (90 × 90 × 90) and (84 × 84 × 84), with voxel sizes of 2. 867 Å and 1. 8 Å, respectively. We also synthesize a higher resolution version of HJC with size 124 × 124 × 124 and voxel size 2 Å. The first two volumes are used in our proof of concept simulations; the last volume is used in an experiment that mimics more realistic cryo-EM conditions (section V.
E. Compared Methods We compare our joint-optimization method to the following approaches: 1) Reconstruction with Unrefined Orientations. We do not refine the initial angles and directly reconstruct the density map. This gives us an indication of the quality of reconstruction prior to the refinement procedure. 2) Reconstruction with True Orientations. We reconstruct the density map with the ground-truth orientations and in-plane translations. This serves as an oracle benchmark that allows us to quantify the improvement brought by our refinement procedure. 3) Reconstruction with the Relion package  [1]. We run the 3D auto-refine function in Relion (version 2. 1. 0). The default parameters of this function (e. g. , Initial angular sampling and Local searches from auto sampling) are used. For the particular experiments in which the inplane translations are zero, the Initial offset range and Initial offset step parameters are set to their minimum values, which are 0 and 0.
E. Simulation of a Real Scenario We then mimic a real scenario in which the output of the 3D ab initio method provided by Relion is used to initialize both the density map and the projection orientations. The in-plane translations are initialized with zeros. We use the same HJC structure to synthesize a volume with size 124 × 124 × 124 and with a voxel size of 2 Å. The number of projection images is 20, 000 and the average SNR of the projection images is -14. 2dB. Examples of projection images are provided in Fig. 9  (left most column). We split the projection dataset in two halves and refine each half separately, starting from the same initial volume. Indepen- dent refinement of the two halves is a common approach in practice and has two main goals. First, by comparing the two refined half maps against one another, a convergence criterion is obtained.